Mitigation pathways toward sustainable development

1. R E S E A R C H A R T I C L E
Mitigation pathways toward sustainable development: Is there
any trade-off between environmental regulation and carbon
emissions reduction?
Danish1
| Recep Ulucak2
| Salah Ud-Din Khan3
|
Muhammad Awais Baloch4
| Nan Li5
1
School of Economics and Trade, Guangdong
University of Foreign Studies, Guangzhou,
510006, China
2
Faculty of Economics and Administrative
Sciences, Department of Economics, Erciyes
University, Kayseri, Turkey
3
Sustainable Energy Technologies (SET)
Center, College of Engineering, King Saud
University, Riyadh, Saudi Arabia
4
School of Economics and Management, Baoji
University of Arts and Sciences, Baoji, China
5
School of Business, Guangdong University of
Foreign Studies, Guangzhou, 510006, China
Correspondence
Nan Li, School of business, Guangdong
University of Foreign Studies, Guangzhou
510006, China.
Email: linan010@126.com;
nanli_gdufs@hotmail.com
Funding information
Nan Li & Danish, Grant/Award Number:
2016A070705055; 299-GK19071; Salah
Ud-Din Khan
Abstract
The income and pollution relationship is widely debated in the literature, but income
alone cannot control emissions, and that requires environmental regulation measures.
Even though weak environmental regulations may offset market failures caused by
negative externality of pollution, investigation of their role in the control of environ-
mental pollution is of prime importance. To better understand the role of environ-
mental regulations in carbon emissions mitigation, this study takes a step to offers a
new perspective on the role of environmental regulations in pollution reduction for
Brazil, Russia, India, China, and South Africa. To this end, the study employs panel
data econometric tools for data spanning from 1995 to 2016. Besides, the study con-
ducts fully modified ordinary least squares estimator to get country-wise results.
Empirical results confirm the positive role of environmental regulations in carbon
emission mitigation, and the current environmental control measures are successful
in achieving pollution reduction targets in the sample countries. Environmental regu-
lations help in establishing the inverted U-shaped relationship between income and
pollution. Climate change mitigation in BRICS (Brazil, Russia, India, China, and
South Africa) countries is not associated with economic development only but driven
by effective environmental regulations as well.
K E Y W O R D S
advanced panel data estimator, BRICS, carbon emission mitigation, environmental regulations
1 | INTRODUCTION
The rising environmental concerns are exactly related to a large amount
of carbon dioxide emission (CO2) emissions emitted to the atmosphere
and are being widely accepted in the literature that fossil energy con-
sumption is heavily responsible for this situation (Danish, Zhang,
Wang, & Wang, 2017b; Sarkodie & Strezov, 2019b). Recently, CO2 has
trigged from 19,809 million tons to 33,431 million tons around the
world (BP, 2018), and posed more significant challenges to human
health (Z. Wang, Huang, & Chen, 2019). The concentration of CO2 has
risen to the highest level since the several decades, and it has been a
critical component, shared more than 60%, of greenhouse gas (GHG)
emissions (IPCC, 2014). So, CO2 emissions are the main reason leading
to global warming, and climate change (Danish, Zhang, Wang, & Wang,
2017a; Sarkodie et al., 2019). In parallel with CO2 emission rises the
fast-growing climate change has been the most vital threat for
humanity.
The BRICS (Brazil, Russia, India, China, and South Africa) has
experienced remarkable economic growth over the past two decades.
The BRICS countries occupy 40% population of the world, make
nearly 21% contribution to the world's gross domestic product (GDP),
consume 40% of the world's energy, and have a notable share of the
Received: 24 September 2019 Revised: 6 November 2019 Accepted: 25 November 2019
DOI: 10.1002/sd.2032
Sustainable Development. 2020;28:813–822. wileyonlinelibrary.com/journal/sd © 2019 John Wiley & Sons Ltd 813

2. world's CO2 emissions. The rapid economic growth in BRICS countries
further mounted the pressure of environmental deterioration
(Danish & Wang, 2019a). The GHG emissions reduction is essential to
address the detrimental impact of global climate change, and this
capacity is directly related to the degree of high CO2-emission coun-
tries’ commitment to the problem (Danish, Baloch, & Bo, 2019). The
BRICS countries are trying to adopt and seeking for pollution reduc-
tion measures, and technological progress is recognized as a signifi-
cant factor in mitigating carbon emissions. Meanwhile, it is believed
that carbon emissions are the undesired byproducts of the production
and consumption process, and that needs supplementary restrictions,
such as environmental regulation (H. Wang & Wei, 2019).
