Does globalization escalate the carbon emissions? Empirical evidence from selected next-11 countries

Adverse consequences are observed in developing countries due to the impact of the globalization process. Therefore, our study aims to empirically verify whether globalization escalates carbon dioxide emissions in selected N-11 (next-11) countries between 1990 and 2019. The study also analyzes how per capita GDP, per capita GDP², population growth, and renewable energy consumption affect carbon emissions. For this reason, the researchers used several econometric methods, including the slope homogeneity test, the cross-sectional dependency test, the panel unit root test, the panel cointegration test, the method of moment's panel quantile regression analysis, and the Wald test. The estimated results of panel quantile regression show how carbon emissions change across a range of quantiles (0.1 to 0.9). The findings show that per capita GDP significantly impacts the overabundance of carbon emissions in N-11 countries. Over time, the study found that the positive coefficient value of per capita GDP decreased from the first to the last (7.41 to 5.87), leading to the validation of the EKC hypothesis. The adverse correlation between per capita GDP² and environmental contamination confirms that the Environmental Kuznets Curve hypothesis is valid for selected N-11 countries. Globalization deteriorates the environment by directly affecting CO₂ emissions. It increases monotonically from the lower quantile to the upper quantile (0.972 to 1.002). At the quantile level of 0.1 to 0.9, population growth and renewable energy consumption increase impede carbon dioxide emissions in these selected countries. Coefficient values in the quantile 0.1 to 0.9 (-0.35 to -0.53) suggest that governments can reduce carbon emissions more due to renewable energy consumption over time. But the negative coefficient values of the population (-0.97, -0.93, -0.90, -0.88, -0.86, -0.85, -0.83, -0.81, and -0.77) decrease from the lower quantile to the upper quantile. The Wald test supports the asymmetric effects of different quantiles. As a robustness check of estimators, the study used FMOLS, DOLS, and CCR, which show the variables’ long-run elasticity. The research developed targeted policy recommendations for sustainably mitigating carbon emissions based on the above results.