A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from Selected Middle East and North Africa Countries

Abstract

The paper investigated cross-cutting issues relating to renewable energy, carbon-emission and economic growth for a group of 8 MENA countries covering the period 1990-2018. Adopting a modified linear Cobb-Douglas production function, the study adopted the Fully-Modified and the Dynamic OLS estimation technique in examining the aforementioned relationship. Findings from the panel FMOLS and DOLS for the region confirm that a significant relationship exists between CO2 emission and economic growth and that renewable energy consumption triggers a significant effect on economic growth as well. Conversely, the panel of the FMOLS result reveals that while economic growth reacts positively from the effect of CO2 emission, CO2 emission reacts negatively from the effect of renewable energy consumption, as against the positive outcome between renewable energy consumption and CO2 emission as reported by the DOLS. This goes to point out that most economies within this region are yet to uncover best and appropriate policies which can control the regulation of renewable energy prices, that can help take into consideration the stability in economic growth structure and at the same time, mitigate the emission of Greenhouse Gases (GHG).

Keywords: Non-renewable resources, Renewable resources, Economic growth, Environment, Pollution

JEL Classifications: L72, Q20, O40, R11, Q52

DOI: https://doi.org/10.32479/ijeep.10074

Full text

International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020
440
International Journal of Energy Economics and
Policy
ISSN: 2146-4553
available at http: www.econjournals.com
International Journal of Energy Economics and Policy, 2020, 10(6), 440-450.
A Multivariate Analysis between Renewable Energy, Carbon
Emission and Economic Growth: New Evidences from Selected
Middle East and North Africa Countries
Owen Aor Maku*, Promise Oghenevwede Ikpuri
Department of Economics, Delta State University, Abraka, Nigeria. *Email: makuowen@gmail.com
Received: 12 June 2020 Accepted: 07 September 2020 DOI: https://doi.org/10.32479/ijeep.10074
ABSTRACT
The paper investigated cross-cutting issues relating to renewable energy, carbon-emission and economic growth for a group of 8 MENA countries
covering the period 1990-2018. Adopting a modied linear Cobb-Douglas production function, the study adopted the Fully-Modied and the Dynamic
OLS estimation technique in examining the aforementioned relationship. Findings from the panel FMOLS and DOLS for the region conrm that a
signicant relationship exists between CO2 emission and economic growth and that renewable energy consumption triggers a signicant eect on
economic growth as well. Conversely, the panel of the FMOLS result reveals that while economic growth reacts positively from the eect of CO2
emission, CO2 emission reacts negatively from the eect of renewable energy consumption, as against the positive outcome between renewable energy
consumption and CO2 emission as reported by the DOLS. This goes to point out that most economies within this region are yet to uncover best and
appropriate policies which can control the regulation of renewable energy prices, that can help take into consideration the stability in economic growth
structure and at the same time, mitigate the emission of Greenhouse Gases (GHG).
Keywords: Non-renewable Resources, Renewable Resources, Economic Growth, Environment, Pollution
JEL Classications: L72, Q20, O40, R11, Q52
1. INTRODUCTİON
Climate change has been attributed to the massive use of
polluting energy sources (fossil fuels) in recent times. This
change caused unwittingly several eects on human and natural
condition. If Greenhouse gases (GHG) emissions continue its
upward trajectory, it will further global warming and long-lasting
changes in all components of climate arrangement. The carbon
emissions growth rate has generated several issues relating to the
health of the population and on the quality of the environment
(Jebli, 2016). The impact of emissions on environmental quality
has remained a topical issue developed by series academic and
scientic researchers (UNFCCC, 2014). The World Bank has
played essential roles in supporting eorts to declining pollution
rate and endorsed low level of emissions growth. The eorts of
the World Bank are mainly focused on enhancing countries to