The ecological destruction and deterioration of environmental qual-
ity get much attention from the authorities and academic literature as
well (Danish & Wang, 2019b). Both Climate change and global warming
are a hazard to human health and the most significant ongoing worri-
some in the 21st century, abrogating and destructive climatic events
that keep destroying the whole planet (Danish et al., 2017a). To
strengthen economic prosperity while keeping ecological quality has
been in the first place of the international community's concerns
(X. Wang & Shao, 2019). On this note, it is realized that economic
growth alone may not a solution to struggle with environmental degra-
dation, and it should be coupled with environmental regulations. But,
defective regulations, such a strict carbon tax or lax policy enforcement
will encourage resource owners to increase the current extraction levels,
leading to a “green paradox” as emissions rise rather than fall (van der
Werf & di Maria, 2012). In this regard, lax environmental policies exe-
cuted by developing countries encourage multinational companies to
invest in pollution-intensive industries and shift their dirty technologies
to these countries and thus enable them to obtain comparative advan-
tages in the production of pollution-intensive goods (Z. Wang, Danish,
Zhang, & Wang, 2018). In contrary to this, if foreign companies have
eco-friendly technologies in their production process, the foreign direct
investment (FDI) would play a positive role in decreasing the environ-
mental pollution (Kretschmer, Hübler, & Nunnenkamp, 2010). Further-
more, in developing countries, governments may properly impose the
implementation of environmental policy measures to investors. With
more economic development, governments should enforce proper regu-
lations to offset any market failure causing environmental pollution and
respond to public awareness for environmental deterioration (Danish,
Baloch, & Bo, 2019). To regulate and enforce proper environmental reg-
ulations efficiently for a clean environment is of utmost importance,
even, effective government policy measures may encourage people to
pay for living a clean environmental (Z. Wang et al., 2018).
Questions still remain concerning the role of environmental regu-
lation in climate change mitigation. The complexities of economic
structure among countries impede to build a consensus on the deter-
minants of climate change across different countries. However, empir-
ical investigations, just like as is attempted in the present study, are
beneficial in climate change-related policy formulation. The sugges-
tion from the above motivations drives to investigate the role of
environmental regulations and income levels in mitigation of CO2
emissions for BRICS countries by using advanced econometric tools.
Such an investigation is timely and worthwhile, given the need for
efficient implementation of environmental regulations as well as inter-
national commitments for environmental issues in BRICS countries.
In order to contribute to the global debate on emission mitigation,
this study is the first attempt to examine the role of environmental
regulations in carbon emission reduction for BRICS countries. Further,
the study investigates the validity of the environmental Kuznets (EKC)
curve by considering environmental regulations, claiming that income
alone cannot control pollution but together with environmental regu-
lations may be super useful. Data availability is restricted the study for
time spanning from 1995 to 2016. The estimation methods used by
earlier studies were not capable of data issues, such as cross-sectional
dependence, heteroscedasticity, and serial correlation. The study
employs a more advanced econometric technique than current ones
conducted by previous studies in this field, Common Correlated
Effects Mean Group (MG-CCE) estimator developed by Pesaran
(2006) and later advanced by Kapetanios, Pesaran, and Yamagata
(2011). Employing the more advanced approach enables the current
study to obtain more reliable and robust results against some viola-
tions of basic assumptions for econometric estimations.
2 | REVIEW OF RELATED STUDIES
Earlier studies on the environmental regulation-CO2 emission relation-
ship are limited and have inconsistent results. For example, the study
of Hao, Deng, Lu, and Chen (2018) examined the effectiveness of envi-
ronmental regulation in Chinese 283 cities over the period 2003–2010
by using the generalized method of moment (GMM) technique. The
study is concluded that present environmental policies are not success-
ful in achieving pollution reduction targets. Cheng, Li, and Liu (2017)
assessed the impact of various types of environmental policies and reg-
ulations on CO2 emissions employing dynamic spatial models. The find-
ings of the study summarized that control and command regulations
are favorable for CO2 emissions reduction. Likewise, for China Chen,
Hao, Li, and Song (2018) carried out an empirical study to explore the
relationship between shadow economy, corruption and environmental
regulations and the environment in Chinese 30 provinces by using the
GMM estimator. Their empirical results reveal that tighter environmen-
tal regulation would be helpful in pollution reduction. Pei, Zhu, Liu,
Wang, and Cao (2019) considered the mediating role of technical effi-
ciency between environmental regulations and CO2 emissions by
employing provincial data of China spanning from 2005 to 2015. The
empirical results show that environmental regulations directly decrease
CO2 emissions, while technical efficiency has an indirect impact on the
decreasing. Analyzing the sectoral data of the United States, Jiang,
Zhou, and Liu (2019) determined the influence of uncertainty of eco-
nomic policies on CO2 emissions through the Granger causality
approach. Their results indicate that uncertainty in economic policies
affects CO2 emissions in all sectors. In a study for Organization for
Economic Co-operation and Development (OECD) countries, Hashmi
and Alam (2019) investigated the dynamic impact of innovation and
environmental regulation for the data spanning from 1999 to 2014 by
814 DANISH ET AL.

3. GMM technique. The results of the study approve the positive role of
environmental regulation in pollution reduction. Using the Tapio
decoupling and GMM estimation methods, Wenbo and Yan (2018)
analyzed the role of environmental regulation efficiency on CO2 emis-
sions by using Chinese province data from 2004 to 2015. Their results
claim an inverted U-shaped relationship between environmental regu-
lation and carbon emissions. Also, the efficiency of environmental reg-
ulation reduces pollution. Ouyang et al. (2019) used threshold analysis
in order to examine the nexus between economic growth, environmen-
tal policies and air pollution including socio-economic drivers in
30 OECD countries using PM2.5 as proxy for pollution. Results of the
study provide an evidence for heterogenous effect of environmental
policies on air pollution because at first environmental regulations rise
PM2.5 while later their impact becomes insignificant. Albulescu, Artene,
Luminosu, and T
am
ail
a (2019) reported the relationship among envi-
ronmental regulations, renewable energy, and CO2 emissions in
European Union (EU) countries between 1990 and 2017. The dynamic
and static GMM analysis suggest that the role of environmental regula-
tion on CO2 emissions mitigation is unclear.
