Environmental Science and Pollution Research Dissipating environmental pollution in the BRICS economies: do urbanization, globalization, energy innovation, and financial development matter?

Abstract

The question of how Brazil, Russia, India, China, and South Africa (BRICS countries) can substantially dissipate environmental pollution (EVP) remains unsolved. In this regard, this research explores the dynamic association between energy consumption (EGC), economic expansion (EXP), globalization (GLO), energy innovation (ENI), urbanization (URB), financial development (FID), and environmental pollution (EVP) using panel data from 1990 to 2020. This study integrated the augmented mean group (AMG), common correlated effect means group estimator (CC-MG), and fully modified ordinary least square (FMOLS) model approach to estimate the long-run interaction among the series. The findings of this study reveal a positive and significant association between economic expansion, energy consumption, urbanization, financial development , and environmental pollution. In contrast, globalization and energy innovation extensively abate EVP in the BRICS economies. Moreover, the outcome of the Granger causality test indicates that energy consumption and energy innovation have a bidirectional association with EVP. The Granger causality test further revealed a unidirectional causality between globalization, urbanization, financial development, and environmental pollution. Finally, this research has implications for policymakers in the BRICS countries.

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Environmental Science and Pollution Research
https://doi.org/10.1007/s11356-022-21508-z
RESEARCH ARTICLE
Dissipating environmental pollution intheBRICS economies:
dourbanization, globalization, energy innovation, andfinancial
development matter?
AgyemangKwasiSampene1 · CaiLi2· FredrickOteng‑Agyeman1 · RobertBrenya2
Received: 31 January 2022 / Accepted: 12 June 2022
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022
Abstract
The question of how Brazil, Russia, India, China, and South Africa (BRICS countries) can substantially dissipate environ-
mental pollution (EVP) remains unsolved. In this regard, this research explores the dynamic association between energy
consumption (EGC), economic expansion (EXP), globalization (GLO), energy innovation (ENI), urbanization (URB),
financial development (FID), and environmental pollution (EVP) using panel data from 1990 to 2020. This study integrated
the augmented mean group (AMG), common correlated effect means group estimator (CC-MG), and fully modified ordinary
least square (FMOLS) model approach to estimate the long-run interaction among the series. The findings of this study reveal
a positive and significant association between economic expansion, energy consumption, urbanization, financial develop-
ment, and environmental pollution. In contrast, globalization and energy innovation extensively abate EVP in the BRICS
economies. Moreover, the outcome of the Granger causality test indicates that energy consumption and energy innovation
have a bidirectional association with EVP. The Granger causality test further revealed a unidirectional causality between
globalization, urbanization, financial development, and environmental pollution. Finally, this research has implications for
policymakers in the BRICS countries.
Keywords Environmental pollution· Energy consumption· Energy innovation· Financial development· Globalization·
Urbanization
Introduction
The landmark of joint institutions and countries purposely
mounting a formidable action to combat multi-dimensional
natural hazards that emanate as a result of climate change
is of great importance to policymakers. One essential com-
ponent contributing to the mammoth of global climate
change is environmental pollution (EVP). Research on
environmental pollution has risen in contemporary times
in emerging nations because of ecological degradation or
deterioration. The stakes were raised high when the COP26
nations reached an agreement at Glasgow during the United
Nations Framework Convention on Climate Change Confer-
ence (UNFCCC) (United Nations Climate Change 2021).
Several countries signed to collaborate to minimize emis-
sions from carbon dioxide (CO2) and ensure that the globe
continues to thrive in the coming decade, with an average
temperature rise of 1.5 °C. Governments are being chal-
lenged to accelerate their carbon reduction strategies and
align their national climate action plans to the Paris Agree-
ment (United Nations Climate Change 2021). As a result,
these global environmental policy interventions highlight
the role of every country, regardless of wealth level, in main-
taining its ecological features and minimizing its assigned
portions of total energy consumption (Nathaniel etal. 2021).
Responsible Editor: Eyup Dogan
* Agyemang Kwasi Sampene
akwasiagyemang91@gmail.com
Cai Li
gscaili@ujs.edu.cn
Fredrick Oteng-Agyeman
fredrickotengagyeman2@gmail.com
Robert Brenya
brenyarobert@yahoo.com
1 School ofManagement, Jiangsu University,
Zhenjiang212013, China
2 College ofEconomics andManagement, Nanjing
Agricultural University, Nanjing210095, China

Environmental Science and Pollution Research
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The essence of EVP mitigation and economic improvement
has been extensively indicated globally.
Yet, there is blur visibility of empirical research to pro-
mote clear understanding. Notwithstanding, human activi-
ties, including industrialization, deforestation, and the need
to meet the demand for food security due to the rise in the
global population, have been the causal factor that threatens
the global sustainability of humanity (Balint etal. 2017;
Osobajo etal. 2020). Furthermore, Baydoun and Aga (2021)
enunciated that energy use equally threatens human sus-
tainability. The ramification of energy on greenhouse gases
(GHGs), climate change, and environmental pollution can-
not be underestimated. Therefore, it is critical to provide
empirical evidence that can improve the knowledge about
environmental pollution and its effect such that policymak-
ers, the public, and various stakeholders can put forward
strategies and mechanisms that can help address these issues.
In this present research, we analyzed the nexus involving
energy consumption (EGC), economic expansion (EXP),
globalization (GLO), energy innovation (ENI), urbanization
(URB), financial development (FID), and environmental pol-
lution in the BRICS economies. The aim is to focus on the
BRICS countries (Brazil, Russia, India, China, and South
Africa) because the BRICS nations are expanding faster and
generating more EVP (Mohanty and Sethi 2021). Moreover,
the main issue for consideration is the amount of emission
that the BRICS countries emit every year is quite alarming.
BRICS countries are among the top 15 emitters of metric
tons of carbon dioxide equivalent (MtCO2) globally. Thus,
the BRICS countries contribute almost two-fifths of global
CO2 emissions (Liu etal. 2020). According to Wang etal.
(2016), there are enormous concerns about the BRICS coun-
tries’ environmental sustainability. In this regard, examining
the factors that increase CO2 emissions and achieving car-
bon neutrality are vital for the various governments of the
BRICS countries (Sampene etal. 2021).
Scholars such as Rahman and Zaman (2021) undertook
an empirical investigation on the effect of EGC, economic
expansion, and GLO on EVP in the BRICS economy. Their
findings reveal that these parameters have a long-term link
and that EGC significantly impacts EVP in these countries.
According to Sampene etal. (2021), the BRICS countries
have significantly impacted global economic expansion and
ecological diversity. Danish and Wang (2019) opined that
the enormous economic expansion of the BRICS countries
in recent times has led to 40% in terms of global EGC and
more incredible contributors to CO2 emissions. Likewise,
Liu etal. (2020) examined the outcome of actual output
and renewable EGC in the BRICS countries. Their find-
ings revealed no link between actual production and renew-
able energy use or real output and EVP in Brazil, China,
or India. According to their results, BRIC governments
should prioritize human capital and economic expansion
while restricting actual production and pollution. Dong
etal. (2017) research also indicated many issues about the
BRICS states’ environmental and societal tenacity had been
articulated.
Globalization (GLO) is a multifaceted process that
merges international markets and regions while accelerating
and simplifying commerce, investment, and spinoff conse-
quences of technology and science. GLO’s environmental
impact has frequently been considered unfavorable, and most
empirical research suggests that globalization, openness,
and trade agreements promote GHG emissions, resulting in
EVP (Lenz and Fajdetić 2021). Furthermore, due to GLO,
enterprises must adjust their structures to satisfy interna-
tional demand, requiring efficient resources that do not cause
EVP. GLO encourages technology transfer, which allows for
the unrestricted flow of products between countries while
also boosting the number of goods produced (Shahbaz etal.
2018a). Similarly, in recent times, urbanization (URB)
activities have also become a concern for policymakers,
government, and researchers because of their environmental
challenges. Wang etal. (2016) indicated that few empirical
literature works exist to analyze the relationship between
URB and EVP in the BRICS countries. Therefore, this study
provides empirical research to explore this relationship and
address the gap created in the literature regarding the BRICS
countries.
Energy innovation (ENI) is the essential variable that can
help address EVP. Baloch etal. (2021) indicated that, despite
the efficient utilization of conventional and renewable energy
at affordable prices, the advantages of ENI cannot be over-
looked. ENI enhances energy efficiency by improving the
production process and reducing EGC, decreasing EVP. Lin
and Zhu (2019) added that ENI helps upgrade and improve
the levels of renewable technology. Moreover, financial
development (FID) has been identified as an essential com-
ponent that impacts the level of EVP. For example, banks
and financial institutions lend money to organizations to
expand their business. This, in turn, leads to the productive
use of land and energy in the long run and the creation of
waste which can eventually lead to EVP. In the same way,
individuals can also borrow from banks, which increases
their consumption capability, leading to more repercus-
sions on the environment. However, the studies by Lv and
Li (2021) indicated that FID might encourage technological
innovation among businesses, reducing the use of energy
and decreasing EVP.
The studies reviewed so far have indicated from a differ-
ent perspective that energy consumption, economic expan-
sion, globalization, energy innovation, financial develop-
ment, and urbanization have diverse outcomes on EVP.
Hence, this current research seeks to examine and explore
these relevant questions. (1) What is the effect of EGC, EXP,
GLO, ENI, FID, and URB on the level of EVP in the BRICS

