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American Journal of  Environmental
Economics (AJEE) 

A Panel Examination of  Economic Growth and Environmental Pressure in the Middle 
East and South Asia

Ehsanullah1* 

Volume 2 Issue 1, Year 2023
ISSN: 2833-7905 (Online)

DOI: https://doi.org/10.54536/ajee.v2i1.2214
https://journals.e-palli.com/home/index.php/ajee

Article Information ABSTRACT

Received: October 31, 2022
Accepted: November 27, 2022
Published: December 01, 2023

The study observes the relationship between environmental scarcity and per capita income 
through carbon dioxide in twenty-six countries from 1990 to 2020. The detailed objective 
is to find whether the projected associations validate the inverted U-shaped hypothesis as 
demonstrated by the Environmental Kuznets Curve (EKC). Using panel fixed and random 
effects valuation procedures, the manuscript finds the negative and significant impact of  
income on pollution, which proves the validity of  EKC. But energy consumption, population 
and industrial sectors have positive effects on the environment. Besides this, we also found 
inverted U curve status by using generalize method of  moments (GMM) technique. Therefore, 
we reveal that the increase in income will reduce the pollution in our study sample.

Keywords

CO2, EKC, Fixed Effect, 
Random Effect

1 Department of  Economics, Hannam University, Hannam Ro, 70, Daeduck Gu, 34430 Daejeon, South Korea
* Corresponding author’s e-mail: ehsan3171@gmail.com

INTRODUCTION
Economic growth and government policies can change 
the types of  mechanical and financial doors employed to 
address environmental challenges. It would be interesting 
to discover if  ecological conservation and economic 
development can coexist under these circumstances. 
When everything is said and done, the quality of  
environmental products is definitely acceptable, therefore 
increased income from deregulation would increase 
people’s interest in natural resources of  better quality. 
(Day & Grafton, 2003; Egli, n.d.; Lindmark, 2002) 
Many countries economic growth is influenced by 
various sorts of  pollution, including air pollution, 
noise pollution, resource depletion, climate change, 
deforestation, and many others. According to the EKC 
statistical association, environmental damage rises with 
growth and industrial development due to dirty practices, 
increased reliance on natural resources, high pollutant 
discharges, and a greater desire to increase production 
and yield levels without considering the environmental 
consequences.(Sabroso et al., 2023) Therefore, achieving 
economic growth is significantly more complicated 
for states than guaranteeing environmental safety. 
The strict environmental regulations in industrialized 
nations highlight the degree of  industrial expansion into 
underdeveloped economies in a straightforward emphasis 
on the environment.(Egli n.d 2002; Nguyen Van, 2005).
Since much empirical papers have presented the subject of  
Environmental Kuznets Curve (EKC) over the previous 
years. These lessons observed the occurrence of  the 
EKC in some areas and republics of  the world by means 
of  various environmental pointers such as, freshwater 
quality, carbon emissions, sulfur dioxide, nitrogen 
oxide and so on. In some studies, the EKC numerical 
relationship between environmental deprivation and 
per capita income was detained for some indicators of  
environmental and on the other hand the EKC theory 

could not be recognized. Furthermost, the studies were 
directed in high income countries (Omotor & Orubu, 
2015; Bouvier, 2004; Bartoszczuk et al., 2002; Roca et 
al., 2001);  with limited number in Asia and middle east 
region. 
Apparently, it is necessary to know the nature of  the 
affiliation between environmental degradation and 
economic growth before encouraging the use of  EKC as a 
policy director in resolving environmental complications. 
Certainly, if  EKC is empirically confirmed as true, it will 
just imply that environmental damage is an inevitable 
consequence of  progress. Given the status of  the reputed 
EKC for economic development and environmental 
sustainability, a significant extent of  study on the reality 
of  EKC has appeared but with mixed empirical support. 
For instance, those which have established the presence 
of  EKC e.g., (Akpan & Chuku, 2011; Beckerman, 1992; 
Bednar-Friedl & Getzner, 2003; Gene M. Grossman 
1991, n.d.; Heil & Selden, 2001; Holtz-Eakin & Selden, 
1995).
Our study is herewith related in conducting a new path 
for improving environmental quality. The study objects 
to subsidize the current literature on the EKC and 
conduct an empirical analysis to determine whether the 
EKC concept holds or not in the middle east and south 
Asia. These two regions have numerous environmental 
and economic issues, i.e., climate change, water scarcity 
and land degradation from the last three decades. The 
implication of  showing a learning on the empirical 
investigation of  environmental Kuznets curve in these 
regions would essentially go an extended way in terms 
of  policy result making. Hence, checking for EKC has 
big policy suggestions in the sense that its existence or 
absence would control the sort of  policies that will be 
expressed by policy makers.
The primary motivation behind this paper is obtained 
from the detail that the geographic structure of  south 



