URBANIZATION, ENVIRONMENTAL DEGRADATION AND …
Transcript of URBANIZATION, ENVIRONMENTAL DEGRADATION AND …
Journal of Economics and Management Sciences
Spring 2021, Volume 2, No.1, pp 79-81
URBANIZATION, ENVIRONMENTAL DEGRADATION
AND ECONOMIC GROWTH NEXUS IN BRICS
ECONOMIES Dr Hina Ali1 & Nargis Ejaz2
_____________________________________________________________________
ABSTRACT
This research aims to investigate the nexus between urbanization environmental
degradation and economic growth in BRICS (Brazil, Russia, India, China, South Africa)
economies. This relation is still under question. Some researcher shows positive
affiliation of urbanization with economic growth and environmental degradation while
some show negative. But truth is that urbanization environmental degradation and
economic growth are correlated to each other. For the analysis, data is taken from the
period 1990 to 2018. Two models are created the first model shows the urbanization
nexus with economic growth and the second is showing the nexus of environmental
degradation and economic growth. The GDP is the dependent variable in both model and
independent variable are labor force participation rate, carbon dioxide emission, gross
fixed capital formation, trade openness, exchange rate, school enrollment, real interest
rate, urbanization growth, and poverty headcount. Annual data is collected from the
world indicator file. To check the correlation between variables, panel co-integration
analyses such as Pedroni co-integration test, and Kao residual co-integration tests are
applied. Panel co-integration tests are employed on two models separately. Both FMOLS
models estimated the relevance. A causality test is also applied. The concluding effect
shows that in BRICS urbanization has a positive effect on economic growth and a
negative effect of environmental degradation on economic growth. The current study
base on the least considered variables panel cointegration test FMOLS technique is used.
Key Words: Gross Fixed Capital Formation, Urbanization Growth, Environmental
Degradation, BRICS
1 Assistant Professor, Department of Economics, The Women University Multan, Pakistan. Email:
[email protected] 2 MPhil Scholar, Department of Economics, The Women University Multan, Pakistan.
Ali and Ejaz 80
BACKGROUND OF THE STUDY
BRICS began in 2001 as BRIC. It is a strong grouping of the world's top emerging
market countries like Brazil, Russia, India, China. In 2010 South Africa joined this group
and knows as BRICS. Promoting peace security to each other and helping to
development and cooperation was the aim of the BRICS mechanism. Since its
establishment, BRICS has had a positive impact on the international structure but at the
same time, it faces degradation also. Brazil, Russia, India, China, and South Africa were
the World's fastest-growing markets for years just because of the sufficient natural
resources, low costs of labor, and beneficial demographics at the time when global
commodities boom.
BRICS counted 11% of global gross domestic products in 1990 this figure increases to
nearly 30% in 2014. In the 2008 financial crisis this figure despite the negative effect and
high in 2010. It is believed that this group of countries will become the dominant
supplier of manufactured. This growth process of these countries is also expected to
affect the process of urbanization. In these countries, because the process of urbanization
is understood as the synonyms of growth and development and largely based on the
production hubs of the manufactured items and services from year 9090 to 2018 one
present increase in Urbanization will increase GDP growth by 0.229023 units.
As they have urbanized BRICS nations face many difficulties, especially at that time
when they have tried to hold out against the movement of people into their cities or have
intentionally steered propel on enterprises to economically or environmentally
undesirable locations. It also provides an example to the world that how BRICS seize the
opportunity that urbanization provides. BRICS experience both good and bad in this
process. Less industrialized countries should learn a lot from the BRICS experiences to
face urbanization and into a more reliable path and steer their urbanization onto a more
reliable path. In the last few decades, it shows that the rising level of urbanization,
industrialization, increasing population, and lifestyle change has increased the threat of
global warming in BRICS from 9090 to 2018. One unit increase in CO2 omission will
decrease 5.5 units decrease in national income. In recent economic growth, large
quantities of fossil fuel for electricity generating contribute to an increase in global
emission in BRICS countries.
REVIEW OF LITERATURE
Chakravarty and Mandal (2016) looked at the affiliation among economic growth and
environmental quality for BRICS countries for this study data for BRICS nations was
collected from the period 1997 to 2011. The study first employed a fixed effect panel
data model and then for dynamic panel data it uses a generalized method of moment
(GMM) method. The dynamic panel models GMM estimates reveal the connection
between income and emission is u shaped with the turning point out of sample this out of
Urbanization, Environmental Degradation and Economic 81
sample turning point demonstrates that emissions have been increasing in lockstep with
income increase factors like imports net energy and share of industrial output in GDP as
found be a significant impact on the environment.
Siddique et al (2020) analyzed the nexus of urbanization co2 emission and energy
consumption in south Asia countries from the period 1983 to 2013. Panel cointegration,
as well as the granger causality technique, were used for this research to investigate the
long-term relationship that exists between urbanization and co2 energy. Fond that the
empirical result indicates that GDP growth and energy use had a positive role in
degradation the environment and trade were also improving in both short and long run
bidirectionality causality exists between co2, energy, and urbanization.