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countries? (2) What is the direction of causality between
these variables and environmental pollution?
Hence, the present research seeks to achieve the follow-
ing objectives:
(1) Estimate the effect of the variables mentioned earlier
on the level of EVP in the BRICS economies.
(2) Explore the direction of causality relationship among
the research variables.
(3) Provide policy recommendations for the BRICS econo-
mies based on the study’s empirical findings.
The motivation andcontribution
ofthestudy
Undoubtedly, a lot of research has been carried out on fac-
tors that affect EVP in the context of BRICS countries
using different econometric estimators (Chishti and Sinha
2022; Danish etal. 2019; Danish and Wang 2018; Dong
etal. 2017; Haseeb etal. 2019; Khattak etal. 2020; Pao and
Tsai 2010; Sinha etal. 2019). However, to the best of our
knowledge, few studies have incorporated energy innovation,
financial development, and globalization in their research
model from the perspective of the BRICS countries. In addi-
tion, most of the studies did not incorporate the Stochastic
Impacts by Regression on Population, Affluence, and Tech-
nology (STIRPAT) model in their investigations. Hence, in
this research, we examine the relationship among these vari-
ables within the STIRPAT framework.
This research makes enormous contributions to existing
knowledge on EVP in BIRCS economies in five ways: (1)
this research is intriguing to the extent that it expanded the
empirical analysis of globalization, financial development,
urbanization, and energy innovation in the STIRPAT theory
among the BRICS countries. (2) The research also adopted
the second-generation cross-sectional dependence test
(CSD) estimation methodologies (Breitung and Das 2005;
Pesaran 2007). These methodologies are essential models
that can help establish a long-run relationship among vari-
ables and across panel observation. These techniques also
help account for the CSD, robustness of slope heterogeneity,
and serial correlation. (3) This research further employed
modern techniques, which include the augmented mean
group (AMG), common correlated effect means group esti-
mator (CC-MG), and fully modified ordinary least square
(FMOLS) as proposed by Eberhardt and Bond (2009). (4)
The newest Dumitrescu–Hurlin (D-H) Granger causality
was employed to analyze the causality association among
the variables (Hurlin and Dumitrescu 2008). (5) We are
optimistic that the present study may assist governments,
institutions, policymakers, and organizations in pursuing
more practical, pragmatic, and proper initiatives related to
environmental safety in general and especially in the BRICS
countries.
Apart from the Introduction section, the rest of the paper
is arranged as follows: the second section covers the litera-
ture review; the third section focuses on the methodology,
data, and empirical model; the fourth section outlines the
empirical results and discussion; and the fifth section illus-
trates the conclusion and theoretical and policy implications.
Literature review
Energy consumption, economic expansion,
andenvironmental pollution nexus
The relationship between EGC, EXP, and EVP has been gen-
erally acknowledged in empirical literature in recent times
(Baydoun and Aga 2021). Energy is essential for the pro-
duction and manufacturing purposes, and it increases EVP.
In Brazil, Su etal. (2021) examined the connection among
EGC-EXP-EVP by employing the FMOLS technique. Their
results showed that an increase in EKC and EXP contributed
to EVP. Similarly, the studies by Adebayo etal. (2021a)
indicated an upsurge in EVP in South Korea due to a rise
in EGC and EXP. Using CO2 emission as a proxy for EVP,
exact researchers have proved a long-run unidirectional rela-
tionship among EGC-EXP-EVP (Ahmad and Du 2017; Isik
etal. 2018). However, some research reported a bidirectional
and short-run association between EGC-EXP-EVP (Jebli
and Belloumi 2017; Mirza and Kanwal 2017). Topcu and
Payne (2018) analyzed the association between trade and
EGC by employing heterogeneity and CSD in their study.
Their research showed that the impact of business activities
(trade) on EGC revealed an inverted U-shaped incidence
which portrays that the effect of EVP on EGC is higher than
EXP.
Moreover, in Nigeria, Tomiwa Sunday Adebayo etal.
(2021b) research concluded that EGC and EXP triggered
EVP. In the BRICS countries, Wu etal. (2015) showed that
EXP has a decreasing impact on EVP in Brazil and Russia
but has a rising effect in China, South Africa, and India.
Their study outcome further revealed that EVP would be
triggered in all the BRICS countries by increasing EGC.
Extant studies have demonstrated that energy consumption
leads to economic growth, which in the long run leads to
a rise in environmental pollution (Amin and Dogan 2021;
Dong etal. 2017; Kazemzadeh etal. 2021; Koengkan etal.
2020b; Kongkuah etal. 2021; Pao and Tsai 2010).
Urbanization andenvironmental pollution nexus
Urbanization can be described as a social phenomenon
where people relocate from rural to cities. The effect of the
URB process on EVP is a novel issue that more scholars