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Am. J. Environ Econ. 2(1) 59-68, 2023

Asia and middle east makes it helpless to the atrocities 
of  climate change restricting from the rising CO2 trends 
within this region. Moreover, these developing economies 
have insistently showed increasing trends in the overall 
energy mandate, in most cases, is principally derived from 
uses of  fossil fuel.
Meanwhile, in this research paper, we check the rationality 
of  EKC expectations in the Hausman test using a panel 
dataset in twenty-six countries (See appendix Table.A.1) 
for the period 1990–2020. We illustrate that economic 
growth, energy consumption, population density, trade 
openness, and industrialization create environmental 
destruction in the extensive run, but the square form of  
the per capita income reduces the CO2 emissions. These 
consequences, which are in courtesy of  EKC theory 
clarify the energy of  the worldwide public in significant 
revision policy to global warming justification particularly 
in industrialized and developing countries.

LITERATURE REVIEW
The EKC is a revised form of  the Kuznets curve 
hypothesis put forward by Simon Kuznets (1955) in 
which the author supposed a nonlinear inverted-U 
shaped association between economic growth and 
income inequality. The modification between these two 
hypotheses is that the EKC hypothesis substitutes income 
inequality in the Kuznets curve hypothesis and remarks 
on the changing plans of  environmental value, emissions 
of  CO2 in the outline of  this paper, with increasing state 
income level which is used to assignment for economic 
development. 
The first empirical study that verified the rationality of  the 
EKC hypothesis for Mexico was founded by (Gene M. 
Grossman 1991, n.d.) in which the writers used sulfur and 
smoke releases to measure environmental quality within 
the EKC examination. But the findings destined the 
presence of  the EKC hypothesis in Mexico. Succeeding 
the paths of  Grossman and Krueger (1991), numerous 
of  the successive revisions have used various actions 
of  environmental quality to assess the legitimacy of  the 
EKC hypothesis. These varied pointers of  environmental 
quality, mainly with application to air pollution, comprised 
total GHG emission (Huang et al., 2008), carbon dioxide 
(CO2) emissions (Ansari et al., 2020; Ehigiamusoe, 2020; 
Galtsev, 2020) nitrous oxide (NO) emissions (Leppelt et 
al., 2014) , sulfur dioxide emissions (Mosconi et al., 2020), 
and suspended particulate matter emissions (Orubu & 
Omotor, 2011). 
Besides, indicators of  water quality (Somlanare Romuald, 
2010), land quality (Mrabet, 2017), ecological footprints 
(Ansari et al., 2020), and forest reserves (Rahman & Islam, 
2020) was also used to represent environmental value 
within the EKC story. The greenhouse emissions-induced 
EKC theory has been discovered using together panel 
and time-series models. Among the panel studies, (Salim 
et al., 2019) found statistical indication in courtesy of  the 
EKC theory holding for a panel of  13 Asian countries. 
Likewise, in a current study by (Leal & Marques, 2020), 

