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International Journal of Energy Economics and Policy | Vol 8 • Issue 1 • 2018 195

International Journal of Energy Economics and 
Policy

ISSN: 2146-4553

available at http: www.econjournals.com

International Journal of Energy Economics and Policy, 2018, 8(1), 195-202.

Carbon Emissions and Economic Growth in South Africa: 
A Quantile Regresison Analysis

B. Mapapu1, Andrew Phiri2*

1Department of Economics, Faculty of Business and Economic Studies, Nelson Mandela Metropolitan University, Port Elizabeth, 
South Africa, 2Department of Economics, Faculty of Business and Economic Studies, Nelson Mandela Metropolitan University, Port 
Elizabeth, South Africa. *Email: phiricandrew@gmail.com

ABSTRACT

Of recent carbon emissions have become an increasing concern for economies worldwide. In this study we investigate the relationship between carbon 
emissions and economic growth for the South African economy, one of the largest emitters of carbon dioxide worldwide. We employ the quantile 
regression methodology which is applied to annual data covering a period of 1970–2014. Our empirical results indicate that very low levels of 
carbon emissions are most beneficial towards economic growth. Our results thus encourage policymakers to continue to embark on energy efficiency 
programmes which specifically target lower levels of carbon pollution.

Keywords: Carbon Emissions, Economic Growth, Environmental Kuznets Curve, South Africa, Quantile Regressions 
JEL Classifications: C13; C32; C51; Q43; Q53.

1. INTRODUCTION

Following the seminal works of Grossman and Krueger (1991; 
1995) much empirical attention has been directed towards 
examining the relationship between environmental degradation 
and economic development, a phenomenon popularly dubbed 
as the environmental Kuznets curve relationship or hypothesis. 
Theoretically, the environmental Kuznets curve depicts that 
during the early stages of economic development, environmental 
degradation is a catalyst for improved economic development 
only up to certain level of development of which afterwards it 
begins to exert an adverse effect. Regardless of this hypothesized 
nonlinear, inverted U-shaped relationship between environmental 
degradation and economic development a bulk of the existing 
empirical literature has relied on linear econometric methodologies 
in examining the relationship between carbon emissions and 
economic growth Ang (2007), Ozturk and Acaravci (2010), Esteve 
and Tamarit (2012) and Cerdeira-Bento and Moutinho (2015), 
Shahbaz et al. (2015), Alam et al. (2016), Tang et al. (2016). 
The danger with this approach is that inaccurate conclusions 
concerning the Environmental Kuznets curve may have been 
deduced in the previous literature.

In our study, we examine the relationship between carbon 
emissions and economic growth for the South African economy 
over a period of 1971–2013. In deviating from the norm of 
linear estimation techniques, we choose the quantile regressions 
methodology as introduced by Koenker and Bassett (1978) as 
our mode of empirical investigation. We favour this technique 
since it examines the effects of regressor variables on the regress 
and at different quantile distributions. In adopting this method 
we are offered the unique advantage of being able to examine 
the effects of varying levels of carbon emissions on economic 
growth hence increasing the scope of policy relevance derived 
from our study. This becomes particularly significant towards an 
emerging economy like South Africa, whose heavy reliance on 
coal-based energy production has placed the country as the African 
continents number one carbon emitter. Knowing what effects 
carbon emissions exerts on economic growth is directly crucial 
towards South African policymakers as they are currently engaged 
in energy efficiency strategies aimed at reducing carbon pollution.

Empirically, our study further takes into consideration the fact that 
a majority of the existing empirical studies have been criticized 
on the premise of including both carbon emissions and energy/



Mapapu and Phiri: Carbon Emissions and Economic Growth in South Africa: A Quantile Regression Analysis

International Journal of Energy Economics and Policy | Vol 8 • Issue 1 • 2018196

electricity consumption as mutual regressors of economic growth 
hence violating the classical assumption of orthogonality between 
the regressors (Burnett et al., 2013). As a simple remedy to this 
multicollinearity problem, Burnett et al. (2013) advises researchers 
to exclude energy/electricity consumption from the estimated 
growth regressions and solely include carbon emissions and other 
growth determinants in the estimated regressions. We note that 
previous South African case studies have not followed in pursuit 
of this empirical rule Menyah and Wolde-Rufael (2010), Kohler 
(2013), Shahbaz et al. (2013a), Khobai and Le Roux (2017) hence 
providing a strong motivation for a fresh perspective on the subject 
matter. In our study, we take advantage of this empirical hiatus and 
in doing so, make a novel contribution to the literature.