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are still investigating (Zhang etal. 2021). Moreover, it is
essential to understand how URB can affect EVP. Chen etal.
(2018) employed the stochastic impact by regression on the
population, affluence, and technology (STIRPAT), indicat-
ing the EKC with an inverted association between URB and
EVP. Similarly, Fan etal. (2019) also found that URB and
EGC affect EVP and social changes in the countries eman-
cipating through a critical transition. Their research further
noted that the association between URB and EVP is multi-
dimensional. While a nation enhances social conditions,
economic expansion and urbanization have environmentally
degradation effects. In addition, Musah etal. (2020) investi-
gated the association between URB-EVP for West African
countries. The results of their research indicated a significant
and positive effect of URB on EVP. However, Kongkuah
etal. (2021) concluded that URB has an insignificant asso-
ciation with EVP level in China.
Globalization andenvironmental pollution nexus
Globalization has provided an edge in meeting the demand
for renewable energy as specialized services to install tech-
nologies. More importantly, globalization facilitates the inte-
gration of the technologies into the firm’s existing energy
supply and production systems (Koengkan etal. 2020a,
c). Foreign corporations can now invest in host countries
as a result of globalization. Foreign enterprises provide
highly advanced technologies and innovative experience
to their production methods, which cuts energy consump-
tion and motivates domestic firms to replicate the foreign
firms’ energy-efficient production methods, lowering EVP
(Shahbaz etal. 2018b). For time series and panel data, some
research looked into the impact of the GLO on EVP. For
instance, in the Asia Pacific Economic Cooperation coun-
tries, Zaidi etal. (2019) research indicated that GLO miti-
gates EVP. Similarly, results from extant literature have sug-
gested that GLO can help control EVP (Pandey etal. 2020;
Rahman and Zaman 2021; Saud etal. 2019; Usman etal.
2022). In contrast, other studies have also shown that GLO
has an adverse impact on EVP (Danish and Wang 2018;
Shahbaz etal. 2017; Yang etal. 2021).
Energy innovation andenvironmental pollution nexus
In previous research, ENI has been well documented as an
essential contributor to reducing CO2 and GHG emissions
(Baloch etal. 2021). Álvarez-herránz etal. (2017) outlined
two stringent ways ENI can reduce EVP. First, ENI improves
energy efficiency by limiting the embodied energy per unit
of output; secondly, it reduces carbon emission due to per
energetic reduction in the unit. ENI is an effective and effi-
cient initiative to attain energy conservation and reduction
in EVP. Shahbaz etal. (2018b) investigated the role of ENI
in addressing EVP by employing the autoregressive distrib-
uted lag (ARDL) approach. The empirical outcome suggests
that ENI has an inverse association with EVP in France.
Mensah etal. (2019) also estimated the impact of ENI in
mitigating EVP in the OECD countries. Their findings sug-
gest that ENI helps reduce EVP. The studies carried out by
some researchers revealed that ENI does not cause EVP. For
instance, Álvarez-herránz etal. (2017) analyzed the nexus
between ENI and EVP for the panel of OECD nations and
reported an adverse effect of ENI on environmental pollu-
tion. In addition, Tobelmann andWendler (2020) found that
ENI did not reduce EVP, while general innovation activity
does not reduce emission. Some researchers concluded that
ENI is essential for EVP correction strategies (Balsalobre-
lorente etal. 2019; Usman etal. 2022).
Financial development andenvironmental pollution nexus
Advanced and sustainable financial development is essen-
tial for productive economic expansion. Hafeez et al.
(2019) believe that it is insightful to acknowledge that if
FID increases economic growth, this may have environ-
mental consequences. FID is a backbone of the innovation
mechanisms that help minimize costs and enhance energy
efficacy (Lin and Zhu 2019). However, the FID structure’s
critical function in promoting economic development can
expand energy usage, inadvertently causing EVP (Usman
etal. 2022). Literature examining the dynamic association
between FID and EVP comprises inconclusive, contrasting,
and mixed results. For instance, the research conducted by
Saud etal.(2019b) (2019a) examined the connection between
FID and EVP in the belt and road nations. The study utilized
the AMG estimator, and their outcome revealed that FID
adversely affects the quality of the environment. Another
research carried out by Odugbesan (2020) also confirmed
a long-run association between FID and EVP in Nigeria.
Similarly, Katircioglu etal. (2018) scrutinize the association
between FID and EVP by employing FMOLS with data set
from 1960 to 2014 in Turkey. The research indicated a posi-
tive relationship between FID and EVP.
Furthermore, in Thailand, Adebayo etal. (2021b)
employed the novel wavelet coherence and ARDL tech-
niques to analyze FID-EVP relations. The research outcome
discovered that an increase in FID did not substantially
impact EVP. Again, in the BRICS countries, Vadlamannati
etal. (2009) reported that a higher level of FID reduces EVP.
Their research suggested that financial openness and lib-
eration are essential for reducing carbon emissions in these
countries. In addition, Lv and Li (2021) researched the nexus
of FID and EVP in the BRICS economies. The study con-
cluded a positive nexus between FID and environmental pol-
lution. Thus, FID significantly contributes to EVP, which
aligned the presence of EKC in the BRICS countries.

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Methodology, data, andempirical model
Data source andunit ofmeasurement
BRICS countries were selected for the present study for
the following reasons: (1) BRICS countries’ economic
expansion and environmental resilience concerns have
been expressed by various stakeholders and governments.
Coordinating the interaction of the environment and
economy and minimizing the global warming impact are
crucial to international sustainable development for the
BRICS countries (Dong etal. 2017; Wang etal. 2016).
(2) The BRICS countries have experienced a rapid rise
in globalization, energy consumption, urbanization, and
financial development over the last decades. In addition,
the study analyzed temporal series between 1990 and 2020
because of the availability of data.
CO2 emissions are utilized as a surrogate for EVP, the
dependent variable in this study. More specifically, we
used CO2 as a proxy to measure environmental pollution
in this research. We measured CO2 in kilotonne, and it
encompasses per capita metric tons of emissions per per-
son by a particular country at a specific point in time.
Various studies have used this variable in their research
work (Lin and Zhu 2019; Tian etal. 2020).
Energy consumption (EGC) research measures energy
use through oil and coal fossils. The unit of measurement for
energy consumption was in kg of oil equivalent per capita;
thus, EGC also measures the energy consumption per head
of the selected country’s population. EGC has been used in
research conducted by Baydoun and Aga (2021) and Rahman
and Zaman (2021). EXP measured economic growth as the
annual economic performance of the selected countries from
1 year to another. GLO is measured in terms of the social-eco-
nomic aspect of international influence on other countries in
this research. URB determined the percentage of a country’s
total population living in its urban core. The patent on energy-
related invention was employed to measure energy innovation
(ENI). The measurement of FID is based on a comparative
analysis of access, complexity, economic development, and
the efficiency of institutions. FID ranges between 0 and 1, but
for compatibility purposes with other study variables, we con-
verted these values between 0 and 100 (Usman etal. 2022).
The EVP, EGC, EXP, and URB data were derived from
WID (2022), and ENI data was extracted from OECD
(2022). The FID data comes from IMF (2022). GLO data
was gathered from the KOF globalization index (Dreher
(2006)). The description of variables, unit measurement, and
source of data for all the selected variables are indicated in
Table1.
Descriptive statistical information
Table2 indicates the statistical descriptive profile of
the BRICS economies for the study period from 1990 to
2020. The mean values for the selected variable are InEVP
(6.370), InEGC (5.486), InEXP (1.225), In GLO (4.035),
In URB (3.980) InENI (2.144), and InFID (3.788). Fur-
thermore, the series analyzed indicates that most possess
a comparatively high standard deviation. More specifically,
the average values of the series are InEVP (0.768), InEGC
(0.615), InEXP (0.954), InGLO (0.202), In URB (0.401)
InENI (0.355), and InFID (0.266). Statistics values such as
kurtosis, Jarque–Bera, and probability tests analyzed the
data structure’s normality. The value of these statistics in
Table2 indicates that the data is not normally distributed.
This confirmation implies some degree of heterogeneity
within the data set. Table2 further provides the correlation
matrix assessment information, which shows that all series
are modestly associated with environmental pollution. The
comparison of trend analysis of all the series is indicated in
Figs.1, 2, 3, 4, 5, 6 and 7.
Theoretical underpinning
The researchers adopted the IPAT approach (I = PAT) devel-
oped by Ehrlich and Holdren (1971). The model focuses
on three main variables influencing pollution in the envi-
ronment. Thus, environmental pollution (I) is influenced by
population (P), affluence (A), and technology impact (T). To
identify the interaction among the variable in this model on
the EVP is to alter one of the variables and keep the others
Table 1 Description of
variables Variable Symbol Description Source
Environmental pollution EVP CO2 emissions in kilotonne (kt) WDI
Energy consumption EGC Energy usage (kg of oil equivalent per capita) WDI
Economic expansion EXP Per capita (constant USD $2,010) WDI
Globalization GLO Social, economic, and political aspects of GLO KOF
Urbanization URB Urban population (% of total population) WDI
Energy innovation ENI Patents on environmental-related technologies OECD
Financial development FID Financial development index (0–100) IMF

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constant. York etal. (2003) developed the extended model of
the IPAT to STIRPAT (Stochastic Impacts by Regression on
Population, Affluence, and Technology). These two models
have been used widely in environmental pollution research.
STIRPAT is a new breed of IPAT, which assumes that a
“variety of variables affect environmental pollution in the
same proportion” (Ehrlich and Holdren 1971; Zhang 2021).
The mathematical function is derived as
Table 2 Synopsis of descriptive statistical information and correlation matrix of the series
Descriptive Stats
InEVPInEGC In EXPInGLO In URBInENI InFID
Mean 6.370 5.486 1.225 4.035 3.980 2.144 3.788
Std. Dev. 0.768 0.615 0.954 0.202 0.401 0.355 0.266
Min. Value 5.268 4.477 -1.880 3.458 3.240 1.000 25.468
Max. Value 7.749 6.779 2.655 4.280 4.467 2.820 30.318
Skewness 0.436 0.591 -0.775 0.015 -0.574 -0.984 0.866
Kurtosis 1.797 1.673 10.043 2.960 1.524 3.918 3.820
Jarque–Bera 9.338*** 16.301*** 426.939***28.240*** 14.063*** 30.488**23.741**
P-Value 0.009 0.000 0.000 0.000 0.000 0.000 0.005
Observation 155 155 155 155 155 155 155
Correlation
Matrix
InEVP1
InEGC 0.352 1
InEXP 0.157 0.277 1
InGLO-0.013 -0.021 0.351 1
InURB 0.139 0.094 0.720 0.421 1
InENI-0.024 0.079 -0.194 -0.352 0.101 1
InFID 0.148 0.303 0.309 0.891 0.016 0.402 1
-20
-15
-10
-5
0
5
10
15
20
1990 1995 2000 2005 2010 2015 2020
Brazil IndiaRussia ChinaSouth Africa
Fig. 1 Trend comparison of BRICS countries’ economic expansion from 1990 to 2020

