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CO2 emissions, income inequality, and country risk: some international evidence

  • Research in Environmental Governance and Innovation
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Abstract

This research explores the effects of income inequality and country risk on CO2 emissions and examines whether the effects change across countries with different development stages or income levels. A new panel quantile regression approach is used to conduct a comprehensive analysis of the impacts of affecting factors on CO2 emissions at various quantiles, while addressing econometric challenges such as endogeneity and heterogeneity. From a global perspective, we can conclude that the marginal impact of inequality on emissions drops constantly with decreasing country risk at 10th to 50th quantiles, which even performs negative, whereas at the other quantiles, the marginal impact of inequality always remains negative. When we focus on the different income groups, the nexus of inequality emissions is negative first and then positive with decrease of country risk in low-income countries but shows no significant in low-middle- and upper-middle-income countries. Additionally, we validate the detrimental impact of income inequality in upper-income countries. Besides, country risk adversely moderates the nexus of inequality and emissions in low- and upper-income countries. Empirical results confirm that the nexus of inequality emissions lies in country risk, income level, and existing emission degree. These findings provide some important recommendations for policy-makers.

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Notes

  1. This classification is based on World Development Indicators (2019) of the World Bank. Detailed information about country groups refers to Table A1.

  2. It should be noted that many other variables related to CO2 emissions should be included in the model, such as economic growth (Akbostancı et al. 2009; Ehigiamusoe and Lean 2019; Ehigiamusoe et al. 2019; Ehigiamusoe et al. 2020), population density (You et al. 2015; Kang et al., 2016), trade openness (Rafiq et al. 2016; Ozatac et al. 2017), urbanization (Zhu et al. 2012; Rafiq et al. 2016; Wang et al. 2018), and industrialization (Li and Lin 2015).

  3. Detailed country risk components are described in Table A2.

  4. This selection is based on the data available. Specially, research period is limited since the data of CO2 emissions is not available after 2014 based on the WDI.

  5. Due to the data available, the low-income countries group only has five countries. Researchers might occasionally encounter the faulty opinion that quantile regression is achieved by subdividing the independent variable into subsets according to the corresponding unconditional distribution and then using OLS on them. Actually, the estimation of quantile regression is obtained by some transformation of the various probabilities of exceeding the chosen cutoffs. Thus, a panel quantile regression helps to obtain consistent results for this group, and the explanation for these results is credible. More details refer to Koenker and Hallock (2001).

  6. Cross-sectional dependence is an important issue, which need to be addressed. According to Machado and Silva (2019)(page 4), the authors first subtract the cross-sectional averages from the series. Levin et al., (2003) suggest this procedure to mitigate the impact of cross-sectional dependence. Thus, we argue that the estimators seem less susceptible to cross-sectional dependence. We sincerely thank an anonymous referee for pointing out this issue.

  7. We are grateful to the referee for this constructive comment.

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Acknowledgments

The authors are grateful to the Editor and the anonymous referees for helpful comments and suggestions. These authors contributed equally to this study and share first authorship. The authors gratefully acknowledge the financialsupport from the Ministry of Education of Humanities and Social ScienceProject of China (No. 19YJC630206) and the Natural Science Foundationof Fujian Province under grant (No. 2019J01215).

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Authors and Affiliations

  1. School of Economics and Management, Fuzhou University, Fuzhou, People’s Republic of China

    Yawei Guo & Wanhai You

  2. Research Center of the Central China for Economic and Social Development, Nanchang University, Nanchang, China

    Chien-Chiang Lee

  3. School of Economics and Management, Nanchang University, Nanchang, China

    Chien-Chiang Lee

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  1. Yawei Guo
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Appendix

Appendix

Table 10 List of 73 countries and their groups.
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Table 11 Details of ICRG risk rating.
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Guo, Y., You, W. & Lee, CC. CO2 emissions, income inequality, and country risk: some international evidence. Environ Sci Pollut Res 29, 12756–12776 (2022). https://doi.org/10.1007/s11356-020-09501-w

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