the authors also found evidence of  the EKC hypothesis 
holding for the extremely globalized Organization for 
Economic Cooperation and Development (OECD) 
fellow nations. Identical decisions were finished by 
(Heidari et al., 2015) for five Southeast Asian countries, 
(ben Jebli & ben Youssef, 2016) for 25 OECD economies, 
(Al-Mulali & Ozturk, 2016) for 27 advanced economies, 
(Mrabet, 2017)for 90 high-, middle-, and low-income 
republics.
The EKC hypothesis was neither detained for the 
full sample nor the sub-samples of  diverse income 
assemblies. Comparable findings were stated by (ben 
Jebli & ben Youssef, 2016)for 24 Sub-Saharan African 
economies, (Salim et al., 2019)for five Southeast Asian 
nations,(Gormus & Aydin, 2020) for top 10 advanced 
nations (Jin & Kim, 2020)for 34 Annex-I countries. 
Lately,(Ansari et al., 2020) used panel data estimators and 
found statistical rationality of  the greenhouse emission 
induced EKC hypothesis in the setting of  South Asian 
economies. 
Alternatively, several of  the current panel studies have 
used both the collective and disaggregated volumes of  to 
estimate the EKC hypothesis.(Ewane & Ewane, 2023b) 
In a study including of  22 OECD nations, (Leppelt et al., 
2014) showed statistical rationality to the EKC hypothesis 
for total greenhouse emissions as well as for emissions of  
CO2, CH4, and NO. On the other hand, (Mosconi et al., 
2020) found the rationality of  the EKC hypothesis, about 
a sample of  six oil-exporting African thrifts, to be mixed 
across different gages used to calculate environmental 
quality. The consequences confirmed the EKC hypothesis 
only for CH4 discharges while contesting it for CO2 and 
nitrogen dioxide emissions. Between the country-specific 
lessons that used time-series EKC models, (ben Jebli 
& ben Youssef, 2016)used quantile regression methods 
and established the validity of  the CO2 emission induced 
EKC hypothesis for 12 out of  15 OECD member realms. 
Earlier studies focused on the pollution-induced EKC 
hypothesis have measured several judgmentally important 
macroeconomic masses that are likely to influence the 
economic growth-emissions relationship. Among these, 
numerous studies have measured for aggregate energy 
feasting levels within the EKC analysis based on the 
considerate that energy consumption disturbs both the 
economic wealth level and the quality of  the atmosphere. 
By the way, (Murshed et al., 2022)controlled for aggregate 
energy consumption within the EKC model and found 
statistical validity of  the CO2 emissions brought EKC 
hypothesis for Indonesia, China, and Brazil but not in 
the context of  India. However, the authors declared that 
energy consumption enlarged the CO2 emission levels in 
all four countries.  
In addition, energy consumption was requested to 
positively influence the volumes of  CO2 release. Also, 
the results stated in the study by (Mrabet et al., 2017) 
maintained the CO2 emission induced EKC theory 
for Qatar. Furthermore, the results also presented that 
advanced electricity use was accountable for lower CO2 



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Am. J. Environ Econ. 2(1) 59-68, 2023

emissions in Qatar. In a new study,(Usman et al., 2022) 
energy consumption in Pakistan declined the quality of  
the environment by inspiring greater volumes of  CO2 
into the air. Also, the authors also found the legitimacy of  
the EKC hypothesis in the framework of  Pakistan.     
Therefore, it is apparent from confusing findings from 
the studies that the validity of  the EKC hypothesis 
is not guaranteed. Rather, it depends largely on the 
macroeconomic variables, energy consumption, which 
are controlled within the analysis. Hence, this paper 
makes a different attempt to bridge the gap in the EKC 
literature in this regard.

METHODOLOGY 
Data
We examine the clear state of  an income pollution 
relationship for an example of  twenty-six countries 
(Middle East and South Asia) over the period 1990–2020. 
The population is estimated as population density, and 
income, which catches economic wealth, is estimated 
as real GDP per capita (constant 2015 US dollars). 
Innovation is estimated utilizing energy force. Energy 

consumption is regularly communicated as all out-energy 
use per dollar GDP. This energy use is in Kg of  oil per 
capita. Environmental degradation is determined by 
utilizing air toxins, in particular, CO2 discharges. Here we 
consider two measures of  CO2 emission, (i) CO2 emission 
measured by metric tons per capita (hereafter CO2-A) and 
(ii) CO2 emission measured by kg per PPP $ of  GDP 
(hereafter CO2-B).
Both data on CO2-A and CO2-B are used quinquennial 
from 1990 to 2020 (i.e., 1990, 1995, 2000, 2005, 2010, 
2015, and 2020). However, the trade openness and 
industrialization are taken as a percentage of  gross 
domestic product (%GDP). 
The data is sourced from the World Bank’s World 
Development Indicators online database.1 All 
explanatory variables are the same years as the dependent 
variables. Table 1 provides the summary statistics for 
CO2-A and CO2-B, and the correlation coefficients 
among major variables. The correlation between two 
CO2s is 0.42, implying that the relation is weaker than a 
prior expectation. Also, Figure 1 shows the scatter plots 
between GDP and pollution indicators.

Figure 1: Scatter plot of  CO2s and GDP
Notes: (i) See Table 1 for the definitions of  variables. (ii) GDPC and both CO2-A and CO2-B are the average of  5 years (i.e., 1990, 
1995, 2000, 2005, 2010, 2015, and 2020).