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In Eq. (1), I represents environmental pollution, popu-
lation (P), affluence (A), and technological Impact (T).
The terms b, c, and d were included in the IPAT equation
to eliminate proportionality constraints in the approach.
α is the constant in the model, and μ denotes the error
(1)
Iit
=𝛼P
b
it
×A
c
it
×T
d
it
×𝜇
it
terms. i represents the individual units (countries), and
t is the time dimensions. Based on the studies by Koçak
and Ulucak (2019), we can formulate Eq. (2) as
We, therefore, revised Eq. (2) to demonstrate the impact of
EGC, EXP, GLO, URB, ENI, and FID on EVP.
(2)
InIit =𝛼i+bInPit +cInAit +dInTit +𝜇it
0.0
10.0
20.0
30.0
40.0
50.0
60.0
70.0
80.0
90.0
100.0
1990 1995 200020052010201
52
020
Brazil ChinaIndiaRussia South Africa
Fig. 2 Trend comparison of BRICS countries’ urbanization from 1990 to 2020
0
50
100
150
200
250
300
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
BrazilChina India Russia South Africa
Fig. 3 Trend comparison of BRICS countries’ financial development from 1990 to 2020

Environmental Science and Pollution Research
1 3
In Equation (3), InEVP (environmental pollution), InEGC
(energy consumption), InEXP (economic growth), In(GLO)
globalization, InURB (urbanization), In(ENI) energy inno-
vation (ENI), and financial development In(FID) represent
(3)
InEVP
it =𝛼i+
bInEGC
it +
cInEXP
it +
dInGLO
it +
eInURBit
+
fInENIit
+
gInFIDit
+
𝜇it
their natural logarithms forms. The terms a–g represent
the parameters for elasticity to be estimated in the model,
α is the constant in the model, μ denotes the error terms, i
represents the individual units (countries), and t is the time
dimensions. It is important to note that this investigation did
not include dummy variables. This is because it is antici-
pated that behavior does not change over time.
0
50
100
150
200
250
300
350
400
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
BrazilChinaIndiaRussia South Africa
Fig. 4 Trend comparison of BRICS countries’ globalization index from 1990 to 2020
0
1000
2000
3000
4000
5000
6000
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
BrazilChina IndiaRussiaSouth Africa
Fig. 5 Trend comparison of BRICS countries’ energy consumption from 1990 to 2020

Environmental Science and Pollution Research
1 3
Econometrics estimation strategy
Cross‑sectional dependency test
Cross-sectional dependency (CSD) is touted as a challenge
in panel data estimation. The presence of CSD can be the
avenue for inefficiency and inconsistency in parameters
evaluated. Thus, these challenges may occur due to dif-
ferent factors such as standard shocks, spatial effects, and
unobserved country-specific components (Mohanty and
Sethi 2021). Thus, testing the CSD among the variables is
important because it helps overcome inconsistent results
0
10
20
30
40
50
60
70
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
BrazilChinaIndiaRussia South Africa
Fig. 6 Trend comparison of BRICS countries’ energy innovation from 1990 to 2020
0
1000
2000
3000
4000
5000
6000
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
BrazilChinaIndiaRussia South Africa
Fig. 7 Trend comparison of BRICS countries’ carbon emissions from 1990 to 2020

Environmental Science and Pollution Research
1 3
and bias (Mohanty and Sethi 2021). Therefore, in this
research, we applied the following CSD test: Breusch and
Pagan (1980) and Chudik and Pesaran (2015). The math-
ematical representation of Chudik and Pesaran (2015) is
shown in Eq.(4):
where T indicates the time, N represents the CSD in the
panel, and ∂ij denotes the correlation coefficient of i and m
units.
Panel series stationarity test
We used the second-generation panel root test, i.e.,
cross-sectionally augmented Dickey–Fuller (CADF)
and augmented cross-sectional I’m Pesaran and Shin
(CIPS) Pesaran (2007) for the analysis of the unit root
test on the selected variable. The CADF and CIPS test
assists in addressing the issues of CSD and also tack-
les spurious in analyzing regression results. Moreover,
both stationarity tests helped the researchers examine
the robustness and accuracy of the series heterogene-
ity. The mathematical expression for the CADF test is
described in Eq. (5):
where xit indicates the variables analyzed in the study, ∆
represents the difference in the variables, and μit shows the
white error term. The Akaike information criteria (AIC)
selected the appropriate optimal lag lengths.
The CIPS test is expressed in a mathematical form as in
Eq. (6):
where the parameter φi(N, T) indicates CADF regression test
statistics.
Panel cointegration test
We employed two approaches to analyze the long-run
association among the variables. First is the Pedroni
(2004) cointegration approach, which explores the coin-
tegration association between the series by examining if
the residual value component of the equation is steady.
The null hypothesis(H0) of this technique is that there
exists no cointegration in the series. Pedroni (2004) coin-
tegration test is expressed mathematically as in Eq. (7):
(4)
CSD√
2T
N(N−1)
N
∑
i=1
𝜑i
N
∑
m=i+1
𝜕
im
(5)
Δ
xit =𝛼it +𝛽it−1+𝛿IT+
N
∑
j=
1
𝛾ijΔxit −j+𝜇
it
(6)
CIPS
=1
N
N
∑
i=1
𝜑i(N,T
)
where αi indicates the specific individual effect, ψit shows
the series trend, and n represents the explanatory variables.
Second is the Westerlund (2007) cointegration approach,
which analyzes our series CSD and heterogeneity. The null
hypothesis(H0) of this technique indicates that there exists
no cointegration in the error correction term among the
series. The model is mathematically expressed as in Eq.
(8):
where dt = (1, t)′ provides the series trend, elasticity estimates
𝜓
�i
=𝜓�i
and 𝜓2i
)�
indicate the constant term for all coun-
tries series, and i and t indicate all the CSD and period of the
study. The test statistics of the two categories of this approach
are expressed mathematically as in Eqs. (9)–(12):
The panel cointegration approach statistics is math-
ematically estimated as
whereGτ andGτ show the group mean statistics,PτandPa
indicate the panel statistics, and
𝜂 i
indicates the transition
from short-run to long-run equilibrium in terms of speed.
Long-run estimation models
We used the following econometrics methodologies
after demonstrating the presence of long-run correlation
among the variables: the augmented mean group (AMG),
common correlated effect means group estimator (CC-
MG), and fully modified ordinary least square (FMOLS)
(Eberhardt and Bond 2009; Pesaran 2006). The AMG
model helps estimate the coefficient of the slope heteroge-
neity across cross-sections, generating the specific group
information or estimation. The AMG technique is based
on a two-stage approach and is mathematically expressed
as in Eqs. (13)–(14):
Stage one AMG technique:
(7)
Y
it =𝛼i+𝜓it+
∑N
n=0
𝛽ni,Xnit +𝜇
it
(8)
Δ
Yit =𝜓�idt+𝛼i(Yit−1−𝛽�iXit−1)+∑
p
i
j=1𝛼ijΔyit−
j
+∑pi
j
=−
pi
𝜙ijΔXi,t−j+𝜇
it
(9)
G
𝜏
1
N
N
∑
i=1
𝜂i
S.E
(
𝜂
i)
(10)
G
a=
1
N
N
�
i=1
T𝜂i
1−
∑
k
j
=1𝜂 ij
(11)
P
𝜏
𝜂
i
S.E
(

