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.

mailto:[email protected]

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

Ali and Ejaz 82

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


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).

Ali and Ejaz 84

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


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

Ali and Ejaz 86

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.


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


Ali and Ejaz 88

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


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


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)




LFPR

(At Level with individual intercept)




LFPR

(At first difference with individual intercept)




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)




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


School Enrollment (At first difference with

individual intercept and trend)



Ali and Ejaz 80


Real interest rate (at Level with no individual

intercept)




Real interest rate (at first difference with no

individual intercept)




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





Exchange rate (at level with individual intercept

and trend)



PP- Fischer chi- Square 7.44996 0.4890 Unit root

Exchange rate (at first difference with individual

intercept and trend)




CO2 omission (at level with individual intercept

and trend)

Levin, Lin & Chu t* 1.31532 0.9058 Unit root



CO2 omission (at First difference with individual Levin, Lin & Chu t* -3.10801 0.0009 No unit root


intercept and trend) ADF- Fischer chi- Square 35.4094 0.0001 No unit root


Poverty Head count ( at level with individual

intercept)




Poverty Head count (at first difference with





GFCF (at level with individual intercept and trend)

Levin, Lin & Chu t* -1.51447 0.0650 Unit root



GFCF (at First difference with individual intercept

and trend)




Trade openness (at level with individual intercept)




Trade openness (at first difference with individual

intercept)




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


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

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URBANIZATION, ENVIRONMENTAL DEGRADATION AND …