Zhu et al (2018) investigated the impact of income inequality and urbanization on CO2
emission in Russia China India Brazil and South Africa (BRICS) data was taken from
1994 to 2013. Panel quantile regression technique was used for this study which shows
unobserved individual heterogenicity and distributional heterogenicity. concluded that
urbanization had a negative impact on carbon emission and also quantitatively explored
the indirect and direct effect on carbon emission of urbanization. The result shows that if
we ignored the indirect effect then we may underestimate the impact of urbanization on
carbon emission. Income inequality has a significant positive impact on carbon emission
in middle and high countries. there was a u-shaped environmental Kuznets curve
between the CO2 and GDP in the BRICS economies. For policy makers, this study had a
significant consequence to enhance environmental quality policy makers should work to
close the economic gap between the affluent and the poor. To minimize carbon emission
the BRICS economies and accelerate urbanization but must enhance energy efficiency
and employ environmentally friendly energy to the maximum degree possible.
Anwar (2020) analyzed the consequences of economic growth and urbanization on CO2
emission. Yearly data was taken from 1980 to 2017 for East countries. Panel fixed effect
model was implied. The study indicates that in the nation’s studies urbanization
economic growth and trade openness all had a sustainable impact on co2 emission. The
major policy recommendation was to support green and sustainable urbanization it
helped economic growth but not at the price of environmental degradation as well as to
strategically manage and enhance the industrial structure increase renewable energy
sharing in total energy consumption.
MATERIAL AND METHODS
This study briefly discusses the data technique and empirical setup in this part using
illustration. Data as well as a structure and statistical method to estimating the nexus of
urbanization environmental degradation and economic growth in BRICS economies. The
secondary panel data estimation figure is used in this study the data set include the years
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1990 through 2018 WDI secondary source were used in this study. The quantitative
variable is utilized to investigate the nexus between urbanization environmental
degradation and economic growth in BRICS economies.
Table 1. List the Variables Utilized in this Study
Variable
Descript
Description of
variables
Unit of
measurement
Source Expected sign
GDP Gross domestic
product
Percentage WDI +ve
CO2 CO2 Percentage WDI -ve
GFCG Gross fixed
capital
formation
Percentage WDI +ve
LFPR Labor force
participation
rate
Percentage WDI +ve
TOP trade openness Percentage WDI +ve
EXR exchange rate
Percentage WDI +ve
EDU school
enrollment
Percentage WDI +ve
INT real interest
rate
Percentage WDI -ve
URB urbanization
growth
Percentage WDI +ve
POV Poverty
headcount
Percentage WDI -ve
The table show lists of all variables used in this study. This table also shows the units of
measurement for these variables as well as their expected sign. Gross domestic product is
the dependent variable. while, labor force participation rate, carbon dioxide emission,
gross fixed capital formation, trade openness, exchange rate, school enrollment, real
interest rate, urbanization growth, and poverty headcount are the independent variable.
Model Specification
Urbanization, Environmental Degradation and Economic 83
To investigate the impact of interrelationship between urbanization environmental
degradation and economic growth a panel data estimation of BRICS economies I created
two models
1st model is presented as the impact of Urbanization on Economic growth
EGR= f (LFPR, GFCF, URB, EDU, TOP, INT)
Econometrics form
EGR=α0+α1LFPR+α2GFCF+α3URB+α4EDU+α5TOP+α6INT+μ
Second model shows the impact of Environmental degradation on economic growth
EGR= f (LFPR, GFCF, CO2, TOP, POV, EXR)
Econometrics from
EGR=β0+β1LFPR+β2GFCF+β3CO2+β4POV+β5TOP+β6EXR+μ
Were
GDP= Gross domestic product EXR = exchange rate
LFPR = labor force participation rate EXR = exchange rate
CO2 = carbon dioxide emissions TOP = trade openness
EDU = school enrollment INT = real interest rate
URB =urbanization growth POV = poverty headcount
GFCF = gross fixed capital formation
FINDINGS OF THE STUDY
This section examines preliminary data analysis and the correlation among the variables.
Descriptive statistics summarize characteristics of specific data set which is split up into
measures of mean, median, minimum and maximum and the standard deviation,
Descriptive statistics provide firsthand information of the variables. The correlation
matrix clarifies the association between two variables. This table shows every variable is
correlated with another variable. The correlation coefficient indicates dependence
between two variables (annexure table 1).
The mean value shows the average value of all the existing variables. Shows Mean,
Median, Maximum, Minimum, Std. Dev., Skewness, Kurtosis of the GDP as 3.1919,
3.9500, 10.0001, -7.8000, 3.9259, -0.8407, 3.8661 respectively. LFPR and GFCF have to
Mean that is equal to 59.6741 and 4.4556, Median is that is equal to 61.0895 and 5.1069,
Maximum that is equal to 62.8540 and 21.0000, Minimum 52.3610 and -14.3999, Std.
Dev. that is equal to 3.3711 and 9.0775, Skewness-.8589 and -0.3116 respectively. The
kurtosis value shows the variable in platykurtic, mesokurtic, leptokurtic (annexure table
2).