𝜂i
)
(12)
Pa=T𝜂i

Environmental Science and Pollution Research
1 3
Stage two of the AMG technique:
where φi represents the intercept, Yit and Xit indicate the
observed variable, ϑt denotes heterogenous variables with
unobserved common factors, ∆ indicates the initial opera-
tor of the variables, time dimension t, and μit represents the
model’s stochastic error term.
The next model estimator we employed is the CC-MG
approach, as it is consistent and reliable in estimation. The
CC-MG considers the serial correlation among the series,
the robustness of a non-cointegrated structural flaw, and
unexplained common elements (Kapetanios etal. 2011). The
CC-MG is expressed mathematically in Eq. (15):
where Yit and Xit are indicated as observed variables, α1i
indicate the specific group effect, βi represents the cross-
section estimators’ slope, ni shows the unknown common
factor with loading with θi heterogenous, and μit exhibits
the model’s stochastic error term. The augmented model
with a mean cross-section of the explained and unexplained
variables can be expressed as in Eq. (16):
This regression was calculated using the ordinary least
square technique for each cross-section. To estimate the coun-
try-wise coefficient estimators, Equation17 offers a robust
outcome, and it is mathematically expressed as in Eq. (17):
Finally, we used the fully modified ordinary least square
(FMOLS) procedure. Pedroni (2004) introduced these techniques
to solve heteroscedasticity, endogeneity, and serial correlation.
The FMOLS model applies to this research because it is robust
and helps derive unbiased and accurate estimates. The FMOLS
model is mathematically expressed in Eq. (18) as follows:
Causality analysis
The research employed the modern Granger causality test Hur-
lin and Dumitrescu (2008) to analyze the causality relationship
(13)
Δ
Yit =𝜑i+𝛿i𝛿Xit +𝛾i𝜗t+
T
∑
t=2
𝜃iΔDt+𝜇
it
(14)
AMG
Estimator =N−1
N
∑
i=1

𝛽
i
(15)
Yit =𝛼1i+𝛽iXit +𝜃init +𝜇it
(16)
Yit =𝛼1i+𝛽iZit +𝜑iyit +zit +𝜃init +𝜇it
(17)
CC
−MG =N−1
N
∑
i=1

𝜃
i
(18)
FMOLS
Estimator =N−1
N

i=1

T

t=1

Zi,t−Zi

2

−1

T

t=1

Zi,t−Zi


EVPit −
T𝛽i

from one series to another. This approach helps address the
possibility of CSD and whether there is slope variability in
our model. The Dumitrescu–Hurlin (D-H) test analyzes the
causal relationships between time series data (Lopez and
Weber 2017). The null hypothesis of the D-H Granger cau-
sality test is that the variables have no causal relationship. In
contrast, the alternative hypothesis is a causal relationship in
the model. The D-H non-causality test is expressed mathemati-
cally in Eq. (19):
where m indicates the length of the lag and
𝜓m
i
shows the
autoregressive parameters of the model.
Empirical results anddiscussion
Cross‑sectional dependence test results
This section examined the econometric model by first deter-
mining the CSD of the study’s variables. Various literary
works have indicated that ignoring the CSD test can result in
inefficiency, inconsistency, unreliability, and bias that result
in misleading information (Ibrahim and Ajide 2021; Osobajo
etal. 2020; Usman etal. 2022). Therefore, to overcome the
challenges of CSD in our research, we employ four techniques
of CSD test in our data set. Table3 indicates the synopsis of
the four CSD used in this research. The test results confirm the
null hypothesis’s rejection among the variables’ cross-section
at a significance level of 1%. Therefore, we can conclude that
the BRICS countries are interconnected in terms of externali-
ties, technological development, globalization, and financial
development (Usman and Makhdum 2021).
Furthermore, Table3 indicates the heterogeneity slope as
suggested by the Pesaran (1999,2007) test. From a theoretical
perspective, the presence of CSD combined with the slope of
heterogeneity of the coefficient indicates that the first genera-
tional unit root test cannot be applied in our research model.
We, therefore, utilized the second-generation panel unit root
test in this study.
Panel stationarity test
After checking the possibility of CSD among the series, the
research further tested the stationarity level and the order
of integration of the variables. We employed two second-
generation unit root tests, thus CADF and CIPS. Table4
indicates that all the series (InEVP, InEGC, InEXP, InGLO,
InURB, InENI, and InFID) were not stationary. Neverthe-
less, after the first difference I(1), all the candidates’ series
transformed to stationary. This technique demonstrates that
we can investigate if the series chosen for this study have
long-run cointegration.
(19)
Y
it =𝛼i+
∑M
m=1
𝜓m
i
,Yi(m−t)+
∑M
m=1
𝜆m
i
,Zi(m−t
)

Environmental Science and Pollution Research
1 3
Pedroni cointegration test
Table5 shows the Pedroni (2004) panel cointegration test.
The test revealed that seven of the eleven test outcomes
reject the null hypothesis, indicating no cointegration
among the candidate series. As a result, most Pedroni
cointegration tests show that EVP, EGC, EXP, GLO, URB,
ENI, and FID have a long-run connection. These findings
show that the variables in our research model have a coin-
tegration interaction.
Westerlund panel cointegration test
The Westerlund (2007) cointegration test outcome is
indicated in Table6. The Westerlund (2007) cointegra-
tion technique uses two categories and two-panel statistics
with the value of their probabilities. Table6 demonstrates
that both categories (Gτ and Ga) are significant, show-
ing a highly significant level of 1%. The outcome of the
Westerlund cointegration indicated the rejection of the null
hypothesis of no cointegration. This outcome suggests a
long-run cointegration among the candidates of variables
selected in the BRICS nations.
Long‑run elasticity estimates
This section estimates the magnitude of the long-run
association between the series by employing three novel
approaches, AMG, CC-MG, and FMOLS, as indicated in
Table7. These three estimation techniques provide the same
sign of the coefficient; nevertheless, there may be a differ-
ence in their magnitude and significance level. We selected
these three estimation approaches because they provide
efficient, reliable, and consistent outcomes in the pres-
ence of CSD and heterogeneity of slope. Also, the FMOLS
technique provides robustness checks for our estimation
Table 3 Synopsis of CSD test
results
*** denotes significance level at 1%
Series Breusch–Pagan LM Pesaran scaled LM Bias-corrected
scaled LM
Pesaran CD
InEVP 194.703*** 41.301*** 41.217*** 7.147***
InEGC 172.964*** 36.439*** 36.356 *** 10.071***
InEXP 51.639*** 9.311*** 9.227*** 6.213***
InGLO 56.328*** 10.359*** 10.276*** 7.006***
InURB 272.398*** 58.674*** 58.591*** 4.601***
InENI 264.97*** 57.015*** 56.931*** 16.211***
InFID 261.702*** 56.282*** 56.199*** 16.164***
Table 4 Synopsis of second-generation panel unit root tests
**** denotes significance level at 1%
CADF CIPS Order of integration
Series Level First difference Level First difference I(1)
InEVP −1.512 −5.882*** 1.282 4.754*** I(1)
InEGC 1.369 −9.121*** 3.320 −6.787*** I(1)
InEXP 1.154 −5.615*** −0.042 −10.059*** I(1)
InGLO −1.025 −8.781*** −3.587 −11.763*** I(1)
InURB 0.007 −3.132*** −0.731 −2.421*** I(1)
InENI 2.165 −4.126*** 2.054 −8.686*** I(1)
InFID 1.921 −5.047*** −0.269 −4.498*** I(1)
Table 5 Synopsis of Pedroni
cointegration test
*** denotes significance level at 1%
Within dimensions Stats Prob Between dimensions Stats Prob
Panel v-statistic −0.176 0.569 Group rho-statistic 0.484 0.807
Panel rho-statistic 0.925 0.823 Group PP-statistic −4.009*** 0.000
Panel PP-statistic −3.160*** 0.000 Group ADF-statistic −8.522*** 0.000
Panel ADF-statistic −2.568*** 0.000
Weighted panel v-statistics −3.763*** 0.001
Weighted panel rho-statistic 0.784 0.962
Weighted panel PP-statistic −3.051*** 0.003
Weighted panel ADF-statistic −3.272**** 0.001

