Estimating country-specific environmental Kuznets curves from panel data: a Bayesian shrinkage approach

Abstract

Designing an efficient global climate policy turns out to be a difficult yet crucial task since there are noteworthy cross-country differences in energy and carbon intensities. In this article, the environmental Kuznets curve (EKC) hypothesis is tested for carbon dioxide (CO₂) emissions, and as a modelling technique, the iterative Bayesian shrinkage procedure is employed to handle the cross-country differences. The results suggest that first the EKC hypothesis is rejected for 47 out of the 51 countries considered when the heterogeneity in countries’ energy efficiencies and cross-country differences in the CO₂ emissions trajectories are accounted for; second, a classification of the results with respect to the development levels of the countries concerned reveals that the emergence of an overall inverted U-shaped curve is due to the fact that in high-income countries increase in gross domestic product (GDP) decreases emissions, while in low-income countries emissions and GDP are positively correlated.

Keywords:

• environmental Kuznets curve
• Bayesian shrinkage estimator
• heterogeneity

JEL Classification:

• O13
• O44
• Q56

Acknowledgement

The authors would like to thank two anonymous referees for their comments.

Notes

¹ The emphasis in this review  is on understanding how the EKC literature triggered a chain of analysis during the last two decades. A number of excellent surveys have appeared on the subject in the last years and they provided useful overviews of the wide range of methods and modelling techniques used in this field. For example, Stern (2004) and Kijima et al. (2010) should be considered for a more complete review.

² To keep the review brief, we do not discuss in detail the reasons why these variables are chosen to be controlled for in the EKC analysis. The reader is referred to the studies cited here for further discussion.

³ BP (2010) uses standard global average conversion factors to estimate carbon emissions. The International Energy Agency (IEA) provides also data for CO₂ emissions from fuel combustion, which are calculated using the intergovernmental panel on climate change (IPCC) method. Consequently, these two data sets have very similar trends and magnitudes; therefore, working with either BP or IEA data set does not have a significant impact on the estimation results of this study.

⁴ Note that while using the Empirical Iterative Bayes’ estimator approach, for each explanatory variable, we will estimate as many parameters as there are countries. Consequently, the introduction of another explanatory variable would require more parameters to be estimated (i.e. 51 parameters in the case of 51-country sample). Although such a model would decrease the well-known omitted variable bias, in order to preserve the efficiency of the estimation procedure (in terms of degrees of freedom), we consider per capita energy consumption as the only additional explanatory variable, which is evidently one of the most relevant determinants of CO₂ emissions.

⁵ In our study, the individual dimension (N = 51) is more important than the time dimension (T = 39).

⁶ For the purpose of brevity, these estimation results are not reported in this article, but they are available upon request from the authors.

⁷ At this point we note that this method works well for all countries but one, Egypt, for which the TP is found to be negative. Since such a result is inconsistent with the nature of the relationship, Egypt is excluded from the later analysis.

⁸ We employ the terminology used in the literature on technical change (for instance, see Gerlagh, 2008): by the term ‘carbon-saving growth’ (‘carbon-intensive growth’) it is meant that increases in GDP are associated with decreases (increases) in CO₂ emissions; the term ‘carbon-neutral growth’ is used to indicate that GDP growth has not a significant impact on the carbon intensity of the economy.

⁹ The reports of the International Energy Agency constitute a very useful source of information about energy indicators and emission trends. For detailed statistics and further analysis, see IEA (2011a, b, c).