Having provided the background and motivation to the study, the 
rest of the manuscript is arranged as follows. The following section 
of the paper presents the review of the previous literature whilst the 
third section outlines the quantile regressions methodology used 
in the empirical study. The description of the time series data as 
well as the empirical findings are presented in the fourth section 
of the paper. The paper is then concluded in the fifth section of 
the paper in the form of policy implications.

2. A REVIEW OF THE ASSOCAITED 
LITERATURE

Theoretically, Antweiler et al. (2001), Coxhead (2003) and Ang 
et al. (2007) all postulate that the assumed relationship between 
environmental deregulation/pollution and economic development 
(i.e., the Environmental Kuznets curve) can be explained by three 
factors. Firstly, there is the scale effect which occurs as pollution 
increases with the size of the economy. Secondly, there is the 
composition effect which refers to the change in the production 
structure of an economy from agricultural based to industry and 
service based which results in the reallocation of resources. Lastly, 
there is the production techniques which indicates that improved 
technology in production may reduce the amount of pollutant 
emissions per unit of production. Empirically, a vast majority 
of the existing academic literature concerned with examining 
the environmental Kuznets curve, have opted to investigate the 
relationship between carbon emissions and economic growth as 
a means of empirically examining the environmental Kuznets 
curve for different economies, using different time periods as 
well as a variety of econometric tools. In providing a review of 
the associated literature, we conveniently generalize the studies 
into two classifications of empirical works, namely, studies who 
focus on developed or industrialized countries and those studies 
who focus on developing or emerging countries.

The first group of studies, which are those studies which have 
examined the relationship between carbon emissions and economic 
growth for developed or industrialized economies include the works 
of Ang (2007) for France; Ozturk and Acaravci (2010) for Turkey; 
Menyah and Wolde-Rufael (2010a) for the US; Esteve and Tamarit 
(2012) for Spain as well as Cerdeira-Bento and Moutinho (2015) for 
Italy. Whilst the studies of Ang (2007); Menyah and Wolde-Rufael 
(2010a) and Esteve and Tamarit (2012) advocate for a positive 

relationship between the time series, the works of Ozturk and 
Acaravci (2010) and Cerdeira-Bento and Moutinho (2015) both find 
a negative emissions-growth relationship. It should be noted that 
cording to theory it is more probable to find a negative relationship 
between emissions and economic growth since industrialized 
economies, are by definition, countries who are at advanced stages 
of development. Given the mixed results obtained from the review 
of developed or industrialized economies, the debate concerning 
these countries remains open to further deliberation.

On the other hand the papers published by Menyah and Wolde-
Rufael (2010b) for the South Africa, Kohler (2013) for South 
Africa; Shahbaz et al. (2012) for South Africa for Pakistan; 
Shahbaz et al. (2013) for South Africa; Shahbaz et al. (2013) 
for Indonesia; Shahbaz et al. (2013) for Romania; Farhani et al. 
(2014) for Tunisia; Begum et al. (2015) for Malaysia; Rafindadi 
(2016) for Nigeria; Khobai and Le Roux (2017) and Ahmad et al. 
(2017) for Croatia suffice as those concerned with examining the 
emissions-growth relationship for developing countries, with the 
studies of Kohler (2013), Shahbaz et al. (2013b), Khobai and Le 
Roux (2017) exclusively focusing on the South African economy. In 
summarizing these studies we note that whilst the works of Shahbaz 
et al. (2013c), Rafindadi (2016) and Khobai and Le Roux (2017) 
advocate for a positive emissions-growth relationship, however, 
the remaining reviewed studies for developing countries mutually 
find a positive emissions-growth relations at low levels which 
turns negative at higher levels. These later group of studies are 
able to capture a nonlinear carbon emissions-growth relationship 
by including a squared term on the GDP variable which is intended 
to capture possibly nonlinear dynamics. However, as pointed out by 
Narayan et al. (2016) including both GDP and the squared term of 
GDP in the same regression would make the econometric model to 
suffer from the issue of multicollinearity. In contrast, the quantile 
regressions methodology applied in our current study naturally 
captures any nonlinearity hence the inclusion of the “squared carbon 
emissions” term is not necessary and hence circumvents the issue 
of multicollinearity. Nevertheless, a comprehensive summary of 
the reviewed studies are presented in Table 1.

