The impact of democracy and income on CO 2 emissions in MINT countries: evidence from quantile regression model

Environmental science and pollution research international(2023)

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摘要
This study analyses the relationship between democracy and environmental pollution in the MINT countries using a panel data spanning 1971–2016. It also investigates the interactive effect of income and democracy on CO 2 emissions. We used various estimation techniques for the analysis, ranging from the quantile regression, OLS-fixed effect and GLS-random effect regressions with Driscoll-Kraay standard errors to control for cross-sectional dependence while a panel threshold regression is used for robustness check. The results showed existence of long-run relationship between CO 2 emissions and the explanatory variables. The quantile regression results for interaction model indicate that economic growth, democracy and trade openness promote environmental pollution via their positive effects on CO 2 emissions. Primary energy however reduces pollution across the lower and middle quantiles but enhances it in higher quantiles. The interaction effect is negative and statistically significant across all quantiles. This implies that democracy has a significant role in moderating the impact of income on CO 2 emission in the MINT countries. It thus follows that if the MINT countries radically strengthen democracy and enhance income, it would be possible for them to achieve greater economic development and reduce CO 2 . In addition, a single threshold model is used to identify the asymmetry in response to CO 2 emissions at lower and upper levels of democratic regimes. The results showed that once the degree of democracy is above the threshold level, an increase in income would reduce CO 2 emissions but once it is below the threshold level, the effect of income becomes insignificant. Based on these results, the MINT countries need to strengthen democracy, enhance income level and relax trade barriers.
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关键词
Democracy,Income,CO2 emissions,Quantile regression model
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