Energy Consumption, Energy Price, Energy Intensity Environmental Degradation, and Economic Growth Nexus in African OPEC Countries: Evidence from Simultaneous Equations Models

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 403-409

J. Environ. Treat. Tech.

ISSN: 2309-1185

Journal web link: http://www.jett.dormaj.com

Energy Consumption, Energy Price, Energy Intensity

Environmental Degradation, and Economic Growth

Nexus in African OPEC Countries: Evidence from

Simultaneous Equations Models

Umar Muhammad Dabachi , Suraya Mahmood , Ali Umar Ahmad *, Suraya Ismail , Ibrahim

Sambo Farouq , Aminu Hassan Jakada , Umar Aliyu Mustapha , Ahmad Tijjani Abdullahi ,

Abubakar Atiku Muhammad , Kamalu Kabiru

Faculty of Business and Management, University Sultan Zainal Abidin (UNISZA), Terengaennu Malaysia

Faculty of Social Management Science, Department of Economics, Bayero University Kano, Kano Nigeria

Received: 12/12/2019 Accepted: 20/01/2020 Published: 20/02/2020

Abstract

This paper investigated the causal relationships among environmental degradation, energy consumption, energy price, energy intensity,

and economic growth using simultaneous-equations models with panel data of OPEC African countries from 1970 through 2018. The study

used second-generation techniques to analyse the stationarity and co-integration relationship among the variables. The empirical results of

the research showed that there exists a bidirectional causal relationship between energy consumption and economic growth; and energy prices

and economic growth; and environmental degradation and economic growth. Moreover, the result revealed a unidirectional causal

relationship running from economic growth to energy intensity. Nevertheless, the findings show a unidirectional causality from energy

consumption to energy intensity with no effect of feedback, and there exists a bidirectional causal relationship between energy prices and

energy intensity; between environmental degradation and energy intensity; and between environmental degradation and energy prices for all

OPEC African countries. The study recommended that energy policies should identify the dissimilarities in the causal linkages between

economic growth and energy consumption to retain sustainable energy consumption in OPEC African countries.

Keywords: Energy Consumption, Economic Growth, Energy Intensity, Energy Price, Simultaneous-Equations Models

Introduction¹

for reform in the energy growth axis, however, will influence a

country's optimal energy policy. Energy saving in the

manufacturing, farming, retail, and housing industries may be a

priority if it helps to reduce energy bills, products and services

costs, and greenhouse gas emissions. Energy-Saving strategies

will lead to a better allocation of resources by moving labour and

capital from the sector of energy to a more productive sector.

Nonetheless, if a country's output is heavily dependent on oil,

energy conservation policies will restrict economic growth.

Policymakers, therefore, need to learn the causal relationship

between energy consumption economic growth. Four theories

have embodied the causal relationship between energy

consumption and economic growth (7), (3). The demand theory

suggests that energy usage explicitly and as a supplement to

labour and capital has an essential impact on the cycle of

economic growth (2). If one-way causality is identified between

energy usage and economic growth, the theory of growth is

For a decade, Africa had undergone a steady growth with at

an average 5 percent annual increase in GDP following many

global crises. There are seven out of the world's ten fastest-

growing economies in Africa. This fast growth results in higher

interest accrued to Africa and turns its image from a region of

civil wars, chaos, and poverty into a region of optimism and trade

and prosperity. In this study, we believe that one major issue

associated with this explosive growth involves energy and

contributes to the research by exploring the significance of energy

concerning Africa's economic growth. In reality, energy usage

promotes economic chances, lowers travel costs, and upgrades the

industrial sector contributing to urban transformation (21).

Energy has indeed been strongly linked to economic growth

because energy is an essential input in the cumulative output

process. Therefore, the relationship between economic growth

and energy policy is generally considered to be close. The scope

Correspondent Author: Ali Umar Ahmad, Faculty of Business

and Management, University Sultan Zainal Abidin (UNISZA),

Terengaennu Malaysia. Email: aliumaraha@unisza.edu.my.

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 403-409

supported. In this case, the energy dependency on the economic

performance of the relevant country is so strong that fluctuations

in the supply of energy would adversely affect economic growth.

