Economic performance and carbon intensity of human well-being: empirical evidence from the MENA region

Abstract

Our paper explores the effect of economic performance variables on the carbon intensity of human well-being (CIWB) for 13 countries in the Middle East and North Africa (MENA) region over the period (1995–2013). We use a time-series cross-sectional Prais–Winsten regression model with panel-corrected standard errors (PCSEs). We find that economic performance has a statistically significant positive influence on CIWB over the period in question; thus, economic performance harms the environment, but the final effect deviates to a constant level after a while. This finding is not encouraging from the economic sustainability point of view. On the contrary, we find that total health expenditure has a statistically significant negative impact on CIWB by increasing life expectancy, which means less stress on the environment.

Keywords:

• sustainable economics
• economic performance
• carbon intensity of human well-being
• panel analysis
• MENA region

Acknowledgments

The author would like to thank the editor and three anonymous referees of the Journal of Environmental Planning and Management for their valuable and helpful comments. We are responsible for any errors.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. Pezzey and Toman (2002) have argued that sustainable economics is an old field of economics.

2. Van den Bergh (2010) and Bartelmus (2010) have criticized this view. Their critique states that this definition is overly general since it incorporates several different specific understandings of sustainability economics.

3. For more details, see Knight and Rosa (2011).

4. Most of the previous studies which examined the EKC hypothesis used the CO₂ emissions as the dependent variable (i.e., Narayan and Narayan 2010; Arouri et al. 2012). However, a new generation of studies, i.e. Dietz, Rosa, and York (2012), used ecological intensity of well-being as the dependent variable.

5. We extracted these factors from the literature. For more details, see Jorgenson, Alekseyko, and Giedraitis (2014), Jorgenson (2014), and Jorgenson and Givens (2015).

6. For more details about consumption-based accounting for emissions, see Boitier (2012).

7. For a review of these attempts, see Knight and Rosa (2011).

8. For more details, see Jorgenson and Givens (2015), Rosa and Dietz (2012) and Dietz, Rosa, and York (2012).

9. We adopt the definition of Sweidan and Alwaked (2016), Jorgenson and Givens (2015), and Jorgenson, Alekseyko, and Giedraitis (2014).

10. Either as individual countries or as a group with other countries.

11. For a comprehensive survey about this area of research, see Payne (2010).

12. For more details, see Jorgenson, Alekseyko, and Giedraitis (2014), Jorgenson (2014), and Jorgenson and Givens (2015).

13. http://www.carboun.com/energy/two-trends-of-energy-and-emissions-in-the-arab-world/.

14. For more details, see Fattouh and El-Katiri (2013) and IMF (2014).

15. Manufacturing refers to industries belonging to ISIC divisions 15–37, i.e., manufacture of machinery, equipment, motor vehicles, computers, electricity and water supply.

16. Positive effects mean more stress on the environment.

17. This is equal to the standard deviation divided by the mean.

18. The coefficient of variation is equal to the standard deviation, S, divided by the mean M. Using subscripts CO₂PC and LE to indicate energy consumption and life expectancy, respectively, the correction factor is .

19. For more details, see Lumley et al. (2002)

20. This technique is introduced by Parks (1967).

21. For more details, see Jorgenson, Alekseyko, and Giedraitis (2014), Allison (2009), Baum (2006), and Wooldridge (2005).

22. We use the same specific form to explore the yearly effect of M_it, E_it and H_it on CIWB_it; the only difference is to replace each variable with Y_it. Thus, we ended up with a total of four formulae in this specific form. For more details, see Appendix A.

23. These are available upon request.

24. Our results report high R²; other works used the same methodology and reported similar results (for example, see Sweidan and Alwaked 2016; Jorgenson and Clark 2012; Jorgenson, Alekseyko, and Giedraitis 2014).

25. The country-specific and year-specific intercepts.

26. The standard errors are larger, on average, in the models with AR(1) than that for the models without AR(1) by around 20%–35%.

27. The value of the Chi²(18) for Tables 3–6 are 14,216.53, 110.82, 54.79, and 375.95, respectively, and Prob > Chi² = 0.00 for all of them.

28. This approach was initially used by urban political economy scholars. For more details, see Timberlake and Kentor (1983), Walton and Ragin (1990), Shandra, London, and Williamson (2003), Knight and Rosa (2011). Recently, Jorgenson and Givens (2015) used this approach.

29. These are available upon request.