3. METHODOLOGY

Our baseline empirical model assumes the following functional 
form:

Yt = β0+βiXt+et (1)

Where Yt is the economic growth rates, Xt is a set of explanatory 
variables, β’s represent the associated regression coefficients and 
et is a well behaved error term. Our main explanatory variable is 
carbon emissions (CO2t) and the remainder of the conditioning 
variables are those primarily dictated by theoretical considerations 
based on the literature. For instance, our first conditioning variable 
is the investment variable (invt) which, according to classical 
theory is assumed to the engine of economic growth and is hence 
positive related to economic growth. Our second conditioning 
variable is the inflation rate (inft) and based on conventional 
growth theory is assumed to hinder economic growth and hence 
empirically exhibit a negative effect on economic growth. Our 



Mapapu and Phiri: Carbon Emissions and Economic Growth in South Africa: A Quantile Regression Analysis

International Journal of Energy Economics and Policy | Vol 8 • Issue 1 • 2018 197

third variable is the employment variable (empt), which according 
to growth theory is assumed to be positively correlated with 
economic growth. Our last conditioning variables is the terms 
of trade variable (tott), which represents international trade and 
the open economy which is assumed to exert a positive influence 
on economic growth. Collectively, our baseline empirical 
specification can be expounded as follows:

Yt = β0+β1CO2t+β2INVt+β3INFt+β4EMPt+β5TOTt+et (2)

From regression (1) in conjunction with regression (2), the 
conventional OLS estimates would be obtained by finding the 
vector βi that minimizes the sum of squares residual i.e.,,

min [ ( )
'

β β
β

∈ ∈ ≥
−∑R i {i:y x } i ik i i y x

2
 (3)

In contrast, the quantile regression approach adopted in our study 
is a generalization of the median regression analysis to other 
quantiles. In particular, the mean average deviations (MAD) 
estimator can be computed as:

min [ /
'

β β
β

∈ ∈ ≥
−∑R i {i:y x } i ik i i y x /]

2
 (4)

Of which the MAD estimate depicted in regression (4) can be 
re-specified as:

min [ / ( ) /
' '

β β β
β β

∈ ∈ ≥ ∈ ≥
− − −∑ ∑R i {i:y x } i i i {i:y x } i ik i i i iy x /+ y xτ τ1 //]  

 (5)

Where τ represents the τth quantile and is specifically set at 0.5 for 
the MAD estimator. The general intuition of the quantile regression 
estimates is to use varying values of τ bound between 0 and 1 
hence yielding the regression quantiles for varying distributions 
of GDP growth given the set of explanatory variables contained in 
the vector X. In our study we opt to use 9 quantiles with intervals 
of 0.1 between the quantiles i.e., τ = {0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 
0.7, 0.8 and 0.9}.

4. DATA AND EMPIRICAL RESULTS

4.1. Data Description
The empirical data used in our study has been collected from 
the World Bank online database and has been collected on an 
annual basis for a period ranging from 1970 to 2014. Our dataset 
particularly consist of economic growth (gdp), Carbon emissions 
(CO2), CPI inflation (inf), gross domestic investment (inv), and 
terms of trade (tot) variables. Tables 2 and 3, present the descriptive 
statistics and the correlation matrices of the time series whereas 
Figure 1 presents a time series plots of the variables. Of particular 
interest from the descriptive statistics reported in Table 2 are the low 
GDP average of 2.63% which we note is well below the 6 percent 
target growth rate currently being embarked by policymakers. We 
also note that the average inflation rate over our study period is 
9.62, a Figure which is above the 3–6% as stipulated by the South 
African Reserve Bank. Moreover, the low employment average of 
1.75 is inherent characteristic of the South African economy, which 
is well known for her labour market deficiencies.