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E-views have been used to test the pairwise correlation that helps to gives correlations
that are computed from all observations that have no missing values for any pair of the
value Correlation matrix is examining the relationship between the variables. The value
ranges from (0-1) and numerical values show the sign and relationship of values. They
are expressing negative and positive relationships shows the variable relationship
between them. This lies between -1 to +1. Negative association shows the relationship
among the positive variables
Panel Unit Root Analysis
The results of PP- Fischer chi- Square, Levin, Lin & Chu and ADF- Fischer chi-square
unit root tests determine stationary of variables or order of integration of variables. These
tests check the null hypothesis of unit root with the alternative hypothesis of no unit root.
GDP Growth, Levin, Lin & Chu t value is -9.2501 and p-value is 0.0000 and has No unit
root, ADF- Fischer chi-square stat is 3245.360 and p-value is 0.0000 with No unit root,
PP- Fischer chi- Square is 384.158 and p-value is 0.0000 with No unit root. While the
GDP is stationary for all its values at first difference as the p-value is 0.0000 for all the
tests used here. LFPR (At Level with individual intercept) has Levin, Lin & Chu t stat is
-6.03501 and p-value is 0.0000 with No unit root, ADF- Fischer chi- Square has 178.341
and p value is 0.0000 with No unit root, PP- Fischer chi- Square has t stat is 266.576 and
p value is 0.0000 with No unit root, LFPR has Levin, Lin & Chu t stat -29.4063 ADF-
Fischer chi- Square has 593.998 and PP- Fischer chi- Square has 1262.48and all are
stationary at first difference. The interest rate has Levin, Lin & Chu t*equal to 1.8037
and P value equal to 0.7919 with Unit root process, ADF- Fischer chi- Square equal to
32.4140 and P value equal to 0.6980 with Unit root process, PP- Fischer chi- Square
equal to 28.4795 with and P value equal to 1.0000 has Unit root process. While Interest
rate Levin, Lin & Chu t*, ADF- Fischer chi- Square, PP- Fischer chi- Square has -
21.1692, 351.357, and 668.270 with No unit root at first difference. School Enrolment
has Levin, Lin & Chu t*-0.96817 with p value that is equal to 0.1665 with Unit root
process, ADF- Fischer chi- Square is 19.1311 with p value that is equal to 0.0386 and No
Unit root process, PP- Fischer chi- Square 33.0707with p value that is equal to 0.0003
No unit root, School Enrollment is stationary using all the test with p-value with p value
that is equal to 0.0000. Real interest rate, Levin, Lin & Chu t stat -3.05197 and p stat are
0.0011, No unit root, ADF- Fischer chi- Square 32.7827 and p stat is 0.0003, No unit
root, PP- Fischer chi- Square 32.6619 and p stat is 0.0003No unit root, while the Real
interest rate is stationary at first difference. The urbanization growth rate is non
stationary at the level form for the BRICS economies while the Levin, Lin & Chu t*,
ADF- Fischer chi- Square, and PP- Fischer chi- Square is stationary with .0009, 0.0001,
and 0.0000respectively for three tests. Exchange rate Levin, Lin & Chu t* -0.27594
while the probability value is 0.3913, Unit root process, ADF- Fischer chi- Square
Urbanization, Environmental Degradation and Economic 85
8.23940 while the probability value is 0.4104, Unit root process, PP- Fischer chi- Square
7.44996 probability value is 0.4890, Unit root, Exchange rate Levin, Lin & Chu t*-
4.04382 while the p value is 0.0000, No unit root, ADF- Fischer chi- Square 26.8970
while the p value is 0.0002, No unit root, PP- Fischer chi- Square 25.4118 while the
probability value is 0.0003, No unit root, CO2 omission Levin, Lin & Chu t* 1.31532
with p value 0.9058, ADF- Fischer chi- Square 4.68541 with p value 0.911, PP- Fischer
chi- Square 4.44132 p value 0.9253, CO2 omission is stationary at first difference.
Poverty Headcount ratio is stationary at the level for as well at its first difference. GFCF
is not stationary at the level form that is stationary at the first difference, Levin, Lin &
Chu t* ADF- Fischer chi- Square, PP- Fischer chi- Square GFCF (at First difference) has
p value 0.0000 and has No unit root where same is the case with the trade openness (see
annexure table 3).
Panel Co-integration Analysis
This section applies to the analysis of panel integration of both the FMOLS 1 model and
model 2. When the variable series does not stop, then the integration of this series of
variables is integrated. Cohesive integration determines the continuous integration of a
series of variables. Panel integration indicates a long-term relationship between
variables. The Kao panel integration test and the Pedroni panel integration test are
mentioned in this section on both FMOLS models.
Table 2: Pedroni Panel Co-Integration Test Model
Model-1
Method Alternative Hypothesis: Common AR coef (within dimension)
Weighted
t. Stat Prob. t-Stat Prob.