Table 1: Descriptive statistics 
Variable Explanation Mean Median St. Dev. Max Min
GDPC Gross domestic product per capita 10696.52 3575.38 15417.91 66023.63 436.56
CO2-A CO2 emissions, metric tons per capita 6.95 2.72 9.70 48.37 0.04
CO2-B CO2 emissions, kg per PPP $ of  GDP 0.30 0.29 0.16 1.04 0.00

Correlation coefficient
GDPC CO2-A CO2-B

GDPC 1
CO2-A 0.92 1
CO2-B 0.26 0.42 1

Note: Variables GDPC and both CO2-A and CO2-B are the average of  5 years (i.e., 1990, 1995, 2000, 2005, 2010, 2015, and 2020)



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Model Specification
With the earlier, and given the system previously thought 
to be over, the important formation of  the EKC detailing 
is that contamination power declines as pay levels rise. 
By this proposition, the prime equation of  EKC can be 
written as, 
CO2it=α1+β1(y)i+β2(y2)i+β3(EC)i+β4(TOP)i+β5(PD)
i+β6(IND)i+εit.…                (1)
where CO2 =[CO2-A,CO2-B];  and ‘𝑦’ = GDP per 
capita income and the y2 is the square form of  GDP per 
capita. The subscript ‘i’ and ‘t’ represent the countries and 
years respectively.
EC= Energy consumption in kg of  oil per capita
PD= Population density in mass per unit volume
TOP= Trade openness in the percentage of  GDP
IND= Industrialization in the percentage of  GDP
ε = Error term

Estimation Techniques 
First, this study includes a panel data examination using 
pooled least squares, the fixed effects (FE) and the 
random effects (RE) requirements. The Hausman test 
is also examined to regulate whether the fixed effects or 
the random effects are more suitable. The fixed effects 
model of  valuation is determined in inspecting the 
influence of  indicators which fluctuate over time. Fixed 
effects model also discovers the relationship between 
forecaster and result variables within an article. The fixed 
effects model runs for all time-invariant changes so that 
the approached coefficients of  the fixed effects models 
would not be influenced because of  lost time-invariant 
features. Summarily, the main goal of  the FE models is 
to observe the reasons of  fluctuations within an object 
(Torres-Reyna, n.d.). 
Under the random effects model, the differences in the 
object are supposed to be independent of  the illustrative 
variables which are present in the model. The random 
effects estimator also delivers estimates for time infinite 
covariates. In the RE model, distributional expectations are 
completed on the error term and an estimation technique 
is used which signs out the unrelated parameters. When 
a pooled-GLS estimator is completed use of, the random 
effects estimator is raised (Brüderl, 2005). The Hausman 
test is used to resolve between the fixed effects model and 
the random effects model. It is analyzed to govern which 
of  the two models is suitable. The Hausman test estimates 
that the error terms are linked with the regressors. The 
fixed and random effects conditions seem desirable as 
result of  the following: 1. The RE model supports in as 
long as estimates for time – constant covariates; 2. The 
FE model is used to originate unbiased estimates; 3. The 
RE is a well degree of  finding the true causal result of  
an object; 4. The FE and the RE models’ assistance in 
scrutinizing panel data which creates of  timeseries data 
and cross-sectional data.
The regression proceeds in a panel analysis, using the 
method of  generalized method of  moments (GMM). 
Equation (1)’s pooled LS results are confused since 