Table 1: Summary of literature review
Author (s) Country/countries Time 

period
Methodology Results

Ang (2007) France 1960–2000 VECM Positive relationship between CO2 and GDP
Ozturk and Acaravci (2010) Turkey 1965–005 ARDL Negative relationship between CO2 and GDP
Menyah and 
Wolde-Rufael (2010a)

US 1960–2007 VAR Positive relationship between CO2 and GDP

Menyah and 
Wolde-Rufael (2010b)

South Africa 1965–2007 ARDL Positive relationship between CO2 and GDP

Shahbaz et al. (2012) Pakistan 1971–2009 ARDL Positive relationship between CO2 and GDP at low 
levels which turns negative at higher levels

Kohler (2013) South Africa 1960–2009 VECM and 
ARDL

Positive relationship between CO2 and GDP at low 
levels which turns negative at higher levels

Shahbaz et al. (2013b) South Africa 1965–2008 ARDL Positive relationship between CO2 and GDP at low 
levels which turns negative at higher levels

Shahbaz et al. (2013c) Indonesia 1975–2011 VECM and 
ARDL

Positive relationship between CO2 and GDP

Shahbaz et al. (2013a) Romania 1980–2010 ARDL Positive relationship between CO2 and GDP at low 
levels which turns negative at higher levels

Farhani et al. (2014) Tunisia 1971–2008 ARDL Positive relationship between CO2 and GDP at low 
levels which turns negative at higher levels

Cerdeira-Bento and 
Moutinho (2015)

Italy 1960–2011 ARDL Negative relationship between CO2 and GDP

Begum et al. (2015) Malaysia 1970–2009 ARDL Insignificant relationship between CO2 and GDP at 
low levels which turns negative at higher levels

Rafindadi (2016) Nigeria 1971–2011 VECM and 
ARDL

Positive relationship between CO2 and GDP

Khobai and Le Roux (2017) South Africa 1971–2013 \VECM Positive relationship between CO2 and GDP
Ahmad et al. (2017) Croatia 1992–2011 ARDL Positive relationship between CO2 and GDP at low 

levels which turns negative at higher levels
ARDL: Autoregressive distributive lag model, VECM: Vector error correction model, VAR: Vector autoregressive model



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International Journal of Energy Economics and Policy | Vol 8 • Issue 1 • 2018198

On the other end of the spectrum, the correlation matrix as depicted 
in Figure 2, tends to depict correlations which concur with those 
predicted by conventional growth theory. For instance, we note 
positive employment-growth and trade-growth relations which 
adheres to traditional economic theory. Similarly, the negative 
inflation-growth and emissions-growth relations are expected. 
However, the negative correlation established between investment 
and growth is a rather peculiar observation since investment 
is commonly perceived as the engine of economic growth. 
Nevertheless, this negative investment-growth correlation is not 
uncommon in the literature as recently advocated for in the study 
of Phiri (2017).

4.2. Empirical Estimates
In initiating our empirical analysis, we first provide the OLS 
estimates of the regression with the results being reported in 

Table 4. As can be observed, the coefficient on the carbon 
emissions variable produces a positive estimate which is 
statistically significant at all critical levels. Note that this result is in 
line with that presented in Shahbaz et al. (2013), Rafindadi (2016) 
and Khobai and Le Roux (2017). Also note that the coefficient on 
the inflation variable is negative and highly significant as expected 
and this particular finding concurs with that presented in Hodge 
(2006) for similar South African data. We further observe a positive 
coefficient estimate on the employment variable thus providing 
evidence of a positive employment-growth relationship as 
predicted by convention theory. On the other end of the spectrum, 
we note insignificant coefficients on both the investment and terms 
of trade variables which is contrary to conventional theory and yet 
concurs with that presented in the study of Phiri (2017) for South 
African data. Our reported results are reinforced by the partialled 
plots of GDP on the regressors as depicted in Figure 2.