Panel V- stat 0.062563 0.4751 -0.533213 0.7031
Panel rho-stat -3.170936 0.0008 -1.548262 0.0608
Panel PP-stat -4.055067 0.0000 -2.530714 0.0057
Panel ADF-stat -2.142766 0.0161 -1.472640 0.0704
Alternative Hypothesis: Individual AR coef (between dimension)
t-Stat Prob.
Group rho-stat -0.731723 0.2322
Group PP-stat -2.491845 0.0064
Group ADF-
stat
-1.567451 0.0585
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Model-1
Method Alternative Hypothesis: Common AR coef (within dimension)
Weighted
t. Stat Prob. t-Stat Prob.
Panel V- stat 0.246463 0.4027 -0.408584 0.6586
Panel rho-stat -3.625902 0.0001 -2.744920 0.0030
Panel PP-stat -4.744248 0.0000 -3.842877 0.0001
Panel ADF-stat -3.375917 0.0004 -3.634119 0.0001
Alternative Hypothesis: Individual AR coef (between dimension)
t-Stat Prob.
Group rho-stat -1.256067 0.1045
Group PP-stat -3.235721 0.0006
Group ADF-
stat
-2.769960 0.0028
Source: Author’s estimation using EViews 9.5.
The table explores Pedroni Panel co-integration analysis of model 1 and model 2 Panel V
statistics and Panel ADF statistics with probability values Panel V- stat 0.062563 p value
is 0.4751, Panel rho-stat -3.170936 p value is 0.0008, Panel PP-stat is -4.055067 and p
value is 0.0000, and probabilities are significant. Acceptances of null hypothesis
occurred. No co-integration exists between variables. If the null hypothesis is accepted
then there will be no cointegration exists among variables. Panel Group rho-stat is -
0.731723 with p value 0.2322, Group PP-stat -2.491845 with p value 0.0064 and Group
ADF-stat -1.567451 and p value 0.0585 that are significant which means there is co-
integration exists between variables. 11 outcomes in which majority of tests are rejecting
ho and Both Panel ADF statistics and Panel V statistics (within dimension) are accepted
null hypothesis.
The table explores the Pedroni Panel co-integration analysis of model 2. Panel rho
statistics and Panel PP statistics with probability values 0.4027, 0.0001, 0.0000 and
0.0004 are highly significant. Rejection of null hypothesis has happened. Co-integration
exists among variables. Weighted Panel V statistics has t statistics -0.408584 p value
0.6586, -2.744920 with p value 0.0030, -3.842877 p value 0.0001, -3.634119 and p value
0.0001which means null hypothesis is accepted and no co-integration exists among
regressors 11 outcomes in which majority of tests are rejecting ho. Panel V statistics and
Panel ADF statistics are also significant. Group rho statistics, Group PP statistics and
Group ADF statistics are highly significant means co-integration exists between
variables.
Urbanization, Environmental Degradation and Economic 87
Table 3: Panel Kao residual Co-integration Test Model
ADF statistics MODEL 1 MODEL 2
T-statistics 7.3589 3.65981
prob 0.0000 0.00581
Table 3 explores the Panel Kao residual co-integration test of models 1 and 2. The results
indicate that model 1 is significant with probabilities of 0.0000. The null hypothesis is
rejected and there is co-integration exists between variables. Results indicate that model
2 is significant with a p value of 0.00581. It means that the null hypothesis is rejected
and there is co-integration exists between variables.
Fully Modified Ordinary Least Square Analysis
This section deals with the basic model of a completely normal square conversion with
model 2. These types define the various relationships and dependent variations. Where
cohesive integration is present within a flexible series, then the FMOLS regression is
used to estimate the positive long-term relationship between the variable series. When a
cohesive relationship exists, then the FMOLS method removes the endogeneity and
serial correlation effect from regressors.
Table 4: Results of FMOLS
Model 1 Variable Coefficient Std. Error t-Statistic Prob.
LFPR 0.309471 0.103596 2.987278 0.0038
INT -11.59618 3.818949 -3.036485 0.0032
URB 0.229023 0.044861 5.105206 0.0000
GFCF 0.225562 0.017034 13.24177 0.0000
EDU 0.051181 0.022425 2.282302 0.0249
TOP 0.033467 0.041640 0.803716 0.4242
C 0.026294 0.019626 1.339731 0.1838
Model 2
LFPR 0.347553 0.236349 1.470506 0.1652
GFCF 0.425582 0.018994 22.40645 0.0000
CO2 -5.59E-06 4.62E-06 -1.209333 0.2481
TOP 0.280097 0.081364 3.442535 0.0044
POV -0.404611 0.492462 -0.821608 0.4261
EXR 0.010746 0.028532 0.376635 0.7125
C -0.457570 0.280651 -1.630388 0.1115
Source: Author’s estimation using EViews 9.5.
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The impact of Urbanization on Economic growth
Urbanization has a significant positive effect on GDP growth, it means that when
Urbanization is increased GDP growth rate increased, one unit increase in Urbanization
will increase GDP growth by 0.229023 units. This is a broader factor to influence
Economic growth. It improves the standard of living of poor ones and promotion
financial and social services for people (Bonito et al., 2017).