Throughout the context of the growth hypothesis, the policies of

energy-saving can adversely affect economic growth. Well into

the situation of the growth model, so as a universality relationship

between power and capital has been believed, the impact of

technological innovations on energy efficiency can be so strong

in the long run that it leads to a decline in the energy reliance of

the productive process as capital usage increases.

emissions of carbon dioxide lead to about 2 over 3 of global

emissions CO . The overall amount of carbon emission due to the

energy sector keeps rising as the global economy grows.

Nevertheless, it challenges the quest for environmental protection

and viable economic growth, which is given as crucial to the

globe's long-term ambitions for economic and social development

as a whole. Such developments eventually lead to different

arguments about the importance of the rise in energy

consumption, especially from non-renewable origins to

developing nations ' growth. When part of climate change

mitigation and environmental pollution strategies, initiatives also

call for the replacement of non-renewable energy sources with

renewable energy. Empirical research of the interaction among

environmental degradation and economic growth in developing

economies is therefore crucial to their short and long-run energy

policies.

Literature Review

The 1971 fall of the Bretton Woods system and the 1973 first

oil shocks threatened some of the conventional macroeconomic

structures, such as the distribution process. Nonetheless, a global

recession preceded the sudden increase in oil prices due to the oil

embargo concerning the Organization of Petroleum Exporting

Countries (OPEC). Several ground-breaking studies were

conducted during this period (5, 6, 9, 16 and 22) to investigate the

correlation between oil price shocks and economic activity to test

whether the reported depression (the 1970s) was due to the 1973

oil shock. (18) is the most influential paper in this field; he argued

that oil price increases were at least partially responsible for every

post-second world war US recession except the one in 1960.

The above-mentioned ground-breaking experiments were

linked to the US economy in contrast to (18). These studies have

identified a relationship between US economic growth and oil

price movement. After these studies, a large number of studies

were conducted in various areas. Therefore, this study examined

the causal relationships among energy price, energy intensity,

energy consumption, and economic growth using simultaneous-

equations models with panel data of OPEC African countries.

However, (17) investigated the causality relationship between

trade openness, economic growth, and energy consumption. A

panel data analysis of Asian countries was used, with the

application of panel VECM, FMOLS, and DOLS. The inference

was drawn about the co-integration between economic growth,

trade openness, and energy consumption. While the FMOLS and

DOLS estimation analysis reveal a positive relationship between

energy consumption and income growth, energy consumption and

trade openness, whereas an inverse relationship between energy

consumption and energy prices is observed. Similarly, to examine

the effects of Trade Openness, Energy Consumption and

Economic Growth Relationship in Iran. (14) applied Bayer and

Hanck co-integration test, Vector Error Correction Model. The

findings of this study show the presence of co-integration

Likewise, (6) relate energy consumption, carbon dioxide

emissions, and economic growth using the South African

economy. The study of (5) combined co-integration approach,

(20) bounds test and Kripfganz and Schneider causality test. The

result indicated that a one-way causality existed from energy use

to economic growth, which validates the energy-led growth

hypothesis. Consequently, (23) found the relationship between

electricity use, real gross domestic product per capita, and carbon

emission in Zimbabwe. The study applied Zivot-Andrews, Maki

co-integration, DOLS, and Toda-Yamamoto causality test. There

exists a long-run positive relationship between electricity

consumption and real growth domestic product per capita, also a

one-way causality existed and running from electricity

consumption to the growth.

3 Acknowledgment Econometric Methodology

and Results

.1 Data and Descriptive Statistics

The study used yearly that covers the period of 1970–2018.

Gross Domestic Product growth as a proxy for Economic growth,

Energy Prices were calculated as a ration of Energy Consumption

(kg of oil equivalent per capita), Energy Intensity (MJ/$2011 PPP

GDP), and environmental degredation (CO emission per capita

metric ton) were collected from World Bank (2019) world

development indicators and Average annual West Texas

Intermediate (WTI) crude oil price (in U.S. dollars per barrel) and

Consumer Price Index (1) was collected from OECD (2019)

database. The trade openness, hibernation, foreign direct

investment, and financial development are the control variables in

this study and were sourced from World Bank (2019) world

development indicators. This study used the Solow development

model, which was first demonstrated by Mankiw, (17) on the eve

of Islam's (1995) in the panel data study. Consider the Cobb-

Douglas growth model as follow:

Where, is the output, is the capital is the labour force;

meanwhile, are (7), (14), (16), and (17), Hence, the current study

extends the equation (1) above by including energy consumption,

energy prices, and energy intensity, the following functional

form:

amongst the variables. The causality result showed

unidirectional relationship in the short run from energy

consumption to trade openness. Meanwhile, the long-run

relationship test showed the bidirectional causality between

economic growth and energy consumption, and between

openness and energy usage, also a unidirectional causality from

openness to economic growth was recorded.

Seeing as how the developed countries have experienced rapid

industrialization, economic development, and growth as a result

of heavy energy use for industrial and other economic activities,

it all seems and indicates that developing countries will employ

the same development models. As per the United Nations (3). Oil,

coal, and gas has driven the industrialization of the country but

휑

푖푡

1−푏

푖푡 푖푡

푌

푖푡

= 푊 (퐾 퐿 )

(ꢀ)

Where, 푌 is the output, 퐾 is the capital 퐿 is the labour force;

푖푡

푡

have also made

a tremendous contribution to economic

meanwhile, 푊 Are (17), (19), (24), and (4). Hence, the current

푡

development and social well-fare. As such, power-related

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 403-409

study extends the equation (1) above by including energy

consumption, energy prices, and energy intensity, the following

functional form:

distribution is assumed to be normally distributed. Also, if the

Skewness Coefficient is in a surfeit of unity, it is measured

relatively excessive, and a low (high) Kurtosis value reveals

excessive platykurtic (extreme leptokurtic). This showed that the

frequency distributions are not normal. The results of the

correlation matrix show that economic growth decrease along

with the energy prices and energy intensity, while energy

consumption, however, increases economic growth in the African

OPEC economies.

퐿푁푅퐺퐷푃_푖_푡= 푓(퐿푁퐸퐶 , 퐿푁퐸푃 , 퐿푁퐸퐼 , 퐿푁퐶푂2 )

(2)

푖푡

This study will transform all the variables into natural

logarithms to capture their elasticity value and set them free from

the problem of heteroscedasticity. The functional relationship

between energy consumption, energy prices, energy intensity, and

economic growth can be represented as follows:

3.2 Testing slope homogeneity Testing the cross-sectional

dependency/ Second Generation Panel Unit Root Test

퐿푁푅퐺퐷푃_푖_푡= 휃 + 휗 퐿푁퐸퐶 + 휗 퐿푁퐸푃 + 휗 퐿푁퐸퐼

The second issue in data analysis for the panel is determining

whether the slope parameters are heterogeneous or not. A robust

null hypothesis is a causality from one variable to another by

imposing the joint restriction on the whole panel (Granger, 2003).

Besides, the parameter homogeneity assumption is not capable of

capturing heterogeneity due to specific characteristics of the

region (Breitung, 2005). Also, after the slope of homogeneity is

the relationship dependency test. Cross-section dependency must

be tested once proceeding for further steps. Otherwise, outcomes

may be bias and contradictory (Breusch and Pagan, 1980;

Pesaran, 2004). Therefore, the presence of cross-section

dependence in the series and the equation of co-integration should

be checked before further studies.

푖푡

ꢁ

푖푡

휗₃퐿푁퐶푂2_푖_푡+ 휀_푖_푡

(ꢂ)

Where 퐿푁푅퐺퐷푃_푖_푡indicated the natural logarithms of real

gross domestic product, 퐿푁퐸퐶_푖_푡indicated the natural logarithms

of Energy Consumption, 퐿푁퐸푃 indicated the natural logarithms

푖푡

of Energy Prices, 퐿푁퐸퐼_푖_푡indicated the natural logarithms of

Energy Intensity, 퐿푁퐶푂2_푖_푡indicated the natural logarithms of

envronmental degradation, and is the error term with the

presumption that it has a normal distribution with zero mean and

predictable variance. The following tables, 1, represented the

descriptive and correlation analysis of the variables. The results

showed that the values of Kurtosis and Skewness show a lack of

symmetric in the distribution. In general, if the values of Kurtosis

and Skewness are 0 and three, respectively, the observed

Table 1: Descriptive and Correlation Analysis

LNGDP

1.425

LNEU

6.766

0.682

0.242

2.161

LNEP

1.733

1.628

4.741

25.46

LNEI

1.465

0.444

0.293

2.527

LNCO2

19.07

LNFD

2.525

0.728

-0.093

2.810

LNPOP

0.986

LNTOP

3.733

LNFDI

4.342

0.481

-0.321

2.658

Mean

Std. Dev.

