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Effects of energy consumption, economic growth, and financial development on carbon emissions: evidence from heterogeneous income groups

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Abstract

This paper examines the effects of energy consumption, economic growth, and financial development on carbon emissions in a panel of 122 countries. We employ both first-generation and second-generation cointegration and estimation procedures in order to address diverse economic and econometric issues such as heterogeneity, endogeneity, and cross-sectional dependence. We find a cointegration relationship between the variables. Energy consumption, economic growth, and financial development have detrimental effects on carbon emissions in the full sample. When the sample is split into different income groups, we reveal that economic growth and financial development mitigate carbon emissions in high-income group but have the opposite effects in low-income and middle-income groups. The implication of the findings is that energy consumption increases carbon emissions. While high levels of income and financial development decrease carbon emissions, low levels of income and financial development intensify it. Based on the findings, the paper makes some policy recommendations.

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Notes

  1. All data are obtained from World Development Indicators (2018) of the World Bank.

  2. All the data are obtained from World Development Indicators (2018) of the World Bank.

  3. All the data are obtained from World Development Indicators (2018) of the World Bank.

  4. Economic growth and real GDP per capita are used interchangeably in this study.

  5. Baltagi (2008) highlighted the motivation of using panel data as follows: first, panel data are better able to identify and measure effects that are simply not detectable in pure time series data. Second, panel data give more informative data, more variability, less collinearity among the variables, more degrees of freedom and more efficiency. With additional more informative data, one can produce more reliable parameter estimates. Third, panel data control for individual heterogeneity. Panel data can control for country- and time-invariant variables while time series study cannot. Fourth, panel data are better able to study the dynamics of adjustment, thereby shedding light on the speed of adjustments to economic policy changes. Finally, panel data models allow us to construct and test more complicated behavioral models compared to purely time series models.

  6. We proxy financial development with credit to private sector relative to GDP which is the most commonly used proxy that measure the credit that goes to the private sector (see Al-Mulali et al. 2016b; Jalil and Feridun 2011; Shahbaz et al., 2013a, b, c; Hafeez et al. 2018; Salahuddin et al. 2015). Although we attempted to use another proxy of financial development (e.g., liquid liabilities relative to GDP) that measures financial depth, or stock market development indicators (e.g., market capitalization relative to GDP), but we could not get complete data for the 122 countries for the 1990–2014 period. When data become readily available, we suggest that future research should consider this possibility for comparison.

  7. Our panel data of 122 countries covering 1990–2014 period are appropriate (not too small) for Westerlund’s (2007) error correction model–based panel cointegration test because this cointegration test produces reliable results even in small sample size. Westerlund (2007, p. 709) said “Our simulation results suggest that the tests have good small-sample properties with small size distortions and high-power relative to other popular residual-based panel cointegration tests.” In his empirical application, Westerlund (2007, p.733–736) presented empirical evidence to show that international healthcare expenditures and GDP are cointegrated in a small sample size using annual panel data of 20 OECD countries for 1970–2001 period. Moreover, some studies have also used the Westerlund’s (2007) error correction model–based panel cointegration test for similar sample size (e.g., Persyn and Westerlund 2008).

  8. This study controls for endogeneity by using the GMM developed by Arellano and Bond (1991) which is renowned for controlling for endogeneity and autocorrelation.

  9. Our data characteristics determine the panel estimation techniques we employ in this study. First, since our variables are integrated of order one and cointegrated, we employ the estimation techniques which are appropriate for cointegrated panels such as DOLS and FMOLS. Thereafter, we test for cross-sectional dependence, and the results show the existence of cross-sectional dependence. To account for this, we employ the estimation techniques that can account for cross-sectional dependence, and also appropriate for cointegrated panels such as CCE and DCCE. Moreover, autocorrelation is potentially common to all time series or long span panel data (such as our 25-year data span). Similarly, endogeneity is a potential common concern in many economic variables. Thus, we employ GMM to control for potential autocorrelation and endogeneity in the model.

  10. Our original intention was to include all countries in the world that have data on carbon emissions, economic growth, energy consumption, and financial development for a minimum of three decades. Unfortunately, after intensive search, we only able to find data for 122 countries for 1990–2014 period. Hence, unavailability of data limited the scope of this study.

  11. We also conducted another cointegration test using the Lagrange multiplier-based cointegration tests proposed by Westerlund and Edgerton (2007) that allow for heteroskedastic and serially correlated errors as well as structural breaks in the intercept and slope. The results (not reported for want of space, but available upon request) reject the null hypothesis of no cointegration, implying that cointegration relationship exists among the variables in all the panels.

  12. We thank the anonymous reviewer for this comment. Although the focus of this study is panel data analysis rather than time series analysis, our empirical strategy enables us to obtain the estimation results of individual-specific country through the CCE estimator. The results are not reported for lack of space, but available upon request. A summary of the results shows that economic growth, energy consumption and financial development are significant determinants of CO2 emissions in most of the countries. However, we do not give much attention to the individual-specific country estimation results because the sample size is too short for time series analysis.

  13. For more information, see Dhaene and Jochmans (2015); Chudik and Pesaran (2015).

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Authors and Affiliations

  1. School of Finance and Economics, Taylor’s University Selangor, Subang Jaya, Selangor, Malaysia

    Kizito Uyi Ehigiamusoe

  2. Economics Program, School of Social Sciences, Universiti Sains Malaysia, Gelugor, Malaysia

    Hooi Hooi Lean

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  1. Kizito Uyi Ehigiamusoe
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Correspondence to Hooi Hooi Lean.

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Appendix

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Table 11 List of countries included in the study
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Ehigiamusoe, K.U., Lean, H.H. Effects of energy consumption, economic growth, and financial development on carbon emissions: evidence from heterogeneous income groups. Environ Sci Pollut Res 26, 22611–22624 (2019). https://doi.org/10.1007/s11356-019-05309-5

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