Environmental Science and Pollution Research
1 3
(Eberhardt and Bond 2009; Pesaran 2006). The analysis
of the long-run association indicates that each variable
reflected in this model affects EVP in the BRICS countries.
As shown in Table7, the empirical findings from the AMG
estimate show that energy consumption positively and signifi-
cantly impacts the level of EVP in the BRICS economies. As
a result, our findings imply that a 1% increase in energy con-
sumption will result in a 0.278% rise in EVP. This result is not
surprising given that extant research has shown that energy
consumption triggers EVP. For instance, a study conducted
by Liu etal. (2020) indicated energy consumption is substan-
tially associated with environmental pollution in the BRICS
countries. According to their findings, a smart energy policy
can help minimize EVP without forfeiting real output. The
empirical results confirmed these studies, which also reported
a positive association between EGC and EVP in the BRICS
countries (Dong etal. 2017; Li etal. 2020; Mohanty and Sethi
2021). However, our research outcome contradicts these stud-
ies (Baydoun and Aga 2021; Tufail etal. 2021).
The outcome of this study pointed out that energy con-
sumption triggers economic growth, thereby increasing envi-
ronmental pollution in the BRICS economies. The findings
from the AMG approach indicate a positive and significant
impact of economic expansion on environmental pollution.
The implication is that a 1% influence on the economic
expansion will increase 0.302% EVP in the BRICS regions.
The possible reason behind this outcome is that economic
expansion increases energy consumption, stimulating EVP
in the BRICS countries. More importantly, the empirical
outcome of this research indicates the presence of the EKC
curve in the selected countries. Our study suggests that the
EKC curve hypothesis exists an inverted U-shaped associa-
tion between EVP and EXP in the BRICS countries. The
presence of the EKC curve indicates that early-stage EXP
raises EVP, and the curve begins to drop after reaching a
specific threshold (Ahmad etal. 2020). This outcome sup-
ports Gulf Cooperation Council (GCC) countries Baydoun
and Aga (2021), BRICS countries (Haseeb etal. 2019; Ibra-
him and Ajide 2021; Usman and Makhdum 2021), and Asia
Pacific countries (Adebayo etal. 2021b).
The empirical findings from the AMG technique indicate
an inverse and significant relationship between globalization
and environmental pollution, as shown in Table7. The elas-
ticity of GLO concerning EVP demonstrates that a 1% influ-
ence in GLO activities mitigates EVP by 0.702% in the long
run if all other things remain constant. In the BRICS coun-
tries, a greater GLO level also clearly indicates less EVP.
The reason behind these results is that the selected countries,
through trade openness and foreign direct investments, can
allocate some of their economic initiatives to other nations,
reducing their economic activities’ stress on the environment
(Usman etal. 2022). Rahman and Zaman (2021) believe that
the conceivable reason for these outcomes may be that these
nations have access to higher technological tools because of
globalization. The result of our study is in line with Rahman
and Zaman (2021), Saud etal. (2019), Sinha etal. (2019),
and Usman etal. (2022) but in contrast with Shahbaz etal.
(2017) and Yang etal. (2021).
It is equally essential to analyze the impact of urbani-
zation on environmental pollution. The current study’s
empirical findings from the AMG methodology suggest that
urbanization positively affects EVP in the BRICS countries.
Hence, our findings imply that a 1% increase in URB will
result in a 4.996% rise in EVP. Urbanization in the BRICS
countries has been an uprising in recent times, causing more
EVP than other economies. The upsurge in URB in these
nations indicates an increase in the economic activities in
these regions. This discovery is not entirely surprising since
many people in the BRICS countries migrate to the urban
centers to search for better living conditions, employment
opportunities, and better infrastructure, to mention a few.
These activities are achieved mainly by consuming energy,
natural gas, and coal. In addition, automobile emissions are
Table 6 Synopsis of Westerlund cointegration test
*** denotes significance level at 1%
Statistics GτGaPτPa
Values −4.421*** −8.327*** −12.341*** −11.381***
Z values −1.542 2.249 −4.331 1.423
P values 0.063 0.883 0.000 0.804
Robust P
values
0.030 0.020 0.000 0.101
Table 7 Findings of the overall
panel of long-run elasticity
estimates
*** denotes significance level at 1%. The symbols show [ ] (standard errors) and probability ( )
Variables AMG CC-MG FMOLS
InEGC 0.278*** [0.103] (0.000) 0.107*** [0.049] (0.000) 1.016*** [0.119] 0.000
InEXP 0.302*** [0.009] (0.001) 0.106** [0.329] (0.001) 0.048*** [0.013] 0.000
InGLO −0.720*** [0.899] (0.000) −0.336*** [0.339] (0.001) −1.453* [0.195] 0.000
InURB 4.996** [1.637] (0.000) 1.877*** [0.112] (0.000) 0.644* [0.124] 0.021
InENI −0.640* [0.024] (0.005) −0.826* [0.821] (0.013) −0.011 [0.033] 0.731
InFID 0.210*** [0.074] (0.000) 0.592*** [0.113] (0.000) 0.350*** [0.074] 0.000

Environmental Science and Pollution Research
1 3
a substantial source of pollution in large cities. Musah etal.
(2020) assert that urbanization leads to high-energy equip-
ment like heaters, microwaves, air conditioning, and refrig-
erators, increasing the EVP rate in these countries. Also,
Chishti and Sinha (2022) reported that the URB system has
an inverse relationship with EVP in the BRICS countries.
The outcome of our research supports Anwar etal. (2020),
Lv etal. (2019), and Musah etal. (2020). However, our study
outcome contrasts Lin etal. (2017) and Rafiq etal. (2016).
Moreover, the empirical analysis from the AMG estimate
indicates the effect of ENI on EVP is negative but insignifi-
cant. Therefore, the findings imply that a 1% increase in ENI
will result in a 0.640% decrease in EVP. The economic intui-
tion behind this result is that less negative environmental
impact emanates from the inverse shock of energy innova-
tion in dissipating EVP. Chishti and Sinha (2022) indicated
that the effect of technology and innovation on environmen-
tal impacts takes a lot of time to correct itself. This result
suggests that the nature of ENI strategies adopted by the
BRICS economies has lower possibilities of dissipating
EVP. This might be the intuition behind why ENI is negative
but insignificant. The negative relationship between energy
innovation and environmental pollution is justifiable because
ENI is a significant initiative to consider and implement sus-
tainable development in the BRICS countries. In addition,
ENI helps promote carbon neutrality, a low-carbon environ-
ment, and achieving energy efficiency. Suki etal. (2022)
reported in their research that energy innovation helps in
mitigating both ecological footprint and EVP in Malay-
sia. The result of this study is consistent with Ahmad etal.
(2020), Mensah etal. (2019), and Rafiq etal. (2016). How-
ever, it contradicts the outcome of Cho and Sohn (2018),
who found out that ENI stimulates environmental pollution
in the BRICS countries.
Lastly, the result from the AMG methodology revealed
that financial development is significantly and positively
coupled with environmental pollution, as shown in Table7.
As a result, a 1% FID influence will increase EVP by
0.210%in the long run. We can draw from the empirical
findings that the current FID of the BRICS countries aids
economic development at the expense of EVP. The results
provide the impression that because the BRICS countries are
in their economies’ (initial) development stage, they have
overlooked the promising impact of FID on the EVP. We
suggest that well-developed and sound FID initiatives in
the BRICS countries are essential in curtailing EVP. These
proper initiatives should include developing funding net-
works and enabling enterprises to access credit facilities at
a lower interest rate. The present research finding aligns with
those of Charfeddine and Kahia (2019) and Shahbaz etal.
(2020). However, our studies contradict Haldar and Sethi
(2022), who found that FID does not significantly impact
environmental pollution. Figure8 indicates the graphical
presentation of the findings underlying this research.
Country‑wise dynamic analysis
The outcome of country-wise dynamic AMG estimates is
indicated in Table8. The result revealed that the coefficient of
EGC is positive and statistically significant for all the selected
countries, precisely 0.487%, 0.926%, 0.640%, 0.824%, and
0.275% for Brazil, Russia, India, China, and South Africa,
respectively. As a result, one of the contributory factors to
EVP in the BRICS countries is energy consumption. In the
same way, the coefficient of economic expansion indicates
Brazil (0.702%), Russia (0.908), India (0.593), China (1.090),
and South Africa (0.130), respectively, proving the EKC
curve’s presence in the BIRCS countries.
Regarding the coefficient of globalization, the outcome
infers that GLO positively and significantly impacts EVP
in Russia (0.434%), India (0.317), and China (0.857). On
the other hand, there is an inverse and significant associa-
tion between GLO and EVP in Brazil (−0.546) and South
Africa (−0.303). The estimated coefficient of urbanization
indicates that there is a considerable and favorable effect
of URB on EVP in Brazil (0.401%), Russia (0.503%),
and India (0.714%). However, we found out that there is
an inverse association between URB and EVP in South
Africa (−0.245%). Energy innovation estimations suggest
that ENI has a negative and substantial influence on EVP.
Finally, the coefficient of financial development is posi-
tively and statistically relevant to EVP for Brazil (0.602%),
Russia (0.108), and India (0.765). The positive association
between FID and EVP may be due to high access to finan-
cial credit facilities to increase production, impacting the
environment.
Dumitrescu andHurlin causality test
The AMG, CC-MG, and FMOL techniques only provide
long-run linkage estimates between the variables. In panel
data, these approaches, on the other hand, do not reveal the
causal association between series. Analysis of the causality
Fig. 8 Graphical presentation of
empirical results of the study