it includes the lagged dependent variable among the 
explanatory variables. An instrumental variables estimator 
or the Generalized Method of  Moments must be used to 
obtain consistent estimates (GMM). The system GMM 
estimator was then adopted. (Arellano and Bond, 1991) 
proposed the system GMM estimator and stated that if  
the orthogonality constraints between delayed values of  
the dependent and the disturbances were used, additional 
instruments may be acquired in a dynamic model from 
panel data. By first-differencing nation effects, the GMM 
estimator also accounts for potential explanatory variable 
endogeneity. If  there is no second-order autocorrelation in 
the unique error terms, the first-differenced endogenous 
variables of  EMS with two lagged periods can be 
considered valid instruments. Since the error term could 
be correlated with the first difference explanatory factors 
of  GDP with one lag period, they were also employed as 
an instrumental variable. This is because environmental 
degradation may worsen economic growth in some 
circumstances.
The CO2 variable for all nations utilized in the investigation 
is estimated in metric tons per capita/per annum and kg 
of  PPP to change for the population size of  the nations 
utilized for the examination. CO2 information was 
gathered for the period 1990 – 2020 for the twenty-six 
nations utilized in this study. The low per capita CO2 
discharges for these nations would recommend that 
these levels should continue by progressively improving 
different methods of  falling discharges, for instance using 
ecological guidelines. CO2 release information was gotten 
from the World Bank, World Development Indicators.
Among the various factors that influence per capita 
carbon dioxide creation, per capita income is the factor 
that has provoked the biggest measure of  theoretical and 
experimental investigation. Our proportion of  pay per 
capita is GDP per capita at steady costs (US 2015) since 
this proportion of  GDP is more solid and accessible 
than the proportion of  GNP and the two measures 
are exceptionally associated. Gross domestic product is 
significantly more applicable to agricultural nations than 
the Gross National Product (GNP) as a proportion of  
yield. There is a wealth of  economic writing and exact 
help of  the EKC for the arrangement of  toxins. Financial 
Growth and the Environment by Grossman and Krueger 
(1995) shaped the crucial reason for some econometric 
trial of  the EKC done over the long run (Somlanare 
Romuald, 2010).
Energy is mandatory for economic growth because all 
manufacturing and consumption actions are directly 
related to energy consumption. The main sources of  
energy are converted from fossil fuels for the industrial 
revolution. The rapid usage of  these fuels for economic 
progress has commanded significant growth in the global 
emissions of  several hesitantly adverse gases. The harmful 
emissions are not only contaminating the atmosphere 
but also disturb human life to a major level. All the air 
pollutants are extremely dangerous, but CO2 is the major 
source of  global warming, which contributes more than 



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60% of  the outcome of  greenhouse gasses (Birdsall & 
Wheeler, 1993). 
The effect of  the industrial sector is one of  the biggest 
challenges for all types of  contamination in south Asia 
and middle east. In addition to automobile emissions 
being responsible for more than 45% of  pollution, 
industrial pollutants are also creating a huge environmental 
deficiency. Industrialization to accomplish economic 
development has caused worldwide environmental 
deprivation. While the influences of  industrial movement 
on the natural environment are the main concern in 
developing and developed regions (Saboori & Sulaiman, 
2013). Agriculture, textile, oil, and gas industries are 
the main source of  pollution in south Asia and middle 
east. Cleaner technologies and best government policies 
might be useful for controlling this contamination from 
manufacturing places.
Trade openness is proxied as (% GDP) and is estimated 
in this example as the proportion of  the number of  
fares and imports to the apparent GDP. Exchange as 
proposed in the writing is a significant determinant of  

worldwide innovation reception and dispersion. This 
happens through imports of  transitional info, learning-by-
sending out experience, foreign direct investment (FDI), 
correspondence, and so forth (Somlanare Romuald, 2010). 
These cycles authorize the utilization of  current innovation 
that advances contamination reduction. The exchange (% 
GDP) information is gotten from the World Bank, World 
Development Indicators informational index. Population 
density may have an outcome in the development of  
outflows (originally of  the development in per capita 
livelihoods) using the interest for public products that 
are contamination serious, for example, framework and 
safeguard, as contended, for instance, by (Ravallion, 1997). 
Populace development insights for the chose nations show 
that the normal development rate in the locale to be 4.93%. 
It is additionally noticed that more thickly populated 
nations generally emanate more significant levels of  CO2 
focus.

RESULTS AND DISCUSSION
Panel data analysis under the panel data valuation method, 

Table 2: Pooled LS
Dependent Variable: CO2-A Dependent Variable: CO2-B
Models (A) (B) (C) (D) (E) (A) (B) (C) (D) (E)
GDPC

0.
08

 
(0

.0
4)

*

0.
02

   
 

(0
.0

3)
*

0.
03

  
(0

.0
2)

*

0.
02

 
(0

.0
5)

*

0.
40

 
(0

.0
5)

*

1.
14

E
-0

5 
(0

.0
1)

*

4.
21

E
-0

6 
(0

.1
8)

**
*

4.
97

E
-0

7 
(0

.8
7)

**
*

1.
25

E
-0

7 
(0

.9
7)

**
*

1.
93

E
-0

7 
(0

.9
6)

**
*

GDPC2

-4
.1

6E
-

09
(0

.0
2)

*

-2
.7

4E
-0

9 
(0

.0
4)

*

-2
.1

1E
-0

9 
(0

.0
5)

*

-2
.7

3E
-0

9 
(0

.0
4)

*

-3
.0

4E
-0

9 
(0

.0
4)