Considering different pollution proxies for China, Wang, Yin, and
Chen (2019) investigated the causal relationship between environmen-
tal regulation and environmental pollution. They found that various
environmental policies influence pollutants differently. Furthermore, Li
and Ramanathan (2018) examined the impact of different types of envi-
ronmental regulations on environmental performance in China. The
authors concluded that the impact of command and control regulations
and marked-based regulations on environmental performance is
nonlinear but positive, but the role of informal regulations is silent. Yang
et al. (2018) used a different measure of environmental regulation to
check the consistencies of the pollution halo hypothesis in China. Their
findings are dramatic, and different measures lead to a heterogeneous
effect on the pollution haven hypothesis. Zhang (2019) adopted the
Bayesian posterior probability approach to examine the role if environ-
mental regulations in reducing environmental pollution for 30 Chinese
provinces during the period 2003–2016. The study found that environ-
mental regulations have a significant spillover effect, and they are help-
ful in controlling environmental pollution in China. But environmental
regulations coupled with industrial agglomeration worsen pollution.
The nexus between environmental regulations and CO2 emissions
endure a topic of research discussion, but the mechanism for the role
of environmental regulations on carbon emissions is still complex. Only
a few studies have analyzed the potential role of environmental regula-
tions on the environment by considering whether or not they influence
carbon emissions. Results from earlier work that focuses on these
interlinkages are rather inadequate and inconclusive. Also, such an
investigation has been ignored in the context of the BRICS countries.
Due to insufficient investigation between underlying variables, unclear
results, methodological drawbacks, and inadequate evidence in some
groups of countries motivate us to consider the causal linkage between
environmental regulations, income, and carbon emissions through
energy consumption and international trade. Table 1 presents a sum-
mary synthesis of the current literature. The table draws attention that
a great majority of studies employ panel data estimation techniques,
most probably due to their statistical power, for investigating the
impact of environmental policies on emission mitigation. However,
these studies employ traditional panel data estimation techniques that
do not consider the cross-sectional dependence problem. Also, a con-
siderable amount of studies in the literature focuses on how CO2 emis-
sions react to environmental policies in China, most probably due to
the fact that among BRICS countries, China is responsible for a huge
share of CO2 emissions in the world. However, results reveal that fur-
ther investigations should be required to reach a consensus in under-
standing the role of environmental policies on emission mitigation
clearly. One can realize that there is not any investigation for BRICS
countries that consume 40% of the world's energy and have a notable
share of the world's CO2 emissions. Different from the current litera-
ture, this study focus on how environmental policies are effective in
emission mitigation in BRICS countries by using second-generation
panel data techniques that takes the cross-sectional dependence into
account.
3 | MATERIALS AND METHODS
3.1 | Data
Time series data of five BRICS (Brazil, Russia, India, China, and
South Africa is used for a period spanning from 1995 to 2016. Carbon
emissions are being treated as a response variable, while income and
environmental regulations correspond to explanatory variables. Besides,
energy use and international trade are control variables. For variable
measure, income is represented by GDP per capita (constant 2010 US
$). CO2 emissions are employed as carbon emissions per capita mea-
sured in a million metric tons of oil equivalent. Energy consumption is
primary energy use prior to transformation to other end-use fuels, and
that is the accumulation of indigenous production plus imports and
stock changes, minus exports and fuels supplied to ships and aircraft
engaged in international transport. Energy use is calculated in kilograms
of oil equivalent per capita. The data on CO2 emissions is retrieved
from British petroleum (BP) Statistical Review of World Energy (BP,
2018). Per capita GDP and energy use are taken from world develop-
ment indicator (WDI), the database of the World Bank (WDI, 2018).
The international trade is made up of pooling the import and export as
a share of GDP. Environmental regulation is the key focus and the
study's core explanatory variable. The relevant data for measuring envi-
ronmental regulation is hard to retrieve due to relatively poor data qual-
ity. The website of Organization of Economic Co-operation
Development (OECD) divides environmental regulations into two parts,
environmental tax, and patents on environmental technologies. The
data on environmental tax is not available for study's sample countries,
at present this study relies on the patents for environmental technolo-
gies as a measure of environmental regulation. Patent data refers to
several attractive properties related to other substitute metrics of inno-
vation. They are widely available, quantitative, commensurable, and
output-oriented. International trade and environmental regulation data
can be collected from the OECD website (OECD, 2019).