1.056

2.260

0.365

0.585

Skewness -0.220

-0.913

3.709

-0.992

5.534

-0.421

4.158

Kurtosis

LNGDP

5.356

1.000

LNEU

LNEP

LNEI

0.045

0.398)

1.000

(

-0.171*

0.001)

-0.177*

(0.001)

1.000

(

-0.101

0.061)

0.078

(0.146)

0.018

(0.729)

1.000

(

LNCO2

LNFD

LNPOP

0.045

0.403)

0.109**

(0.044)

-0.140*

(0.009)

0.078

(0.148)

1.000

(

-0.143*

0.008)

0.135**

(0.012)

-0.082

(0.128)

-0.001

(0.982)

-0.413*

(0.000)

1.000

(

-0.145*

0.007)

-0.258*

(0.000)

0.164*

(0.002)

-0.036

(0.503)

-0.159**

(0.003)

-0.128**

(0.018)

1.000

(

LNTOP

LNFDI

0.230*

0.000)

0.445*

0.000)

(0.145*)

(0.007)

0.144630

0.0073

-0.308*

(0.000)

-0.219*

(0.000)

-0.223*

(0.000)

-0.332*

(0.000)

0.172*

(0.001)

0.218*

(0.000)

0.007

-0.063

1.000

(

(0.892)

0.388*

(0.000)

(0.243)

-0.346*

(0.000)

-0.222*

(0.000)

1.000

(

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 403-409

The presence of cross-sectional dependence between

countries is tested through the (6) LM test when the time

dimension exceeds the cross-sectional dimension. (24) has

improved this test if the time dimension is smaller than the cross-

section dimension, and the time dimension is larger than the

cross-section continuum. If the average group is zero, this test is

biased, but the average individual is distinct from zero. In the last

step to know the stationarities' property of the variables, the panel

unit root test must be taken.

There are two groups of panel unit root tests developed in the

literature. The first group includes first-generation unit root tests

that ignore cross-sectional dependence, while the second group

contains second-generation unit root tests that allow for cross-

sectional dependence (8) and (18). There are various methods for

the panel unit root test. This study chooses two-second generation

panel unit root tests such as CIPS test, and CADF test. The table

below showed the results of each test. As can be seen from Table

for more than one cointegration vector, but (10) assumes only one

cointegrating vector. (14) consider testing for cointegration under

the assumption that: for …., N the null hypothesis is for …N

against the alternative hypothesis that for a non-vanishing fraction

of cross-section members. This test statistic is parallel to that of

(20) and is known by a cantered and scaled version of the cross-

sectional average of the individual trace statistics. Denotes the

trace statistic for the null hypothesis of a k-dimensional

cointegrating space for the unit where the superscript s indicates

the specification of the deterministic components. Using the

central limit theorem in the cross-sectional dimension and the

appropriate mean and variance correction factors imply that under

the null hypothesis:

푁 ∑^ꢈꢅ퐿푅 ꢃ ⁄ ꢄ ꢆ 퐸(퐿푅 ꢃ ⁄ ꢄꢇ

ꢁ

푣 푘

푖

푥

√

푖ꢉ1 푖

푥

푆

푘

퐿퐿퐿 ꢃ ⁄ ꢄ =

→ 푁(ꢊ, ꢀ)

(4)

푥

푣 푘

푉푎푟 ꢅ퐿푅 ꢃ ⁄ ꢄꢇ

푖

푥

√

below, since the probability values of series and co-integration

equations are smaller than 0.05, H0 hypotheses are firmly

rejected, and it has been decided that there is cross-sectional

dependency among these countries. This revealed a significant

change in the series in one of the countries also affects the others.