*

-1
.5

7E
-

10
(0

.0
0)

*

-1
.3

0E
-

10
(0

.0
2)

*

-8
.8

3E
-

11
(0

.0
3)

*

-8
.4

0E
-

11
(0

.0
9)

**
*

-7
.8

5E
-

11
(0

.0
4)

**

EC

0.
18

 
(0

.0
3)

*

0.
11

 
(0

.0
5)

*

0.
03

 
(0

.0
5)

*

0.
01

 
(0

.0
3)

*

2.
24

E
-0

5 
(0

.0
05

)*

2.
10

E
-

05
(0

.0
0)

*

2.
12

E
-0

5 
(0

.0
0)

*

2.
10

E
-

05
(0

.0
0)

*

IND

0.
05

 
(0

.0
04

)*

0.
63

 
(0

.0
14

0)
**

0.
07

 
(0

.0
56

9)
**

0.
63

 
(0

.0
01

)*

0.
04

 
(0

.0
0)

*

0.
06

 
(0

.0
4)

**

TOP

0.
77

 
(0

.1
05

8)
**

*

0.
90

 
(0

.1
36

4)
**

*

4.
41

E
-

05
(0

.8
5)

**
*

3.
57

E
-

05
(0

.9
6)

**
*

PD

0.
00

05
53

 
(0

.3
06

8)
**

*

9.
95

E
-

06
(0

.7
5)

**
*

C

-0
.1

9 
(0

.6
4)

**
*

-0
.1

4 
(0

.6
2)

**
*

-1
.4

 
(0

.0
02

0)
*

-0
.7

5 
(0

.2
35

6)
**

*

-0
.5

2 
(0

.4
30

6)
**

*

0.
23

 
(0

.0
00

)*

0.
25

 
(0

.0
00

)*

0.
11

 
(0

.0
00

)*

0.
98

 
(0

.0
0)

*

0.
13

 
(0

.0
06

)*

R2 0.86 0.93 0.93 0.93 0.93 0.13 0.21 0.29 0.29 0.29
No. of  obs. 156 156 156 156 156 156 156 156 156 156

Source: Research finings, 
Note: The values in the brackets are ‘P’ values and *, ** and *** representing the probability of  1, 5 and 10% respectively



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Table 3: Fixed effect (FE) and Random Effect
Dependent Variable: CO2-A Dependent Variable: CO2-B

Fixed Effect Random Effect Fixed Effect Random Effect
GDPC 0.000110     

(0.604199)
0.000434(6.326225)* 3.57E-05(5.695411)* 8.89E-06 

(2.1373)"
GDPC2 1.65E-09(0.767041) -2.77E-09(-2.918937)* 3.02E-10 

(4.077894)*"
4.31E-11(0.771595)***

EC 0.000761(4.684145)* 0.001099(11.23299)* 0.001373 
(1.530771)*"

0.002382(2.913815)*

IND 0.030351(1.164805) 0.035390(2.064045)** -0.000164 
(-3.556429)*"

-0.000111(-2.989369)*

TOP 0.001698(1.270241) 0.000543(0.940248) 1.02E-05 
(1.823426)*"

2.04E-05(4.136153)*

PD 0.012014(1.183707) 0.005945(1.406439)*** 0.000963 
(2.757383)*"

0.000458(1.6482)***

C 3.411923(1.972429) -0.548023(0.787295) 0.628890 
(10.56299)"

0.309056(6.7334)

R2 0.95 0.92 0.52 0.18
No.of  obs. 156 156 156 156

Source: Research findings
Note: The values in the brackets are ‘t statistics’ and *, ** and *** representing the probability of  1, 5 and 10% respectively