Primary school enrollment rate and economic growth have a positive relationship. One
unit increase in primary education level explains 0.051 units increase in growth.
Education variable reduces income inequality poverty and economic growth. School
enrollment upgrades employment levels and equal distribution of income and the income
level of the country. Education is a highly statistically significant consequence of
economic growth. These variables have desirable relation between them. When
education enrollment is increased, then economic growth will be enhanced. The
coefficient has 0.051181and the p value is 0.0249 and which is significant in BRICS
countries. The coefficient value indicates that a one unit increase in school enrollment
rate explains 0.051 units increased in economic growth. It has a statistically significant
consequence on growth. It creates individual employment and enhances growth.
Education variable increases economic development in BRICS countries (Akhtar et al.,
2017). The labor force participation rate has a strong significant effect on economic
growth. It has a positive connotation between these two. The coefficient value indicates
that a one unit increase in labor force participation rate explains 0.30 units increase in
Economic growth as it assures for enhance the economic growth. In total employment
power, the contribution will have a huge impact on economic growth. It was found that
INT has a significant negative impact on the economy. The coefficient is -11.59 and the
probability is significant. The real interest rate has significant encouraging
interconnectedness with the income dissimilarity index. It upsurges income growth at a
statistically significant level. The coefficient value indicates that one unit increase in real
interest rate explains -11.59 units decline in economic growth. Growth is increased by
reducing the real interest rate and has a negative effect on society.
The impact of environmental degradation on economic growth
The table explores the impact of CO2 on the economic growth of Pakistan. Many control
variables are also used in the analysis. LFPR has the coefficient 0.347553 which comes
out to be statistically insignificant. GFCF has 0.42 and the probability is 0.0000. This
investment has a positive impact on economic growth. Gross capital formation has a
highly significant connection with economic growth. CO2 omission has a negative
relationship with economic growth. It means that CO2 omission decreases the income of
the economy inequality index in prescribed countries. One unit increase CO2 omission
will decrease 5.5 units decrease in national income. GDP growth variable is statistically
Urbanization, Environmental Degradation and Economic 89
insignificant negative nexus with income inequality index. Current GDP growth is not
adequate to decrease in BRICS countries. TOP is positively related to economic growth
one unit enhance in the TOP enhance the growth 0.280, the FMOLS model estimated
that POV will decline the growth by -0.40. The labor force participation rate is highly
significant to influence growth.
Causality Analysis
The concept of causation has been discussed over the centuries but remains one of the
useful forms of knowledge since it explains what can or should be done to achieve the
desired result or avoid the unpleasant result. Causality refers to a connection in which a
change in one variable is accompanied by a change in another. Unidirectional causality
running from GDP to co2 because its p value is less than 0.05 means ho is rejected same
as with urbanization. Unidirectional causality exists from urbanization to GDP but GDP
does not granger cause urbanization. Correlation is a measure of linear dependence
between two random variables. So, no additional variables are involved in the calculation
of the correlation between all variables randomly used (see annexure table 4).
CONCLUSION AND POLICY OPTIONS
The principal reason of this study is to examine the relationship between urbanization
environmental degradation and economic growth. Panel unit root tests are applied to
check the stationary of variables. Some variables are stationary at the first difference
level all variables are stationary. To check the correlation between variables, panel co-
integration analyses such as Pedroni co-integration test and Kao residual co-integration
tests are applied. Panel co-integration tests are employed on two models separately.
Panel co-integration and FMOLS technique with two models has been used. 1st model
shows the result of the urbanization effect and the second one is showing environmental
degradation using GDP as a dependent variable in both models. The study also indicates
that labor force participation rate, carbon dioxide emission, gross fixed capital formation,
trade openness, exchange rate, school enrollment, real interest rate, urbanization growth
and poverty headcount that are used for analysis. The results confirm that Urbanization
has a significant positive effect on GDP growth, it means that when Urbanization is
increased GDP growth rate increased, one unit increase in Urbanization will increase
GDP growth by 0.229023 units in BRICS countries. Education is the highly statistically
significant, which shows that when education enrollment is increased, then economic
growth will be enhanced it is significant in BRICS countries. The coefficient value
indicates that a one unit increase in school enrollment rate explains 0.051 units increased
in economic growth. It has a statistically significant consequence on growth. The study
recommend that the government of BRICS country should reduce energy consumption
such as oil paper gas electricity and coal to be an effective way to control co2emission.
Ali and Ejaz 90
Furthermore, the BRICS Governments should adopt laws that design and offer
ecologically sound cities and wise growth strategies.
References
Akhtar, S., Warburton, S., & Xu, W. (2017). The use of an online learning and teaching
system for monitoring computer aided design student participation and predicting
student success. International Journal of Technology and Design Education, 27(2),
251-270.
Anwar, A., Younis, M., & Ullah, I. (2020). Impact of urbanization and economic growth
on CO2 emission: A case of far east Asian countries. International Journal of
Environmental Research and Public Health, 17(7), 2531.
Bonito, J. D. M., Daantos, F. J. A., Mateo, J. C. A., & Rosete, M. (2017). Do
entrepreneurship and economic growth affect poverty, income inequality and
economic development. Review of Integrative Business & Economics
Research, 6(1), 33-43.