Therefore, while the decision-makers in these countries set

their policies, they should take into consideration to policies of

the other countries and the other external factors. Furthermore,

since cross-section dependency determined, while choosing the

unit root and co-integration tests method, this situation should be

taken into account. Therefore, panel unit root tests and co-

integration analysis considering the cross-section dependency

have also been used. Results in Table 2 showed that series are

non-stationary at levels but become stationary at first differences;

they are said to the of the first order, I(1). In this case, it has been

concluded that the existence of a co-integration relationship

between these series can be tested since series under consideration

are integrated of the same order.

푣

ꢋ

In the serial limit 푄 → ∞ tailed by 푁 → ∞. 퐸(퐿푅_푖ꢃ ꢄ) and

ꢌ

^푉^푎^푟⁽^퐿^푅푖^푣( ) )) denote mean and variance of the asymptotic trace

ꢋ

ꢌ

statistics respectively found from

a stochastic simulation

푣

ꢋ

(

Johansen, 1995). For 푄 → ∞ the expressions 퐸(퐿푅_푖ꢃ ꢄ) and

ꢌ

^푉^푎^푟⁽^퐿^푅푖^푣( ) )) converge to the limit of the expected value and

ꢋ

ꢌ

variance of the trace statistic, respectively, equivalent to the case

v deliberated. For each country in the panel, the null hypothesis,

푟 = ꢊ, is tested using the observed trace statistic. If the null

hypothesis experienced, then the null hypothesis, 푟 = ꢀ, is tested.

This serial testing technique ends when the null hypothesis, 푟 =

푟 is not rejected, which determines the rank evaluation of r. For

푖

푣

ꢋ

determining the panel trace test, the statistic 퐿푅_푖ꢃ ꢄ, as noted in

ꢌ

Eq. (4), is obtained by standardising the average of the N

countries' trace statistics. If cointegration is present, the procedure

allows one to test whether the cointegrating vector is

homogeneous across countries.

.3 Larsson et al. (2001) Cointegration Test

This method employs this study to estimate the cointegration

between the variables. The (14) method is equivalents Johansen's

(

1988) methodology within a panel error correction model

VECM) framework. It has some advantages over the residual-

based test cointegration, such as(6) (9). The (14) procedure allows

Table 2: Cross-sectional Dependency Test, Testing of Slope Homogeneity and Second Generation Panel Unit Root Test

Variables

LNGDP

LNEC

LNEP

LNEI

LNCO2

Test of Homogeneity

CIPS Level

-1.710

-2.156

-0,460

-1.046

CIPS First Difference

-6.183*

CADF Level

-2.035

CADF First Difference

-5.816*

11.1*

5.91*

-2.93*

7.19*

8.10*

-6.183*

-3.887*

-5.685*

-5.333*

-1.874

-1.985

-1.459

-2.131

-5.100*

-8.343*

-4.174*

-6.211*

-0.013

39.51*

LM adj*

LM CD*

9.546*

6.876*

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 403-409

Table 3: Larsson’s Heterogeneous Panel Cointegration

Countries

ALGERIA

ANGOLA

CONGO

GABON

GUINEA

LIBYA

NIGERIA

LR_NT

LR_TEST

E(Z_k)

r=0

P values

0.021

0.000

0.131

0.385

0.011

0.000

0.019

r=1

26.03

19.78

24.48

16.77

20.43

44.51*

18.25

24.32

4.98

P values

0.127

0.438

0.180

0.656

0.393

0.000

0.547

r=2

10.51

4.99

P values

0.243

0.809

0.387

0.718

0.919

0.008

0.686

r=3

P values

0.352

0.681

0.803

0.498

0.420

0.171

0.660

51.57*

78.79*

43.05

0.863

0.169

0.061

0.458

0.650

1.869

0.193

0.609

-0.939

1.137

2.212

7.000

8.770

5.808

3.788

20.25*

6.078

8.601

2.064

6.068

10.54

7.000

36.21

54.09**

95.09*

52.20**

58.72*

12.19

27.73

45.264

7.000

14.96

24.73

7.000

Var(Z_k)

