An Analysis of the Environmental Kuznets Curve for Carbon Dioxide Emissions: Evidence for OECD and Non-OECD Countries

Abstract

Over the past two decades, researchers have sought to establish empirical evidence for an Environmental Kuznets Curve (EKC) for carbon dioxide (CO2), with varied results. This study builds on that research to re-evaluate whether the EKC exists for CO2 emissions, using an improved dataset and the enhanced econometric technique Arellano-Bover/Blundell-Bond Generalized Methods of Moments (GMM) estimator. We directly compare OECD countries with countries of the non-OECD regions of Latin America, Asia, and Africa to determine how various factors like economic growth, population, trade, urbanization, and energy use influence CO2 emissions. We find that the OECD countries have an N-shaped curve with income growth whereas the regions of Asia and Africa experience an income-based EKC pattern. The results further reveal that population growth has a mixed impact on CO2emissions,increased trade and urbanization contribute to CO2 emissions for most areas, and increased energy use actually helps to decrease CO2 emissions.

Incorporating Forests, Agriculture, and Energy Consumption in the Framework of the Environmental Kuznets Curve: A Dynamic Panel Data Approach

“…However, in other geographic regions, results do not confirm an EKC relationship, as the estimated coefficients of GDP and GDP squared are statistically insignificant. Even though coefficient estimates associated with GDP are statistically significant for Asian countries, the signs do not meet a priori expectation, suggesting an N-shaped pattern [33]. Past literature also revealed mixed findings on the EKC relationship between CO 2 emissions and economic growth.…”

“…It also indicates a persistent trend in CO 2 emissions, suggesting that emissions in the past year have a positive impact on the level of current emissions [33]. Similarly, the Arellano-Bond test for zero autocorrelation in first differenced errors reveals that AR(1) is significant and AR(2) cannot reject the null hypothesis, suggesting that error terms are serially uncorrelated in all models [33,37]. Because Brazil, Russia, India, China, and South Africa (BRICS) are among the countries emitting the most carbon in the world (China, rank 1; India, rank 3; Russia, rank 4; Brazil, rank 13; and South Africa, rank 14) [40], we have also presented a separate model examining the EKC relationship for BRICS countries.…”

“…An important oversight in existing literature is that past studies overlooked the endogeneity problem between environmental degradation and income variables, which creates simultaneity bias when estimating empirical models of EKC. There is a possibility of a two-way causality between CO 2 emissions and economic growth of a country [17,33,34]. An increase in economic activities triggered by higher GDP could lead to an increase in environmental degradation, such as pollution and CO 2 emissions.…”

“…As an alternative to the external instrumental variables approach, some previous studies used a dynamic panel data approach to address the issue of endogeneity in empirical EKC models [33,35]. The dynamic panel data approach was first developed by [36], in which the auto-regressive specification is considered in a panel data setting and the model is estimated by the generalized method of moments (GMM) technique.…”

“…The coefficients on lagged values of per capita CO 2 emissions are found to be positive and statistically significant, validating the use of a dynamic panel model to estimate our empirical model. It also indicates a persistent trend in CO 2 emissions, suggesting that emissions in the past year have a positive impact on the level of current emissions [33]. Similarly, the Arellano-Bond test for zero autocorrelation in first differenced errors reveals that AR(1) is significant and AR(2) cannot reject the null hypothesis, suggesting that error terms are serially uncorrelated in all models [33,37].…”