the pooled OLS, fixed effect, and random effect models 
were tried and the Hausman test was used to determine 
the most effective and reliable model. Additionally, GMM 
is also used to estimate the results of  this study. 
While evaluating the pooled analysis, in table 2, it is 
clearly seen the effect of  economic growth on pollution 
level of  the countries. The coefficients of  GDP and 
GDP2 are positively and negatively affecting the CO2-A, 
respectively, in all models (A to E). In terms of  GDP, 
the findings tell us, if  the country’s economic condition 
increases, the unwanted emissions will also be more. After 
that, with the increase of  further economic per capita, the 
pollution will be minimized. Which shows the validity of  
EKC presence. Moreover, these two coefficients (GDP 
and GDP2) are also significant. The coefficient values of  
the indicators can be seen from table 2. Moving towards 
energy consumption, the value of  EC has positive but 
significant effect on dependent variable in models ‘B 
to E’. Meaning that pollution will be increased with the 
usage of  energy. But the alternative source of  energy can 
diminish pollutants up to some level.
Also, like GDP per capita, we have the same results 
for industrial effects. With the upgradation of  energy 
sources and modern techniques, the industrial level can 
decrease pollution with significant effect. Whereas trade 
and population density have positive effects on the 
environment but are insignificant. By increasing these 
elements, the environmental degradation will be more. 
So, the countries need to manage these two issues as well.
On the other hand, CO2-B also has the same estimated 
results as CO2-A for all independent variables and the main 
thing is, here EKC curve also valid and significant. But the 
value of  the R2 is quite different in both CO2 measures.
In order to regulate the effective and operative model 

to include in our study (the fixed effects or the random 
effects), the Hausman test is examined. Basically, the 
Hausman test supports in determining whether the FE 
or the RE is appropriate for our regression investigation.
The null and alternative hypothesis of  the Hausman test 
is specified as follows: 
𝐻𝑜 = 𝑣𝑎𝑟(𝑏) ≠ 𝑣𝑎𝑟(𝐵): there is a correlation random 
effect 
𝐻𝑜 = 𝑣𝑎𝑟(𝑏) = 𝑣𝑎𝑟(𝐵): there is no correlation random 
effect 
The null hypothesis tells that there is an associated random 
effect which indicates that the random effect evaluations 
are favored to the fixed effects approximations, whereas 
the alternative hypothesis marks that there is no correlated 
random effect which shows that the fixed effects estimates 
are ideal to those of  the random effects evaluations. The 
rule of  thumb for the Hausman diagnostic test is that if  
the probability of  chi2 < 0.05, then FE is best model and 
on the other hand if  the probability of  chi2 > 0.05, then it 
is not significant and the null hypothesis is acknowledged 
while the alternative hypothesis is excluded, which means 
RE is good fitted.
Resulting in the use of  the Hausman test to know 
whether the FE or the RE method was more suitable, 
the outcomes state that the random effects assessments 
were more consistent than the fixed effects test in both 
pollution indicators (CO2-A andCO2-B). The result 
from our study has several economic suggestions in 
the investigated region. The FE and RE results of  both 
dependent variables are explained in table 3.
On the base of  random effects model, the significant 
negative coefficient of  income per capita squared 
(GDP2) variable shows a validation of  EKC theory in 
middle east and south Asia in CO2-A analysis, whereas in 



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CO2-B, the results are converse. Concluding that, there 
is no EKC proof  in CO2-B. Other variables also have 
opposite coefficient value in random effect model. Only 
the population density has positive value but insignificant, 
this is consistent with some extant studies in Africa 
region, for instance, (Rashid, 2009) Orubu & Omotor, 
2011)found the existence of  EKC for CO2 in Africa 
countries. 
On the other side, (Omojolaibi, n.d.)could not establish 
EKC for some selected countries. In case of  CO2-A, 
the significant positive coefficient of  income and the 
negative coefficient of  the income squared suggests that 
as GDP per capita rises, environmental deprivation is 
also growing, but a positive edge is reached when GDP 
per capita ranges a point and environmental ruin begins 
to decrease. The effect therefore exposed that there is a 
negative but significant connection between GDP2 and 
carbon emissions. Therefore, this result goes in line with 
the a priori belief  of  the EKC that as per capita income 
upsurges, pollution level falls.
Meanwhile, in terms of  CO2-B, both values of  GDP and 
square of  GDP are positive, which shows by increasing 
the amount of  per capita, the environment humiliation 
will be more. Resulting that, there is no EKC proof  in 
CO2-B. 
The indicator of  energy consumption (EC) has a positive 
significant affiliation with carbon emissions in Asia and 
middle east in both estimators. These results showed that 
as energy use increases, environmental degradation rises. 
This confirms our expectation which means, the more 
usage of  energy sources on commercial, and residential 
level will lead to degrade the environment. The indicator 
of  population density (PD) has a positive but insignificant 
bond with total CO2.
The result clarifies that as population density grows, 
carbon discharges rise, this result is also true with 
regarding prior prospects. It is projected that as the 
number of  people increases, there develops more burden 
on the present natural capitals which leads to a rise in 
smog. The purpose for this kind of  result could be 
that even though the population is rising in the region, 
the people do not involve much in industrial sector 
activities, but relatively they participate in agricultural 
actions which have slight pollution marks when related 
with industrial and manufacturing happenings. This 