However, as previously mentioned, the OLS estimates have been 
heavily criticized for constraining the coefficient on the regressand 
variables to be the same across different quantiles. Therefore, we 
proceed to present the empirical estimates of the quantile regressions 
which have been performed for 9 quantiles (i.e., 10th, 20th, 30th, 40th, 
50th, 60th, 70th, 80th and 90th quantiles) with the results been reported 
in Table 5. As can be observed, the coefficient estimates for the 
carbon dioxide variable are positive across all quantiles and are 
significant at a critical level of at least 5%. However, the positive 
effect of carbon emissions on GDP are amplified at the tail ends of 
distribution (i.e., very low and very high levels of carbon emissions) 
with the coefficients reducing as one moves from the extreme 
quantile (i.e., 10th and 90th quantiles) towards the centre quantile 
(50th quantile) which incidentally happens to be the MAD estimate.

On the other hand, the coefficients on the inflation variable are 
negative and significant at all quantile levels with the negative 
effects of the inflation variable being more pronounced at lower 
quantiles and the coefficients becoming lower as one moves don 
the quantile levels hence signifying a diminishing negative effect 
of inflation on economic growth as one moves along the quantile 
levels. Concerning the employment variable we note a positive 
coefficient on the employment variable across all estimated 
quantiles which are statistically significant at all critical levels with 
the marginal positive effect of employment on economic growth 
diminishing as one moves up the different quantile levels. In lastly 
observing the coefficients obtained for the investment and terms of 
trade variables we note that all quantile estimates produce negative 

Table 2: Descriptive statistics
??? gdp CO2 inf inv emp tot
Mean 2.63 8.58 9.62 21.90 1.75 1.79
Median 2.95 8.70 9.37 20.75 1.30 2.10
Maximum 6.60 10.04 18.65 32.10 8.50 20.00
Minimum −2.10 6.65 1.39 15.20 −4.30 −16.20
Std. dev. 2.27 0.93 4.21 5.06 2.68 6.44
Skewness −0.43 -0.28 0.14 0.40 0.28 -0.01
Kurtosis 2.35 2.01 2.01 1.85 3.15 4.15
Jarque bera 2.15 2.38 1.94 3.61 0.62 2.44
Probability 0.34 0.30 0.38 0.16 0.73 0.30
observations 44 44 44 44 44 44

Table 3: Correlation matrix
gdp CO2 inf inv emp tot

gdp 1
CO2 −0.20 1
inf −0.39 0.11 1
inv −0.03 −0.35 0.43 1
emp 0.64 −0.25 0.18 0.37 1
tot 0.17 −0.08 −0.14 −0.01 0.09 1

Table 4: OLS estimates
Variable Coefficient Standard error t-stat P value
CO2 0.45 0.12 3.85 0.00***
Inf −0.28 0.05 −5.59 0.00***
Inv 0.01 0.05 0.28 0.78
Emp 0.66 0.10 6.31 0.00***
Tot 0.01 0.03 0.52 0.60
***, **, * represent 1%, 5% and 10% significance levels, respectively