Environmental Science and Pollution Research
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association is significant in recommending effective and
efficient measures for stakeholders and policymakers. As a
result of slope heterogeneity among cross-sectional series,
the research utilized a novel approach proposed by Hurlin
and Dumitrescu (2008). The D-H technique is a new ver-
sion of the Granger causality test that incorporates cross-
sectional data CSD and slope of heterogeneity. This tech-
nique provides the W-bar and Z-bar statistics. Table9 shows
the results of the D-H non-causality test. The outcome of
the Granger causality test indicates that energy consumption
and energy innovation have a bidirectional association with
EVP. The Granger causality test further revealed a unidi-
rectional causality between economic expansion, globaliza-
tion, urbanization, financial development, and environmental
pollution. The findings indicate that policy targeting EVP,
EGC GLO, and ENI shall has an alternating approach since
a bidirectional Granger causality effect exists on each other.
The implication is that any radical changes in EGC, GLO,
and ENI will increase EVP and vice versa. A bidirectional
causality between energy consumption and EVP implies
that energy efficiency and energy conservation programs
can reduce environmental pollution in the BRICS countries.
Due to the relative one-way causation, any policy measures
focused on these variables will impact the environmental
policies of the BRICS countries.
Conclusion, theoretical implication,
andpolicy implications
Conclusion
BRICS countries are currently expanding, and they are
now considered among the top performers in terms of
global economic growth. The related environmental
pollution that comes along with this economic devel-
opment is enormous. In this research, the main vari-
ables that affect EVP include energy consumption, eco-
nomic expansion, globalization, urbanization, financial
development, and energy innovation. Accordingly, this
research investigated the influence of these indicators on
EVP among the BRICS countries using panel data from
1990 to 2020. To estimate the interaction among these
variables, we initially analyzed the CSD across the cross-
section of the data. Westerlund (2007) cointegration
technique was applied to estimate the long-run associa-
tion among the variables. After confirming the cointegra-
tion relationship between the series, this research used
the AMG, CC-MG, and FMOLS approaches to estimate
the variables’ long-run interaction.
To sum up the findings of this study, (1) we found a
positive and significant association between economic
Table 8 AMG long-run
elasticity estimates (country-
wise analysis)
*** denotes significance level at 1%. The symbols shows [ ] (standard errors) and probability ( )
Variables Brazil Russia India China South
Africa
InEGC 0.487*** 0.926*** 0.640* 0.824*** 0.275**
[0.017] [0.004] [0.230] [0.011] [0.024]
(0.000) (0.000) (0.001) (0.000) (0.001)
InEXP 0.702** 0.908*** 0.593** 1.090*** 0.130***
[0.042] [0.003] [0.591] [0.036] [0.001]
(0.002) (0.000) (0.001) (0.000) (0.000)
InGLO −0.546 0.434* 0.317* 0.857*** −0.303
[0.062] [0.223] [0.129] [0.153] [0.169]
(0.004) (0.083) (0.033] (0.000) (0.466)
InURB 0.401* 0.503** 0.714* 0.945** −0.245
[0.435] [0.295] [0.246] [0.275] [0.497]
(0.010) (0.001) (0.004) (0.020) (0.001)
InENI −0.215*** −0.240 −0.319** −0.428 −0.348
[0.620] [0.195] [0.0054] [0.473] [0.531]
(0.000) (0.137) (0.005) (0.316) [0.284)
InFID 0.602*** 0.108*** 0.765*** −0.386** −0.164**
[0.004] [0.002] [0.005] [0.005] (0.021)
(0.002) (0.000) (0.000) (0.000) (0.050)
Constant 3.645 −12.141 −8.366 −3.226*** 0.526
[1.470] [5.233] [4.132] [1.319] [0.006]
(0.001) (0.183) (0.000) (0.000) (0.000)