DANISH ET AL. 815

4. 3.2 | Model specification
Environmental regulation may influence the environment either
directly or indirectly through the demand effect. Negative environ-
mental regulation measures may directly harm their courage to CO2
emissions reduction for governments, corporates, and residences.
Thereby, both enterprises and residents will not accommodate the
requirements of relevant environmental measures for the reduction of
CO2 emissions, ultimately carbon emission may increase. Concerning
indirect effect, environmental measures may prompt the economic
activities as such the stock market, investment and trade, and so on
and then may cause the energy demand. Due to lower economic
growth, governments may introduce policies to accelerate economic
growth and this may occur to environmental policy uncertainty, due
to negligence, carbon emissions may increase during the time (Jiang
et al., 2019). Environmental policy uncertainty or weak environmental
regulations attract carbon-intensive industries in the shape of FDI
from developed countries to developing countries where environmen-
tal policies are not stringent (Sarkodie Strezov, 2019a). On the other
hand, due to the rising economic growth rate, the imposition of envi-
ronmental regulations can offset any market failure that might cause
an increase in pollution and respond to public awareness of environ-
mental quality (Danish, Baloch, Bo, 2019). Technological advance-
ment measures and regulations in the energy sector are particularly an
appropriate way when addressing global climate change. Environmen-
tal regulations for restriction of the total energy consumption and sus-
tain energy efficiency are an effective instrument to control pollution
(Albulescu et al., 2019). In addition to being considered as a key tool
for controlling pollution, environmental regulations are regarded as a
significant factor for countries to cross the EKC threshold and accom-
plish win–win result in mitigation of CO2 emissions and economic
development (H. Wang Wei, 2019).
The empirical model of this study builds on recent studies of
Albulescu et al. (2019), Hashmi and Alam (2019), and Ouyang et al.
TABLE 1 Studies on environmental regulation and CO2 emissions
Authors Country Time Methods Key findings
Hao et al. (2018) 283 Chinese cities 2003–2010 GMM technique Environmental policies are not successful in achieving
pollution reduction targets.
Cheng et al. (2017) 30 Chinese
provinces
1997–2014 Dynamic spatial panel Control and command regulations measure were favorable
for CO2 emissions reduction.
Chen et al. (2018) 30 Chinese
provinces
1998–2012 GMM estimator Tighter environmental regulation would be helpful in
pollution reduction.
Pei et al. (2019) Chinese provinces 2005–2015 Panel OLS Environmental regulation directly inhibits CO2 emissions
and indirectly through technical efficiency as well.
Jiang et al. (2019) United States 1985–2017 Granger causality Uncertainty in economic policies affects CO2 emissions in
all sectors.
Hashmi and Alam
(2019)
OECD countries 1999–2014 GMM technique Positive role of environmental regulation in pollution
reduction.
Wenbo and Yan (2018) Chinese provinces 2004–2015 GMM estimation method Inverted U-shaped relationship between environmental
regulation and carbon emissions. At present efficiency of
environmental regulation reduce pollution.
Ouyang et al. (2019) OECD countries 1998–2015 Panel threshold Heterogenous effect of environmental policies on air
pollution because at first environmental regulation rises
PM2.5 but later the impact becomes insignificant.
Albulescu et al. (2019) EU countries 1990 and
2017
Dynamic and static GMM
estimator
Environmental regulation in CO2 emissions mitigation is
unclear.
K. Wang et al. (2019) China 2006–2014 Difference-in-differences Various environmental policies influence pollutants
differently.
Li and Ramanathan
(2018)
China 2004–2014 Panel OLS The impact of command and control regulation and
marked based regulation on environmental performance
is nonlinear but positive, but the role of informal
regulations is silent.
Yang et al. (2018) China 2006–2010 Conditional logit model The study's findings are dramatic, and different measures
lead to a heterogeneous effect on the pollution haven
hypothesis.
Zhang (2019) 30 Chinese
provinces
2003–2016 Bayesian posterior
probability approach
The study found that environmental regulation has significant
spillover effect and it is helpful to control environmental
pollution in China. But environmental regulation coupled
with industrial agglomeration worsen pollution.
Abbreviations: EU, European Union; GMM, generalized method of moment; OECD, Organization for Economic Co-operation and Development,
OLS, Ordinary least square.