Chakravarty, D., & Mandal, S. K. (2016). Estimating the relationship between economic
growth and environmental quality for the BRICS economies-a dynamic panel data
approach. The Journal of Developing Areas, 50(5), 119-130.
Munir, K., & Ameer, A. (2018). Effect of economic growth, trade openness,
urbanization, and technology on environment of Asian emerging
economies. Management of Environmental Quality: An International Journal.
Siddique, H. M. A., Majeed, D. M. T., & Ahmad, D. H. K. (2020). The impact of
urbanization and energy consumption on CO2 emissions in South Asia. South
Asian Studies, 31(2).
Zhang, N., Yu, K., & Chen, Z. (2017). How does urbanization affect carbon dioxide
emissions? A cross-country panel data analysis. Energy Policy, 107, 678-687.
Zhu, H., Xia, H., Guo, Y., & Peng, C. (2018). The heterogeneous effects of urbanization
and income inequality on CO 2 emissions in BRICS economies: evidence from
panel quantile regression. Environmental Science and Pollution Research, 25(17),
17176-17193.
Annexure
Table 1: Descriptive Statistics
GDP LFPR GFCF CO2 TOP POV ER EDU INT URB
Mean 3.1919 59.6741 4.4556 363916.6000 31.3126 7.3262 87.3659 105.7957 15.2668 2.0376
Median 3.9500 61.0895 5.1069 347488.6000 25.8018 0.7000 88.0884 102.7480 11.9667 2.2370
Maximum 10.0001 62.8540 21.0000 503677.1000 72.8654 36.6000 130.9974 165.3086 41.7917 3.3615
Minimum -7.8000 52.3610 -14.3999 220705.7000 15.6126 -0.0286 47.9517 94.9995 8.4583 1.2098
Std. Dev. 3.9259 3.3711 9.0775 82028.3200 15.6908 12.0520 18.0901 13.1739 8.5524 0.5806
Skewness -0.8407 -0.8589 -0.3116 0.2248 1.2194 1.3864 -0.0404 3.3417 2.0514 0.0962
Kurtosis 3.8661 2.2449 2.4835 2.2059 3.3150 3.3506 2.9846 15.2126 6.3449 2.0474
Source: Author’s estimation using EViews 9.5.
Table 2: Correlation Matrix
URB GDP LFPR GFCF CO2 TOP POV EXR EDU INT URB
GDP 1.0000
LFPR 0.0731 1.0000
GFCF 0.9161 0.1400 1.0000
CO2 -0.2255 -0.3247 -0.1133 1.0000
TOP -0.1237 -0.7994 -0.0489 0.6712 1.0000
POV -0.0405 -0.8455 -0.0525 0.3212 0.7666 1.0000
EXR -0.1704 -0.1859 -0.0962 0.3055 0.3702 0.4348 1.0000
EDU 0.1301 -0.3307 0.1249 -0.0509 0.1378 0.4444 0.0771 1.0000
INT -0.1726 -0.0562 -0.2737 -0.6368 -0.2872 -0.0335 -0.5021 0.1208 1.0000
URB 0.1832 -0.6472 0.0485 -0.4178 0.1852 0.5259 -0.1760 0.4508 0.5131 1.0000
Table 3: Results of Unit Root
Variables Method t-stat Prob. Conclusion
GDP Growth
(At Level with individual intercept)
Levin, Lin & Chu t* -9.2501 0.0000 No unit root
ADF- Fischer chi- Square 3245.360 0.0000 No unit root
PP- Fischer chi- Square 384.158 0.0000 No unit root
GDP growth
(At first difference with individual intercept)
Levin, Lin & Chu t* -18.1350 0.0000 No unit root
ADF- Fischer chi- Square 671.775 0.0000 No unit root
PP- Fischer chi- Square 1359.91 0.0000 No unit root
LFPR
(At Level with individual intercept)
Levin, Lin & Chu t* -6.03501 0.0000 No unit root
ADF- Fischer chi- Square 178.341 0.0000 No unit root
PP- Fischer chi- Square 266.576 0.0000 No unit root
LFPR
(At first difference with individual intercept)
Levin, Lin & Chu t* -29.4063 0.0000 No unit root
ADF- Fischer chi- Square 593.998 0.0000 No unit root
PP- Fischer chi- Square 1262.48 0.0000 No unit root
Interest rate (at level with no individual intercept)
Levin, Lin & Chu t* 1.8037 0.7919 Unit root process
ADF- Fischer chi- Square 32.4140 0.6980 Unit root process
PP- Fischer chi- Square 28.4795 1.0000 Unit root process
Interest rate (At first difference with no individual
intercept)
Levin, Lin & Chu t* -21.1692 0.0000 No unit root
ADF- Fischer chi- Square 351.357 0.0000 No unit root
PP- Fischer chi- Square 668.270 0.0000 No unit root
School Enrolment (At level with individual
intercept and trend)
Levin, Lin & Chu t* -0.96817 0.1665 Unit root process
ADF- Fischer chi- Square 19.1311 0.0386 No Unit root process
PP- Fischer chi- Square 33.0707 0.0003 No unit root
School Enrollment (At first difference with