The results from the (14) cointegration test for African OPEC

countries were reported in Table 3 above. Since the test follows a

standard normal distribution, its 1%, 5%, and 10% critical values

are 58.72. The results suggested one cointegrating vector between

energy consumption, energy prices, energy intensity, and

economic growth at the 5% level of significance. Compared with

the Pedroni tests, (14) co-integration test provide stronger

evidence of cointegration. Therefore, the panel rank (LR) test

results reject the null hypothesis of no cointegration among the

variables. Given the presence of panel cointegration with one

cointegrating vector, the null hypothesis of a similar co-

integrating vector is tested. A panel of Table 3 revealed that the

null of homogeneous cointegrating vectors is rejected as the test

statistic, 58.72, exceeds the critical value of 43.964. Hence, the

LLL (2001) panel test for co-integration indicates an average

rank, r=0, between energy consumption, energy prices, energy

intensity, and economic growth. Therefore, this result is

suggesting that the determinants understudy contributed to the

development of African OPEC economies. This result is

consistent with the previous studies of (16).

퐿푁퐸퐼_푖_푡= 휃 + 휗 퐿푁퐺퐷푃 + 휗 퐿푁퐸퐶

푖푡

ꢁ

푖푡

+ 휗 퐿푁퐸푃 +휗 퐿푁퐹퐷퐼

푖푡

+ 휀_푖_푡

(7)

(8)

퐿푁퐶푂2_푖_푡

= 휃 + 휗 퐿푁퐺퐷푃 + 휗 퐿푁퐸퐶 + 휗 퐿푁퐸푃 +휗 퐿푁퐸퐼

푖푡

ꢁ

푖푡

ꢍ

푖푡

+ 휀_푖_푡

The simultaneous causality results in Table 4 below revealed

bidirectional running from energy consumption to economic

growth, energy prices to economic growth, from environmental

degradation to economic growth,. Also, the result showed a

unidirectional causal relationship running from energy intensity

to economic growth, and from energy consumption to

environmental degradtion. Furthermore, the finding indicated that

energy prices and energy intensity, environmental degradtion and

energy intensity, environmental degradtion and energy price have

a bi-direction causal relationship. However, there is no causal

relationship between energy consumption and energy intensity.

The findings supported the energy-led growth hypothesis and in

line with (5) and (15).

.4 Simultaneous Equations Causality Test

11) developed the non-causality test in heterogeneous panel

Conclusion and Policy Implications

The present study investigates the five-way linkages between

(

data models with fixed coefficients. In the structure of a linear

autoregressive data generating procedure, the augmentation of

standard causality tests to panel data suggests testing cross-

sectional linear restriction on the coefficients of the model. The

utilisation of cross-sectional data may broaden the data set on

causality from an offered variable to another. The Simultaneous

Equation Causality Test Estimates are:

energy consumption, energy prices, energy intensity,

environmental degradtion, and economic growth using the Cobb–

Douglas production function. While the literature on the causality

links between emissions-energy-growth has increased over the

last few years, no study examines this interrelationship via the

simultaneous equation models. The objective of the present study

is to fill this research gap by examining the above interaction for

African OPEC countries over the period 1970-2018. Our results

퐿푁푅퐺퐷푃_푖_푡= 휃 + 휗 퐿푁퐸퐶 + 휗 퐿푁퐸푃

푖푡

ꢁ

푖푡

suggest that energy consumption and energy prices enhance

economic growth. This shows a bi-directional effect. Thereby

rejecting the neo-classical assumption that energy is neutral for

growth. This pattern is similar to the findings of Oh and (19), (7),

(17), and (2).

Thus, we conclude that energy is a determinant factor of the

GDP growth in these countries, and, therefore, a high level of

economic growth leads to a high level of energy demand and vice

versa. As such, it is essential to take into account their possible

adverse effects on economic growth in establishing energy

conservation policies.