816 DANISH ET AL.

5. (2019) incorporating international trade, energy consumption and a
more quality measure of environmental regulation (patents on envi-
ronment technologies) into the model construction. The functional
model form used in the study is presented below:
Ln CO2it
ð Þ = α + β1Ln Yit
ð Þ + β2Ln Yit
2
+ β3Ln ECit
ð Þ + β4Ln ERit
ð Þ
+ β5Ln ITit
ð Þ + μit ð1Þ
The log-linear specification in Equation (1) is necessary to avoid
scaling problems because it makes easy to interpret the estimated
coefficients which are calculated in elasticity form (Alola, Bekun,
Sarkodie, 2019).In Equation (1), α refers to constant term and β's
are partial slope parameters (coefficients) to be estimated. μi shows
the error term that captures the effect of all unobserved variables in
the estimated model. The subscripts t and i indicate the time dimen-
sion used in the study that is, 1995–2016 and cross-section dimen-
sion of five BRICS countries, respectively. Expected signs of β1 and β2
are positive and negative, respectively, and in this case, they will have
a positive and negative impact on CO2 emissions. This is attributed to
the well-known trade-off between income and pollution as the
inverted U-shaped curve or EKC hypothesis. The EKC hypothesis
states that income influence pollution through scale effect, composi-
tion effect and technique effect. During the scale effect, economic
development drives natural resource exploitation and industrialization,
thus to increase energy consumption and wastes, which pollute the
environment (Danish, Baloch, Mahmood, Zhang, 2019). The compo-
sition effect refers to structural change occurs when the economy is
shifting from the industrial sector toward the service sector, and dur-
ing this stage, pollution starts to decline. Finally, technique effect
refers to technological progress particularly in the energy sector and
for production. Renewable energy technology which brings renewable
energy to energy mix coupled with regulation measures, through the
technique effect, reduces CO2 emissions. The graphical presentation
of the EKC is shown in Figure 1.
β3 is expected to be positive for energy consumption. Since energy
use, especially based on fossil fuels, leads to increase CO2 emission,
the largest contributors of global anthropogenic GHGs. Energy is cru-
cial for socioeconomic activities worldwide, and access to energy
improves livelihood and wellbeing. However, there is strong evidence
in the literature that energy consumption is the main contributor to
carbon emissions. Hence, environmental pollution is decoupled from
the energy consumption-growth trajectory (Alola et al., 2019). Based
on the recommendation of the IPPC reports, energy continues to be
the foremost contributor to global anthropogenic greenhouse emis-
sions, due to its role in economic development. The projected increase
in an energy-dependent economy results in more energy-related pollu-
tion (Asumadu-Sarkodie Yadav, 2019). Based on this, energy con-
sumption is included in the model.
Trade is another important indicator of environmental pollution
widely discussed in the literature. We include international trade as a
control variable into the model in Equation (1) to avoid omitted variable
bias. International trade is also closely related to environmental regula-
tions. Environmental regulations may provide protections or intensives
for trade, and that affects the composition of an economy. Government
efficiency may generate trade openness through critical environmental
regulations which could expand foreign trade (Z. Wang et al., 2018).
Energy consumption may reduce due to efficient technology use which
is encouraged by trade openness (Sbia, Shahbaz, Hamdi, 2014), and
trade openness benefits the economy's sustainable development
through efficient resource use and improvements in economies of
scale. Also, the role of trade is important to transfer renewable energy
or clean technology (Semančíková, 2016;Alam Murad, 2020). On the
other hand, the scale effect refers to increases in international trade. In
other words, trade openness triggers economic activities, for instance,
by more transportation services and more production and consumption
of goods and services. So, trade may worsen environmental quality by
stimulating activities. Also, the technology transfer through trade
expansion may be energy-intensive which contributes to pollution
SCALE
EFFECT
COMPOSITION
AND
TECHNIQUE
EFFECT
TECHNIQUE
OBSOLESCENCE
EFFECT
HIGH
INCOME LEVELS
MIDDLE
INCOME LEVELS
LOW
INCOME LEVELS
N
O
I
T
A
D
A
R
G
E
D
L
A
T
N
E
M
N
O
R
I
V
N
E
FIGURE 1 Income-pollution relationship (Danish Wang, 2019a) [Colour figure can be viewed at wileyonlinelibrary.com]
DANISH ET AL. 817

6. (Mahmood, Wang, Yasmin, Manzoor, Rahman, 2019). Considering
the explanations that trade activities may have a twofold effect on the
environment, a positive or negative sign may be obtained for β5.
3.3 | Econometric approach
According to data of the study variables employed in the Equation (1),
time dimension is sufficiently enough to check cross-correlations among
countries and unit root/stationarity properties of each variable. Check-
ing dependencies across countries among the study's variables provide a
guideline in further econometric tests selection that is used in the empir-
ical analysis likewise unit roots and cointegration tests (Adjei et al.,
2019; Danish, Zhang, Hassan, Iqbal, 2019). The cross-sectional depen-
dency (CD) among the sections of the panel is checked by using a CD
test proposed by Pesaran (2004). Further, the stationarity of the vari-
ables is controlled by conducting the CIPS unit root test developed by
Pesaran (2007) that takes CD issue into account. Later, the long-run
relationship among study variables is investigated by Westerlund's
(2007) cointegration test that produces reliable outputs under heteroge-
neity and CD. Consistent with recent studies (Danish Wang, 2019c;
Sencer Atasoy, 2017), this study performs CCE mean group estimator
developed by Pesaran (2006) and later advanced by Kapetanios et al.
(2011). The selection of MG-CCE estimator is corroborated on the
assumptions of dependencies across countries and heterogeneity in the
data as well as its statistical power. Besides that, the CCE mean group
estimator counters structural breaks and nonstationary unobserved
common factors in the data. It produces a panel-level estimation mea-
sured as average of the single group-estimates. The estimation proce-
dure of CCE mean group in case of our model is presented as:
CO2i,t = α + δiXi,t + μ1
CO2t + μ1
Xt + μi,t ð2Þ
where X represents a matrix of explanatory variables.