individual intercept and trend)
Levin, Lin & Chu t* -4.79239 0.0000 No unit root
ADF- Fischer chi- Square 51.9975 0.0000 No unit root
Ali and Ejaz 80
PP- Fischer chi- Square 86.8320 0.0000 No unit root
Real interest rate (at Level with no individual
intercept)
Levin, Lin & Chu t* -3.05197 0.0011 No unit root
ADF- Fischer chi- Square 32.7827 0.0003 No unit root
PP- Fischer chi- Square 32.6619 0.0003 No unit root
Real interest rate (at first difference with no
individual intercept)
Levin, Lin & Chu t* -7.04632 0.0000 No unit root
ADF- Fischer chi- Square 62.8104 0.0000 No unit root
PP- Fischer chi- Square 107.047 0.0000 No unit root
Urbanization growth rate (at level with individual
intercept)
Levin, Lin & Chu t* 0.79864 0.7878 Unit root process
ADF- Fischer chi- Square 6.83551 0.7409 Unit root Process
PP- Fischer chi- Square 5.38333 0.8641 Unit root
Urbanization growth rate (at first difference with
individual intercept)
Levin, Lin & Chu t* -3.11056 0.0009 No unit root
ADF- Fischer chi- Square 86.9114 0.0001 No unit root
PP- Fischer chi- Square 275.391 0.0000 No unit root
Exchange rate (at level with individual intercept
and trend)
Levin, Lin & Chu t* -0.27594 0.3913 Unit root process
ADF- Fischer chi- Square 8.23940 0.4104 Unit root process
PP- Fischer chi- Square 7.44996 0.4890 Unit root
Exchange rate (at first difference with individual
intercept and trend)
Levin, Lin & Chu t* -4.04382 0.0000 No unit root
ADF- Fischer chi- Square 26.8970 0.0002 No unit root
PP- Fischer chi- Square 25.4118 0.0003 No unit root
CO2 omission (at level with individual intercept
and trend)
Levin, Lin & Chu t* 1.31532 0.9058 Unit root
ADF- Fischer chi- Square 4.68541 0.9112 Unit root process
PP- Fischer chi- Square 4.44132 0.9253 No unit root
CO2 omission (at First difference with individual Levin, Lin & Chu t* -3.10801 0.0009 No unit root
Urbanization, Environmental Degradation and Economic 81
intercept and trend) ADF- Fischer chi- Square 35.4094 0.0001 No unit root
PP- Fischer chi- Square 79.1172 0.0000 No unit root
Poverty Head count ( at level with individual
intercept)
Levin, Lin & Chu t* -6.19511 0.0000 Unit root process
ADF- Fischer chi- Square 140.446 0.0000 Unit root process
PP- Fischer chi- Square 11.9184 0.0180 Unit root process
Poverty Head count (at first difference with
individual intercept)
Levin, Lin & Chu t* -3.92056 0.0000 No unit root
ADF- Fischer chi- Square 23.7708 0.0001 No unit root
PP- Fischer chi- Square 36.8414 0.0000 No unit root
GFCF (at level with individual intercept and trend)
Levin, Lin & Chu t* -1.51447 0.0650 Unit root
ADF- Fischer chi- Square 17.3281 0.0674 Unit root process
PP- Fischer chi- Square 8.57700 0.5727 No unit root
GFCF (at First difference with individual intercept
and trend)
Levin, Lin & Chu t* -5.49192 0.0000 No unit root
ADF- Fischer chi- Square 50.8799 0.0000 No unit root
PP- Fischer chi- Square 53.7727 0.0000 No unit root
Trade openness (at level with individual intercept)
Levin, Lin & Chu t* -0.96561 0.1671 Unit root process
ADF- Fischer chi- Square 13.0051 0.2234 Unit root process
PP- Fischer chi- Square 14.5222 0.1505 Unit root process
Trade openness (at first difference with individual
intercept)
Levin, Lin & Chu t* -6.28474 0.0000 No unit root
ADF- Fischer chi- Square 57.1042 0.0000 No unit root
PP- Fischer chi- Square 83.5538 0.0000 No unit root
Table 4: Results of Casuality Analysis
LFPR does not Granger Cause GDP 2.99627 0.0538
GDP does not Granger Cause LFPR 1.34026 0.2658
GFCF does not Granger Cause GDP 2.39029 0.0974
GDP does not Granger Cause GFCF 1.84084 0.1646
CO2 does not Granger Cause GDP 2.90816 0.0585
GDP does not Granger Cause CO2 7.13454 0.0012
TOP does not Granger Cause GDP 0.29137 0.7478
GDP does not Granger Cause TOP 0.04932 0.9519
POV does not Granger Cause GDP 1.28155 0.2864
GDP does not Granger Cause POV 2.75301 0.0732