휗 퐿푁퐸퐼 +휗 퐿푁푈푅퐵 + 휗 퐿푁퐶푂2

푖푡

ꢍ

푖푡

^휀푖푡

(4)

(ꢎ)

(6)

^퐿^푁^퐸^퐶푖푡 = 휃 + 휗 퐿푁퐺퐷푃 + 휗 퐿푁퐸푃

푖푡

ꢁ

휗 퐿푁퐸퐼 +휗 퐿푁푇푂푃

^휀푖푡

푖푡

ꢍ

푖푡

^퐿^푁^퐸^푃푖푡 = 휃 + 휗 퐿푁퐺퐷푃 + 휗 퐿푁퐸퐶 + 휗 퐿푁퐸퐼

푖푡

ꢁ

푖푡

^휗3^퐿^푁^퐹^퐷

푖푡

^휀푖푡

Journal of Environmental Treatment Techniques

2020, Volume 8, Issue 1, Pages: 403-409

Table 4: Simultaneous Equations Causality Results

LNEC ꢆ/→ LNGDP LNEC ←/ꢆ LNGDP

Z-Statistics

P-value

-3.42*

0.001

4.50*

0.000

LNEP ꢆ/→ LNGDP

LNEP ←/ꢆ LNGDP

Z-Statistics

P-value

3.81*

0.000

-3.91*

0.000

LNEI ꢆ/→ LNGDP

LNEI ←/ꢆ LNGDP

Z-Statistics

P-value

1.47

0.142

3.19*

0.000

LNEC ꢆ/→ LNEP

LNEC ←/ꢆ LNEP

Z-Statistics

P-value

3.45*

0.000

3.292*

0.000

LNEC ꢆ/→ LNEI

LNEC ←/ꢆ LNEI

Z-Statistics

P-value

0.490

0.624

3.88*

0.000

LNEI ꢆ/→ LNEP

LNEI ←/ꢆ LNEP

Z-Statistics

P-value

3.59*

0.000

2.01**

0.044

LNCO

-6.54*

0.000

ꢆ/→ LNGDP

LNCO

7.11*

0.000

LNCO

5.98*

0.000

←/ꢆ LNGDP

←/ꢆ LNEC

ꢆ/→ LNEP

Z-Statistics

P-value

LNCO

ꢆ/→ LNEC

Z-Statistics

P-value

-0.33*

0.743

LNCO

ꢆ/→ LNEP

LNCO

4.78*

0.000

Z-Statistics

P-value

-7.76*

0.000

LNCO

ꢆ/→ LNEI

LNCO

7.64*

0.000

ꢆ/→ LNEI

Z-Statistics

P-value

-5.15*

0.000

Our empirical results also show that there is a unidirectional

causal relationship from energy consumption to energy intensity

without feedback. This implies that due to the expansion of

production, the countries are consuming more energy, which puts

pressure on the environment leading to more emissions. Hence, it

is essential to apply some sorts of pollution control actions to the

whole panel regarding energy consumption.

The policies with which to tackle environmental pollutants

require the identification of some priorities to reduce the initial

costs and efficiency of investments. Reducing energy demand,

increasing both energy supply investment and energy efficiency

can be initiated with no damaging impact on the African OPEC’s

economic growth and therefore reduce emissions. At the same

time, efforts must be made to encourage industries to adopt new

technologies to minimise pollution. Finally, given the generous

subsidies for energy in the exporting countries, relatively there is

more scope for more drastic energy conservation measures with

not much effects on economic growth in these countries. Indeed,

it is unlikely that the elimination of energy price distortions

restrains economic growth in the oil-exporting countries.

However, subsidy reform should for in a reform program that

engenders broad support and yields widespread benefits.

It is found that bidirectional causality between economic

growth and energy intensity emissions implies that degradation of

the environment has a causal impact on economic growth, and a

persistent decline in environmental quality may exert a negative

externality to the economy through affecting human health, and

thereby it may reduce productivity in the long run. The main

policy implications emerging from our study are: First, these

countries need to embrace more energy conservation policies to

reduce energy intensity emissions and consider strict

environmental and energy policies. The research and investment

in clean energy should be an integral part of the process of

controlling carbon dioxide emissions and find sources of energy

to oil alternatives. These countries can use solar energy as a

substitute for oil. Thus, implementing energy and environmental

policies and also reconsidering strict energy policies can control

carbon dioxide emissions. As a result, our environment will be

free from pollution, and millions of peoples can protect

themselves from the effects of natural disasters. Second, high

economic growth gives rise to environmental degrading, but the

reduction in economic growth will increase unemployment.

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