CO2 and
X, the
cross-section panel averages of the dependent and the independent
variables are used as proxies for unobserved factors. The MG-CCE
yields consistent estimates, allows time-variant unobservable variables
with heterogeneous impact across panel members, and is the most
efficient in the presence of common factors (George Kapetanios
Pesaran, 2007). This method has the ability to counter issues of a lim-
ited number of “strong” unobserved common factors and an infinite
number of “weak” factors (El Anshasy Katsaiti, 2014). MG-CCE also
is found as one of the most robust methods in case the panel variables
are nonstationary (Coakley, Fuertes, Smith, 2006).
The mean group estimator for the CCE is obtained by calculating
the mean of each coefficient over each individual regression as stated
below:
CCEMG = N−1
X
N
i = 1
βi ð3Þ
where βi is the estimates of coefficients in Equation (1).
4 | RESULTS
The empirical section begins with the examination of cross-sectional
dependencies across countries by employing Pesaran's (2004) CD test
whose results can be seen in Table 2. Results confirm the rejection of
the null hypothesis of no-cross sectional dependence, implying that
there exist dependencies across countries. This means that a shock
occurs in one country has a spillover effect over sample countries in
the study (Danish Wang, 2019c). Based on the CD test results, the
next step is the investigation of the stationarity level of series. It is
worth mentioning here that the methods used for each step in the
study are robust against cross-sectional dependence to avoid incon-
sistencies. On this note, Pesaran's (2007) CIPS panel unit root test is
performed, which captures dependencies across countries. The CIPS
panel unit root test results are illustrated in Table 2. Results reveal
that series for each variable is not stationary at level I(0), but they
become stationary at first difference I(1). In other words, the series
are integrated at the first order.
The order of integration one that is, I(1) for data series call for
investigating cointegration relationship among study variables. To this
end, the Westerlund's (2007) cointegration test is applied and results
that suggest the rejection of the null hypothesis of “no cointegration
relationship” are presented in Table 3.
Having determined the cointegration relationship for the study var-
iables, the long-term coefficients are estimated through MG-CCE esti-
mator. Pooled mean group (PMG) and Mean group fully modified
ordinary least squares (MG-FMOLS) estimators are also used for
robustness checks. The results for all three estimation methods are
summarized in Table 4. The coefficient signs for income (Ln Y) and the
square of income (Ln Y2
) are positive and negative, respectively, and
they are statistically significant. These findings confirm a nonlinear rela-
tionship between income and pollution, which is an inverted U-shaped
curve claimed by the EKC hypothesis. Further, the coefficient of energy
consumption (Ln EC) is positive and statistically significant as is
TABLE 2 CD test and CIPS test results
Ln (CO2) Ln (Y) Ln (Y2
) Ln (ER) Ln (EC) Ln (IT)
CD test 10.01 (0.000) 14.34 (0.000) 14.32 (0.000) 2.96 (0.003) 9.16 (0.000) 5.35 (0.000)
CIPS panel unit root test results
CIPS (at level) −2.360 −1.757 −1.742 −3.820 −2.513 −1.908
CIPS (at first difference) −3.888 −3.041 −2.789 −5.713 −3.264 −3.248
818 DANISH ET AL.

7. expected and discussed in the previous section. The impact of environ-
mental regulations (Ln ER) is found negative and statistically significant.
Finally, the environmental impact of trade is reported positive and sta-
tistically significant. As can be followed from Table 4, the results of
PMG and MG-FMOLS is strongly consistent with MG-CCE results.
For more specific information about the sample countries, country-
wise estimations and coefficients are obtained through MG-FMOLS
estimator, and the results are reported in Table 5. The coefficients of (Ln
Y) and (Ln Y2
) are positive and negative, and this confirms the inverted
U-shaped curve between per capita income and pollution in BRICS
countries. The impact of energy consumption on carbon emissions is
positive and statistically significant in all BRICS countries. However, as is
expected, the coefficient of environmental regulations is found negative
and statistically significant for all countries. Finally, the response to emis-
sions by trade varies across countries. For Brazil and India, the coeffi-
cient of international trade concerning pollution is observed positive but
found negative for South Africa, while the impact of international trade
on carbon emissions is statistically insignificant for China and Russia.
5 | DISCUSSION
The empirical results highlight that there is an inverted U-shaped rela-
tionship between per capita GDP and pollution in BRICS countries.