EXR does not Granger Cause GDP 1.73284 0.1848
GDP does not Granger Cause EXR 1.76552 0.1792
EDU does not Granger Cause GDP 0.26806 0.7654
GDP does not Granger Cause EDU 0.0452 0.9558
INT does not Granger Cause GDP 2.23706 0.1116
GDP does not Granger Cause INT 1.74999 0.1786
GFCF does not Granger Cause GDP 3.76836 0.0259
GDP does not Granger Cause GFCF 3.00558 0.0533
URB does not Granger Cause GDP 3.0729 0.05
GDP does not Granger Cause URB 0.25001 0.7792
GFCF does not Granger Cause LFPR 0.58151 0.5611
LFPR does not Granger Cause GFCF 0.36213 0.6972
CO2 does not Granger Cause LFPR 0.10426 0.9011
LFPR does not Granger Cause CO2 0.25736 0.7735
TOP does not Granger Cause LFPR 1.11164 0.3323
LFPR does not Granger Cause TOP 2.76807 0.0667
POV does not Granger Cause LFPR 4.01678 0.024
LFPR does not Granger Cause POV 1.62614 0.2067
EXR does not Granger Cause LFPR 2.79778 0.0683
LFPR does not Granger Cause EXR 1.14325 0.3251
EDU does not Granger Cause LFPR 0.02076 0.9795
LFPR does not Granger Cause EDU 1.14316 0.3225
Ali and Ejaz 80
INT does not Granger Cause LFPR 0.8743 0.42
LFPR does not Granger Cause INT 1.52008 0.2233
GFCF does not Granger Cause LFPR 1.41781 0.2464
LFPR does not Granger Cause GFCF 2.87831 0.0602
URB does not Granger Cause LFPR 4.27906 0.016
LFPR does not Granger Cause URB 0.31196 0.7326
CO2 does not Granger Cause GFCF 3.71891 0.0281
GFCF does not Granger Cause CO2 4.31868 0.0162
TOP does not Granger Cause GFCF 1.19337 0.3079
GFCF does not Granger Cause TOP 0.46158 0.6318
POV does not Granger Cause GFCF 1.28335 0.2877
GFCF does not Granger Cause POV 0.96958 0.3876
EXR does not Granger Cause GFCF 0.38142 0.6855
GFCF does not Granger Cause INT 0.01755 0.9826
EDU does not Granger Cause GFCF 0.25372 0.7765
GFCF does not Granger Cause EDU 0.81687 0.4451
INT does not Granger Cause GFCF 5.14436 0.0078
GFCF does not Granger Cause INT 1.40122 0.2521
TOP does not Granger Cause CO2 0.34155 0.7113
CO2 does not Granger Cause TOP 1.05727 0.3504
POV does not Granger Cause CO2 0.49007 0.6154
CO2 does not Granger Cause POV 1.16042 0.3215
EXR does not Granger Cause CO2 1.42994 0.2468
CO2 does not Granger Cause EXR 0.75558 0.4738
EDU does not Granger Cause CO2 1.16932 0.3144
CO2 does not Granger Cause EDU 0.47268 0.6246
INT does not Granger Cause CO2 2.12775 0.124
CO2 does not Granger Cause INT 3.52943 0.0327
URB does not Granger Cause CO2 1.23979 0.2928
CO2 does not Granger Cause URB 0.02955 0.9709
POV does not Granger Cause TOP 6.61793 0.0028
TOP does not Granger Cause POV 0.33328 0.7181
EXR does not Granger Cause TOP 3.47452 0.0368
Urbanization, Environmental Degradation and Economic 81
TOP does not Granger Cause EXR 0.34971 0.7062
EDU does not Granger Cause TOP 0.16344 0.8494
TOP does not Granger Cause EDU 0.80385 0.4502
INT does not Granger Cause TOP 0.17186 0.8423
TOP does not Granger Cause INT 0.33708 0.7146
URB does not Granger Cause TOP 0.15999 0.8523
TOP does not Granger Cause URB 0.28913 0.7494
EXR does not Granger Cause POV 5.45923 0.0108
POV does not Granger Cause EXR 0.65349 0.5289
EDU does not Granger Cause POV 4.16242 0.0212
POV does not Granger Cause EDU 0.00844 0.9916
INT does not Granger Cause POV 1.51261 0.2306
POV does not Granger Cause INT 0.45421 0.6377
URB does not Granger Cause POV 8.5615 0.0006
POV does not Granger Cause URB 0.06457 0.9376
EDU does not Granger Cause EXR 0.51901 0.5978
EXR does not Granger Cause EDU 0.96018 0.3887
INT does not Granger Cause EXR 1.85209 0.1652
EXR does not Granger Cause INT 0.44176 0.6448
URB does not Granger Cause EXR 0.18779 0.8292
EXR does not Granger Cause URB 1.16788 0.3175
INT does not Granger Cause EDU 0.53576 0.5868
EDU does not Granger Cause INT 0.55395 0.5764
URB does not Granger Cause EDU 1.802 0.1698
EDU does not Granger Cause URB 1.44808 0.2394
URB does not Granger Cause INT 4.99534 0.0084
INT does not Granger Cause URB 0.14882 0.8619