The impact of income inequality on environmental quality: a sectoral-level analysis

Abstract

Many studies in the literature examine the income inequality-environment nexus at the country level. In this paper, we argue that the impact of inequality on sectoral emissions might vary and should be examined by considering sectoral-level differences. We focus on 28 OECD economies and use DOLSMG, BA-OLS, and CUP-FM estimators. Our findings reveal that a cointegration relationship exists among the series in the long run, indicating that both income and income inequality are crucial factors in sectoral emissions. The estimates show that a 1% increase in the Gini index leads to an increase in emissions from the power and building sectors by about 1.4%. On the other hand, a 1% rise in the Gini index positively contributes to the environment in the transport, other industrial combustion, and other sectors by about 0.05%, 0.05%, and 0.02%, respectively. Policies aimed at reducing carbon emissions should be designed at the sectoral level.

Keywords:

• sectoral CO₂ emissions
• income inequality
• environmental Kuznets curve
• panel data
• OECD countries

JEL Codes:

• O15
• O44
• O50
• Q56
• C23

Acknowledgements

We would like to thank the anonymous referees and editor for supporting us in improving the earlier draft of this paper. All remaining errors are ours.

Authors’ contributions

Sedat Alataş: conceptualization, writing – review and editing, methodology. Tuğba Akın: data curation, software, formal analysis, visualization.

Availability of data and materials

Data is available from the authors on request.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Supplemental data

Supplemental data for this article can be accessed here.

Notes

1 For a detailed literature review, please see Section 2.

2 It is worth noting that some other studies also emphasize the importance of sectoral differences in emissions in the recent literature (Aslan, Destek, and Okumus 2018; Erdoğan et al. 2020; Fatima et al. 2021; Karakaya, Alataş, and Yılmaz 2020; Khan et al. 2020; Lin and Xu 2018; Sözen, Cakir, and Cipil 2016).

3 For further information regarding the EKC hypothesis, please see Section 3 or Aslan, Dogan, and Altinoz (2019) and Ulucak, Yücel, and Koçak (2019).

4 Please see section 3 for more information.

5 For further information about the theoretical background of the inequality-emissions nexus, please see Alataş (2022) and Grunewald et al. (2017).

6 Table 6 in the supplementary file provides detailed information regarding these studies.

7 We have divided Table 6 into two panels. While panel (a) shows the country-level studies, we present the state, regional, or household-level studies in panel (b).

8 We perform our empirical investigation for the 28 OECD countries (Australia, Austria, Belgium, Canada, Chile, Denmark, Finland, France, Germany, Greece, Ireland, Israel, Italy, Japan, Luxembourg, Mexico, Netherlands, New Zealand, Norway, Poland, Portugal, South Korea, Spain, Sweden, Switzerland, Turkey, United Kingdom, United States). We have excluded some countries from the sample due to data unavailability, especially for both income inequality and control variables.

9 For the pair-wise correlation matrix, please see Table 7 in the supplementary file.

10 For recent studies extending the EKC framework by adding control variables, please see Inglesi-Lotz (2019).

11 The results are available upon request from the authors.

12 To calculate the local maxima (or minima) points (turning point of income where emissions are at a maximum or minimum), we use the following formula: $\tau =\hspace{0.17em}\text{exp}\hspace{0.17em}\left[{-\beta }_2/{2\beta }_3\right],$ where ${\beta }_2$ and ${\beta }_3$ are the estimated coefficients of income per capita and its square presented in Equation (1)(1) $${\ln {\mathit{CO}}_2}_{\mathit{it}}={\mathrm{\beta }}_0+{\mathrm{\beta }}_1\mathrm{l}\mathrm{n}{\mathit{GINI}}_{\mathit{it}}+{\mathrm{\beta }}_2\mathrm{l}\mathrm{n}{\mathit{GDP}}_{\mathit{it}}+{\mathrm{\beta }}_3{\left(\ln {\mathit{GDP}}_{\mathit{it}}\right)}^2+{\mathrm{\beta }}_4{\ln \mathit{KOF}}_{\mathit{it}}+{\mathrm{\beta }}_5{\ln \mathit{URB}}_{\mathit{it}}+{\mathit{u}}_{\mathit{it}}$$(1) , respectively.

13 The assumption that the slope coefficients are homogeneous or heterogeneous can also affect the sign of the coefficients. Therefore, simply ignoring slope heterogeneity leads to biased results (Pesaran and Smith 1995). Due to assumption differences, some coefficients estimated from alternative estimators for the same model can obtain opposite signs.