This could be attributed to structural changes in the sample countries,
to similar to conclusions reached by Danish and Wang (2019a), Dan-
ish, Baloch, and Bo (2019) and Danish, Baloch, Mahmood, and Zhang
(2019) for BRICS in the presence of biomass energy, natural resource,
and governance. The findings are different in the sense that this study
uses more advanced methods for empirical estimations, and the EKC
is confirmed for BRICS in the presence of energy consumption and
environmental regulations, which are ignored in the literature. Energy
consumption increases carbon emissions and the finding of the nega-
tive role of energy consumption in environmental pollution (increasing
effect) is due to a large share of fossil fuels in the energy mix in BRICS
countries. As is verified by empirical findings of this study, govern-
ments in these BRICS countries should strengthen command control
environmental regulations which mainly reduce high energy consump-
tion and increase energy efficiency (Pan, Ai, Li, Pan, Yan, 2019). The
well-known negative role of energy consumption on the environment
is evident in a large number of studies, only a few studies are listed
here (see Danish, Wang, Wang, 2018; Sarkodie Strezov, 2019b;
Shabani, Shahnazi, Dehghan Shabani, Shahnazi, 2019).
The negative coefficient of environmental regulations means a
positive contribution to climate change mitigation. In other words,
environmental regulations are a fruitful tool for carbon emission
reduction in BRICS countries. Environmental regulations to ensure
environmental related patents can be properly applied (C. Cheng, Ren,
Wang, Yan, 2019). It is clear that environmental regulations signifi-
cantly promote the development of technological innovation (Guo,
Qu, Tseng, 2017) which increases energy efficiency and decreases
CO2 emissions. However, according to empirical results for the panel
sample in Table 4, GDP and energy consumption have higher coeffi-
cients than environmental regulations, and their additive effects on pol-
lution may dominate the negative effect of regulations. Based on this,
one might claim that the efficiency of environmental regulations toward
pollution reduction is lower. The concern for regulation efficiency can
be overcome via setting emission reduction targets. For instance, envi-
ronmental regulations in BRICS countries should be more strengthen
towards permission of technology transmission, to lower the patents
TABLE 5 Country-wise FMOLS results
Ln (Y) Ln (Y2
) Ln (EC) Ln (ER) Ln (IT)
Regressor Coefficient (Prob.) Coefficient (Prob.) Coefficient (Prob.) Coefficient (Prob.) Coefficient (Prob.) The EKC holds?
Brazil 0.3902a
(0.001) −0.0390a
(0.004) 1.2183a
(0.000) −0.0440a
(0.000) 0.0544a
(0.000) Yes
China 0.3537c
(0.061) −0.0247c
(0.079) 1.0509a
(0.000) −0.0737b
(0.021) −0.0708 (0.196) Yes
India 1.397a
(0.000) −0.170a
(0.000) 2.390a
(0.000) −0.099a
(0.000) 0.318a
(0.000) Yes
Russia 0.161a
(0.006) −0.016a
(0.000) 1.147a
(0.000) −0.113a
(0.000) 0.063 (0.100) Yes
South Africa 51.395a
(0.000) −2.896a
(0.000) 0.940a
(0.000) −0.076c
(0.064) −0.079a
(0.000) Yes
Note: a, b, c are the significance levels at 1, 5, and 10%, respectively.
TABLE 4 MG-CCE, PMG, and MG-FMOLS results
MG-CCE PMG-ARDL MG-FMOLS
Regressor Coefficient [Prob.] Coefficient [Prob.] Coefficient [Prob.
Ln (Y) 3.9691a
[0.000] 2.3928a
[0.000] 0.4414a
[0.001]
Ln (Y2
) −0.2386a
[0.000] −0.1344a
[0.000] −0.0232a
[0.002]
Ln (EC) 0.7905b
[0.023] 0.6777a
[0.000] 0.8676a
[0.000]
Ln (ER) −0.0393c
[0.052] −0.1782a
[0.000] −0.0226b
[0.025]
Ln (IT) 0.0577 [0.764] −0.1013 [0.738] 0.0407 [0.1205]
Note: a, b, c are the significance levels at 1, 5, and 10%, respectively.
TABLE 3 Westerlund panel
cointegration test results
Statistic Gt Ga Pt Pa
Value (P-value) −3.786a
[0.000] −8.452 [0.830] −8.640a
[0.000] −10.087 [0.282]
Note: a is the significance levels at 1%.
DANISH ET AL. 819

8. application fee and the intellectual property rights (Mensah, Long,
Boamah, Bediako, Dauda, 2018), together these can be useful to
increase the efficiency in mitigation efforts for pollution. In BRICS
countries, economic growth alone is not reducing pollution but coupled
with environmental regulations can reverse this outcome. It can be also
an important tool in emerging economies to maintain pollution reduc-
tion commitments to the Paris agreement (H. Wang Wei, 2019). The
BRICS countries are leading in technological knowledge transfer and
innovation. The results correspond to environment-friendly innovations
and carbon pricing strategies which are effective in carbon reduction
initiatives. An environmental regulation similar to carbon pricing also
reduces pollution (Hashmi Alam, 2019).
Environmental regulations have negative coefficients for all sam-
ple countries and reduce pollution in all BRICS countries. This means
that they positively influence the improvement of carbon reduction.
Hence, environmental regulations are critical due to linking emission
mitigation and technological innovation (C. Cheng et al., 2019). To
strengthen the environmental policy measure can help to quickly
achieve the optimum level of income after pollution starts to decline.