Table 5: Quantile regression estimation results
tau CO2 INF INV EMP TOT

Coefficient P value Coefficient P value Coefficient P value Coefficient P value Coefficient P value
0.1 0.52 0.00*** −0.30 0.00*** −0.09 0.29 0.79 0.00*** 0.03 0.62
0.2 0.53 0.02** −0.31 0.00*** −0.08 0.47 0.72 0.00*** 0.04 0.54
0.3 0.51 0.00*** −0.22 0.00*** −0.08 0.43 0.72 0.00*** 0.01 0.95
0.4 0.34 0.04* −0.25 0.00*** 0.04 0.51 0.63 0.00*** −0.05 0.30
0.5 0.36 0.02** −0.28 0.00*** 0.07 0.29 0.64 0.00*** 0.03 0.59
0.6 0.41 0.01** −0.29 0.00*** 0.06 0.33 0.64 0.00*** 0.03 0.66
0.7 0.44 0.00*** −0.25 0.01** 0.04 0.49 0.67 0.00*** 0.02 0.71
0.8 0.43 0.00*** −0.22 0.06* 0.03 0.51 0.68 0.00*** 0.05 0.35
0.9 0.43 0.00*** −0.16 0.00*** 0.06 0.39 0.48 0.00*** 0.05 0.34
***, **, * Represent 1%, 5% and 10% significance levels, respectively



Mapapu and Phiri: Carbon Emissions and Economic Growth in South Africa: A Quantile Regression Analysis

International Journal of Energy Economics and Policy | Vol 8 • Issue 1 • 2018 199

and insignificant coefficient estimates for the former variable 
while producing positive and insignificant estimates for the later 
variable. Note that these results closely emulate those obtained 
from the previous OLS estimates. The associated quantile process 
estimates are presented in Figure 3.

4.3. Residual Diagnostics
As a final step in our empirical analysis, we implement diagnostic 
test to our estimated regression. In particular, we implement 
two diagnostic tests, namely, Ramsey’s RESET test for model 
misspecification and Jarque-Bera (J-B) goodness of fit test. The 
results of these diagnostic tests are reported in Table 6 and as can 
be seen our empirical estimates contain no specification errors and 
are normally distributed. We thus consider our obtained quantile 
regression estimates to be plausible.

5. CONCLUSION

The primary objective of this current study has been to evaluate 
the relationship between carbon emissions and economic growth 
in South Africa using annual data collected over a 44 years period 
spanning from 1970 to 2013. In differing from pervious empirical 
studies, we employ the quantile regression approach which 
provides the advantage of assuming parameter heterogeneity in 
analysing the effects of carbon emissions on economic growth. 
Moreover, we circumvent the possibility of multicollinearity 
within the estimated regression estimates by not including energy/
electricity consumption alongside carbon emissions as regressors 
in the estimated growth model.

Our obtained empirical results confirm positive relationship 
between carbon emissions and economic growth, albeit, the 
positive effect being most magnified at extremely low or extremely 
high values and diminishing as one moves to centre values. We 
consider the overall positive relationship to be expected since 
South Africa is well known for her dependency on coal usage in 
producing energy for productive and consumption usage within 
different sectors of the economy. Hence given the country’s current 

Figure 1: Time series plots of the variables

Table 6: Diagnostic tests on estimated quantile regression
Test Statistic P value Decision
Ramsey RESET test 4.31 0.11 No specification 

error
Jarque-Bera (J-B) 2.28 0.31 Normal distributed 

regression



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International Journal of Energy Economics and Policy | Vol 8 • Issue 1 • 2018200

stage/level of economic development increased electricity usage 
in South Africa would be accompanied with increased carbon 
emission as well as improved economic growth. However, our 
quantile estimates indicate that very low levels of carbon emissions 
are most beneficial for economic development through improved 
economic growth rates.

In effect our study bears important policy implication since 
policymakers have been embarking on energy efficiency 
programmes over the last decade and a half or so. Part and parcel 
of these energy efficiency programmes is to shift from coal-based 
energy production schemes to renewable energy sources which 
would exert a positive environmental effect in terms of greenhouse 

Figure 2: GDP versus other variables



Mapapu and Phiri: Carbon Emissions and Economic Growth in South Africa: A Quantile Regression Analysis

International Journal of Energy Economics and Policy | Vol 8 • Issue 1 • 2018 201

emissions. From a policy perspective, our results imply it would 
be in government’s best interest to keep carbon emissions as 
low as possible to fulfil the macroeconomic policy objectives of 
improving both environmental degradation and long-run economic 
growth. Based on our study, government’s current pursuit of energy 
programmes and strategies though increased renewable energy 
sources is thoroughly encouraged.

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