result therefore indicates that people do not significantly 
influence environmental degradation in some Asian 
states. Similarly, trade openness (TOP) has positive and 
insignificant effects on both pollution variables. Meaning 
that, by increasing the trade activities, the environment 
will be worse. But due to insignificance, this result is non 
robust.
Lastly, the effect of  industrialization is negative and 
significant in CO2-B. The development in industrial 
level can reduce the emissions. But the significant impact 
tells us, the country should give more concentration to 
industry in developing friendly environmental projects. 
But in case of  CO2-A, the value of  coefficient is positive, 
which shows industry has harmful impact on environment 
quality. So, there must be more environmental regulation 
to avoid pollution in the industrial sector.
Finally, table 4 presents the results of  Generalized method 
of  moments (GMM). To begin with CO2-A, it is clearly 
seen that our regression supports the inverted U shape 
curve theory. As for the variables, GDP and GDP2 have 
positive and negative effect respectively. On the contrary, 
CO2-B does not confirm the EKC statement, as the 
value of  GDP2 has positive sign. The fact that carbon 
emissions are global pollutants, whereas the difference 
between two CO2s is about unit and weight may account 
for the difference in the turning points between the two 
types of  emissions. The literature review supports this 
discrepancy and its interpretation; for example, (Dinda, 
2004) and (Nahman & Antrobus, 2005)  summarized by 
saying that EKCs were more likely to hold for different 
global pollutants. All other explanatory variables have the 
same coefficients as table 3 except energy consumption, 
which has negative effect on both dependent variables. 
Hence, it shows, that more energy consumption will 
lead to push down the environmental scenario of  the 
society. It also suggests us, while using the right source 
of  energy production like the advanced energy resources 
can lead to minimize the pollution level. The PD found 
that the positive and significant relation in both cases, 
meaning that rise to global pollution in those regions for 
both pollutants. This finding has similarities with (Omri, 
2013). Lastly, trade has a positive and insignificant impact 
on environmental quality.(Zakari, 2015) established the 
impartiality hypothesis where no connection between 
CO2 production and trade was found.

Table 4: Generalized method of  moments (GMM)
Dependent Variable: CO2-A Dependent Variable: CO2-B

GDPC 0.000988(2.326196) * 5.21E-05 (4.821649)*
GDPC2 -1.08E-08(-2.767070)* 8.10E-10(3.623738)*
EC -0.001251(-6.089305) * -0.002473(-1.796982)***
IND 0.132222(1.7553)*** 0.000169(3.846192)*
TOP 0.002115 (2.302912)** 9.10E-06(0.56442)**
PD 0.017112(1.371170)** 0.001270(1.438960)**
Observations 104 104

Source: Research findings
Note: The values in the brackets are ‘t statistics’ and *, ** and *** representing the probability of  1, 5 and 10% respectively



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CONCLUSION
In this examination, the relationship between per capita 
income and environmental degradation in the Middle East 
and South Asia has been explored, utilizing longitudinal 
information spread between 1990 and 2020. 
According to the pooled OLS, RE and GMM the value 
of  GDP2 is negative and significant for CO2-A, whereas 
for CO2-B, the EKC does not exist in RE and GMM 
model. Thus, it proves the EKC theory is somehow valid 
in these regions. In addition to this, the value of  ‘P’ in 
the Hausman test is greater than five percent, which 
proves the random model is well fitted for our study. The 
country variable which interfaced with the pay variable to 
make the inverted shape EKC signals the significance of  
public establishments in natural security. The impact of  
several factors, for example, population density, energy 
consumption, trade openness, and industry on natural 
quality gives profession to mainstreaming the climate into 
the whole cycle of  anticipating advancement to guarantee 
ecological manageability in these states. 
After the examination of  these countries, it is noticed 
that there are some operational changes practiced in 
these republics and the policy makers must approve 
those legislative changes with the clean machinery. 
Policymakers must study technology, economy, and 
environment together and grip the official principles 
they will design accordingly. Consequently, individual 
environmental rules followed by states, linked to their 
own economic assemblies and cultural positions will have 
more influence in total. Environmental policies of  these 
nations should cover fresh technology, renewable energy 
resources and sincere environmental regulations. Finally, 
the advance policies to be useful in these countries need 
to be accomplished with a sustainable growth target 
considering the environmental targets carefully.

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