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expansion, energy consumption, urbanization, financial
development, and environmental pollution. (2) In contrast,
globalization and energy innovation extensively dissipate
EVP in the BRICS economies. (3) The outcome of the
Granger causality test indicates that energy consumption
and energy innovation have a bidirectional association with
EVP. (4) The D-H result revealed a unidirectional causality
between economic expansion, globalization, urbanization,
financial development, and environmental pollution.
Theoretical implication
This research employed the STIRPAT approach to building
an extended model on environmental pollution by incor-
porating new variables like globalization, energy innova-
tion, and financial development to achieve the goals of this
study. The concept of the IPAT and STIRPAT approaches
provides an extensive understanding of the mechanism by
which activities of people affect EVP. The objective of the
STIRPAT is to provide a conceptual framework and statisti-
cal strategy for testing the relationship between the actions
of people and their impact on the natural environment. The
underlying concept is to identify the fundamental variables
that cause environmental pollution and mitigate these factors
to help conserve the environment. Therefore, we are very
optimistic that the STIRPAT approach will contribute to the
primary understanding of the interaction between humans
and the preservation of the natural ecosystem.
Policy implications
The following are some relevant policy implications rec-
ommended for dissipating EVP in the BRICS economies.
First, the positive impact of energy consumption on envi-
ronmental pollution indicates the call for policymakers and
the government to implement pragmatic activities to address
this critical problem. We, therefore, recommend that various
governments in the BRICS countries focus on dwindling the
utilization of conventional fossil energy. Instead, the policy-
makers should invest more into innovation and advancement
to improve efficiency in energy usage to reduce environ-
mental pollution. Also, pragmatic initiatives such as focus-
ing on taxation beyond energy, energy conservation meas-
ures, investment in low-carbon cities, and the expansion of
electricity markets among the BRICS countries would help
sustain the environment (Sampene etal. 2021). Moreover,
we suggest that incorporating green policies and practices
could provide a better alternative to improving the BRICS
countries’ pathway to reducing environmental pollution (Cai
etal. 2022).
The second is to offset the effect of economic expansion
contribution to environmental pollution. This study suggests
that policymakers facilitate the creation of tertiary corpora-
tions that rely on high-technological innovation and design and
implement strategies to reduce environmental pollution (Osei
etal. 2021; Ostic etal. 2022; Zhang 2021). Third, empirical
Table 9 Findings of pairwise Dumitrescu and Hurlin panel causality
test
*** denotes significance level at 1%—the symbols ⇎ (does not
homogeneously cause), ⟷ (bidirectional), and → (unidirectional)
Null hypothesis W-stat Z-bar-Stat Prob Conclusion
InEGC ⇎ InEVP 6.217*** 3.775 0.000 EGC ⟷ EVP
InEVP ⇎ InEGC 4.367* 1.361 0.000
InEXP ⇎ InEVP 3.800** 1.510 0.002 EXP → EVP
InEVP ⇎ InEXP 1.284 −0.839 0.401
InGLO ⇎ InEVP 12.362*** 9.524 0.000 GLO → EVP
InEVP ⇎ InGLO 2.304 0.115 0.908
InURB ⇎ InEVP 5.052** 2.685 0.002 URB → EVP
InEVP ⇎ InURB 1.029 −1.078 0.281
InENI ⇎ InEVP 6.723*** 4.248 0.000 ENI ⟷ EVP
InEVP ⇎ InENI 4.730 2.422 0.005
InFID ⇎ InEVP 6.723*** 4.248 0.000 FID → EVP
InEVP⇎ InFID 1.730 −0.422 0.672
InEXP⇎ InEGC 6.252*** 3.780 0.000 EXP → EGC
InEGC⇎ InEXP 1.841 −0.319 0.749
InGLO⇎ InEGC 8.103*** 5.539 0.000 GLO ⟷ EGC
InEGC⇎ InGLO 5.123** 2.751 0.005
InURB⇎ InEGC 4.596 2.258 0.003 URB → EGC
InEGC⇎ InURB 1.945 −0.217 0.827
InENI⇎ InEGC 7.381*** 0.748 0.000 ENI → EGC
InEGC⇎ InENI 4.040** 0.803 0.003
InFID⇎ InEGC 7.600 5.069 0.000 ENI → EGC
InEGC⇎ InFID 3.193 0.946 0.143
InGLO ⇎ InEXP 5.269** 1.014 0.002 GLO → EXP
InEXP ⇎ InGLO 1.479 0.144 0.320
InURB⇎ InEXP 6.876** 2.515 0.001 URB → EXP
InEXP⇎ InURB 2.865 0.63715 0.5240
InENI⇎ InEXP 8.100*** 1.856 0.000 ENI → EXP
InEXP⇎ InENI 3.065 0.823 0.410
InFID⇎ InEXP 4.869* 0.640 0.009 FID → EXP
InEXP⇎ InFID 2.150 0.308 0.975
InURB⇎ InGLO 7.723*** 0.744 0.000 URB ⟷ GLO
InGLO⇎ InURB 7.417*** 0.898 0.000
InENI⇎ InGLO 7.891*** 5.341 0.000 ENI ⟷ GLO
InGLO⇎ InENI 6.693*** 4.220 0.000
InFID⇎ InGLO 9.822*** 7.148 0.000 FID ⟷GLO
InGLO⇎ InFID 5.484*** 3.090 0.000
InENI⇎ InURB 6.294*** 3.847 0.000 ENI → URB
InURB⇎ InENI 4.447 2.120 0.034
InFID⇎ InURB 5.257** 2.877 0.004 FID → URB
InURB⇎ InFID 2.605 0.396 0.691
InFID⇎ InENI 5.527*** 1.258 0.000 FID → ENI
InENI⇎ InFID 1.38762 0.192 0.847

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results from our research indicated that globalization helps
mitigate environmental pollution in the BRICS countries.
Therefore, the study suggests that policymakers invest in
novel environmentally friendly applications and technologies
and promote global collaboration that focuses on reducing the
environmental pollution. These global collaborations can be
in international agreements that focus on carbon emission and
environmental preservation policies (Xu etal. 2018).
Fourth, considering the positive and significant effect of
urbanization on EVP in the BRICS economies, environmen-
tal strategies are needed to guide urbanization development
in these nations without compromising economic expansion
and environmental pollution issues. Thus, stakeholders in
urban planning in the BRICS countries should address pol-
lution from urbanization by adopting effective land acquisi-
tion and use and promoting smart cities. Enhancing energy
efficiency in the domestic and industrial sectors, particularly
in the BRICS countries, is crucial to disentangle urbaniza-
tion from carbon emissions (Wang etal. 2016). The BRICS
countries should coordinate and expedite the mechanisms
of energy innovation by establishing common interests in
adopting industry standards, addressing technological dis-
parities, cutting costs, and making clean energy technol-
ogy more accessible. Furthermore, because globalization
enhances economic openness and foreign direct investment,
BRICS economies should limit the transfer of obsolete tech-
nologies and adopt new ones. It would help economies pro-
mote environmentally friendly technologies, resulting in
lower pollution levels (Baloch etal. 2020).
Fifth, the mitigation effect of energy innovation on envi-
ronmental pollution indicates that policymakers in the BRICS
economies can rely on it as the best mechanism in fighting
against environmental pollution. Aside from the obvious
benefit of a better environment due to investments in techni-
cal innovation and green energy, growth in relevant environ-
mental technology can help firms gain a competitive edge.
Innovative technologies can earn a larger market share due
to increased demand for environmentally friendly products
and services; hence, policies focused on innovating in envi-
ronmentally friendly technology can impact the competitive
landscape in various economic sectors (Suki etal. 2022).
Lastly, financial development was found to positively
impact environmental pollution in the BRICS countries.
Our research results show that financial development cer-
tainly increases environmental pollution; therefore, we rec-
ommend that FID activities initiate strategies that focus on
waste management among firms and organizations. In the
context of sustainable development, the research suggests
that stakeholders initiate long-run financial development
strategies that can help address environmental issues in the
BRICS economies (Guo and Hu 2020).
To sum up, the BRICS economies should (1) first pay
critical attention to maintaining a balance between economic
expansion and environmental pollution, (2) firmly oppose
the hasty and haphazard development of high-pollution
activities and projects, (3) the need to increase investment
in research and development in low-carbon technology and
green innovation, (4) expedite the development and imple-
mentation of low-carbon technologies on a wide scale.
Limitation andfuture research
There are some limitations in this present research. For
example, the study’s outcome is based on a limited panel
data set for the selected series (1990–2020). In future work,
we will expand the research by analyzing the effect of vari-
ables such as good governance, the rule of law, and regu-
latory quality on environmental pollution by employing
the latest dataset. In addition, CO2 emission covers only
a nominal aspect of multifaceted environmental pollution
confronting emerging countries (Majeed and Mazhar 2020;
Murshed etal. 2021; Suki etal. 2022; Udemba 2020). Per-
haps we will incorporate ecological footprint as the proxy for
environmental pollution in future studies. Suki etal. (2022)
indicated that ecological footprint could be described as a
more holistic indicator of ecological contamination since it
considers the human and industrial practices that contribute
to an increase in the degradation of the environment. Moreo-
ver, the research can be extended to developing countries by
employing advanced methodologies to analyze the relation-
ship among factors that affect EVP in these countries.
Acknowledgements We acknowledge [1]Innovation team construction
of "low carbon economyand industrial development", supported by the
excellent innovation team construction project of philosophy andSocial
Sciences in Colleges and universities of Jiangsu Province[2]The
Humanities and Social SciencesResearch Program of the Ministry of
Education: Research on the Formation Mechanism and Breakthrough
Path of"Low-end Capture "in the Global Value Chain of High-tech
Industry (18YJA630105).
Author contribution Agyemang Kwasi Sampene, conceptualization
and methodology. Cai Li, conceptualization, methodology, and for-
mal analysis. Fredrick Oteng Agyeman, investigation and visualization.
Brenya Robert, validation and investigation.
Data availability WID: https:// datab ank. world bank. org/ source/ world-
devel opment- indic ators#
OECD: https:// data. oecd. org/ envpo licy/ paten ts- on- envir onment-
techn ologi es. htm
IMF: https:// data. imf. org/? sk= F8032 E80- B36C- 43B1- AC26-
493C5 B1CD3 3B
KOF: https:// kof. ethz. ch/ en/ forec asts- and- indic ators/ indic ators/ kof-
globa lisat ion- index. html
Declarations
Ethics approval Not applicable.

Environmental Science and Pollution Research
1 3
Consent to participate Not applicable
Consent for publication: All authors reviewed and approved the manu-
script for publication.
Competing interests The authors declare no competing interests.
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