BRICS countries are urged to determine an optimal level of environ-
mental regulation. This finding is corroborated with a similar finding
obtained by Zhang (2019). However, Ouyang et al. (2019) and
Albulescu et al. (2019) found heterogeneous and nonlinear effects for
environmental policies concerning pollution reduction in OECD coun-
tries. Hao et al. (2018) found that environmental policies are not
effective in controlling pollution in city-level data analysis. Albulescu
et al. (2019) could not found clear evidence for the role of environ-
mental regulation in pollution reduction for EU countries even though
their study validates the EKC hypothesis.
Finally, the environmental impact of trade is insignificant for the
overall sample. The impact of trade on CO2 emissions is positive (nega-
tive coefficient) and significant in South Africa, whereas trade has a neg-
ative influence (positive coefficient) on CO2 emissions in Brazil and India.
Results are statistically insignificant for China and Russia. The countries
where trade reduces pollution, the plausible reason could be clean tech-
nology transfer through trade expansion. Another reason may be that
stringent environmental regulations restrict the import of dirty technolo-
gies, dirty imports goods, and the production of dirty export-oriented
goods (Grossman Krueger, 1991; Helpman, 1998). Besides, trade dete-
riorates environmental quality in Brazil and India, does not contribute to
sustainable development, because trade openness may invite energy-
intensive production and technology (Copeland Taylor, 1995; Rezza,
2013; Solarin, Al-mulali, Musah, Ozturk, 2017). It may refer to that
trade expansion in Brazil and India allows the use of machinery for goods
production which is not environmentally friendly. These results are simi-
lar to those obtained by Z. Wang et al. (2018) and Danish and Wang
(2019a) all of whom found similar results for BRICS countries.
6 | CONCLUSION
This study investiagted the relationship between environmental policy
measures, energy consumption, income, and carbon emissions for five
BRICS countries between 1995 and 2016. The sample countries in this
study were firstly analyzed as a whole panel, and then country-wise
coefficients were estimated . The findings of the study are summarized
as: environmental regulations are helpful in reducing pollution, and also
help to form an inverted U-shaped relationship between per capita
income and pollution. Energy consumption is found as a key compo-
nent in the model, and it is highly responsible for pollution in BRICS
countries. Besides, international trade contributes to pollution in Brazil
and India, whereas it reduces CO2 emissions in South Africa.
Considering the empirical findings which are related to environ-
mental regulations, it can be concluded that environmental regulations
can efficiently restraint environmental pollution, and they have a spill-
over effect on environmental pollution. In short, the barriers which
proscribe technologies which are not environmental friendly are the
core motive that may cause a positive effect of environmental-related
patents on CO2 emissions (Zhang, 2019). Environmental regulations
toward economic growth are effective in pollution reduction but not
efficient enough to balance the adverse effect of energy consumption
on pollution in BRICS countries. However, the energy and trade
effects in BRICS countries distant overtake the green innovation
effects and carbon pricing policy instruments. On the other hand, Eco-
nomic development alone cannot reduce pollution but required
energy regulation measures will be helpful for reducing pollution. The
result speaks volumes on policy strategies towards environmental pol-
lution reduction measures without comprising access to energy as
highlighted in the sustainable development goals.
In terms of trade, some policy implications are suggested based on
the estimation results of the study. The countries where trade reduces
pollution need to strengthen their environmental regulations as these
economies are experiencing rapid growth in foreign trade. Besides,
Brazil and India need to revise their environmental regulations for trade
activities, as trade brings polluted technologies and goods to these
countries, which can be restricted with effective and stringent environ-
mental regulations.
Although this study draws different perspectives from the current
literature in terms of the sample, methodology, study variables, and
analysis period, there are some restrictions that can be considered by
future studies. The study can be further extended on the relationship
between income, environmental regulation and CO2 emission for
other countries or groups. More factors such as institutions (Hassan,
Danish, Khan, Xia, Fatima, 2019), globalization, corruption, and so
forth, may be included in the estimation model which endeavors to
highlight the efforts needed to reduce environmental pollution. Also,
alternative pollution indicators or econometric techniques may be
employed by future studies to yield alternative implications.
ACKNOWLEDGMENTS
The authors would like to sincerely appreciate funding from
Researchers Supporting Project number (RSP-2019/58), King Saud
University, Riyadh, Saudi Arabia. Also, (corresponding author) and
(first author) are sincerely appreciating the Researchers Supporting
Project number (2016A0705055) and (299-GK19071), respectively,
from Guangdong University of foreign studies Guangzhou, China.
820 DANISH ET AL.

9. ORCID
Danish https://orcid.org/0000-0003-1046-1655
Recep Ulucak https://orcid.org/0000-0001-9938-0063
Muhammad Awais Baloch https://orcid.org/0000-0001-6274-9699
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How to cite this article: Danish, Ulucak R, Khan SU-D,
Baloch MA, Li N. Mitigation pathways toward sustainable
development: Is there any trade-off between environmental
regulation and carbon emissions reduction? Sustainable
Development. 2020;28:813–822. https://doi.org/10.1002/
sd.2032
822 DANISH ET AL.