use clean energy generation by giving nancial incentives (World
Bank, 2013). It is relevant to note that the Middle East and North
Africa (MENA) region has around 57% of the world’s proven oil
reserves and 41% of proven natural gas reserves (Menichetti, et al.,
2018). About 85% of all GHG emissions in this region are mainly
derived from energy produced and consumed. CO2 emissions
(measured in Millions kilotons) has increased largely in MENA
countries since 1980 (Figure 1). The associated environmental
problems are aggravated through heavy subsidies on petroleum
products which promote excessive and inecient use of fossil-
fuels (Farzanegan and Markwardt, 2012).
In this perspective, energy subsidies in the 20 largest non-OECD
countries stretched to ($ 310*1012) in 2007. Eleven (11) countries
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Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020 441
out-of-the-total of 20 countries in the world that nancially
supported the gasoline consumption were from the MENA
region (IEA, 2008; Brown 2011). As assessed by the World
Bank (2012), fuel subsidies alone are 2 to 7.5 times larger than
the public spending on health in Morocco, Yemen and Egypt. In
2007, Iran was the largest fossil fuel subsidizer in the world with
($ 56*1012) per year, followed by Russia with ($ 51*1012) per
year. Venezuela, Saudi Arabia, China, Egypt, India, Indonesia, and
Ukraine represent the other large subsidizers, with annual subsidies
exceeding ($ 10*1012) yearly (IEA, 2008); a reection that under-
pricing of petroleum products in the MENA region is considerable.
According to the World Bank (2012), the price gaps between the
price of gasoline in Yemen, Bahrain, Egypt, Saudi Arabia, Iran,
Kuwait, Libya, Qatar and Algeria and the average world price of
gasoline were 81%, 90%, 62%, 95%, 58%, 87%, 97%, 89% and
77% per liter in 2008. The mammoth subsidies distort the price-
system and cause inecient allocation of resources. The towering
energy-intensity of production and use of fossil-fuels represents a
natural signicance of such subsidies (Farzanegan and Markwardt,
2012). The existence of cheap-energy impedes investment in
clean-technology and energy ecient means of transportation
(Ellis 2010; Moltke et al., 2004). The IEA, 2010 emphasizes that
the removal of fuel subsidies remains the crux for the overall
mitigation of climate change for the MENA region. According to
the Carbon-Dioxide Information Analysis Center (CDIAC, 2011),
six Middle Eastern countries ranked among the top twenty emitting
nations based on CO2 per capita in 2011: Qatar (1), Kuwait (4),
Oman (7), UAE (9), Saudi-Arabia (10) and Bahrain (11), (global
ranking in parentheses). The MENA region dependence on oil and
gas, as well as their energy-intensive industrial projects which
promote the use of domestically produced hydrocarbons; has
left an ineaceable mark on the region’s carbon footprint. These
problems have signicantly risen since the 1960s side-by-side
rapid rates of energy-intensive industrialization, urbanization and
rising living standards (World Bank, 2016).
The drive for sustainable development is therefore urgently needed
for all MENA countries. On one hand, energy used in economic
activities may enable such social and economic development, but
on the other hand, can have negative impact on the environment
resulting to climate changes at the global scale (Alshehry
and Belloumi, 2017). Conventional energy consumption may
contribute to the relation between CO2 emission and economic
growth via two channels. Conventional energy use may lead to an
increase in economic activities, and at the same time, aect CO2
emission positively. The replacement of a part of conventional
energy by renewable energy can trigger the negative eects caused
by the overuse of fossil fuels in MENA countries. Based on the
above premise, this study attempts to ll the gap by examining the
cross-cutting relationship between economic growth, renewable
energy consumption and CO2 emissions using a modied Cobb-
Douglas production function which is expanded to include the
energy component as an additional production factor as developed
by Ismail and Mawar, (2012).
The MENA region is chosen for two basic premise that,
environmental quality has worsened in the recent decades in
this region due to the extensive use of fossil fuels. Most of the
MENA countries use hugely fossil fuel energy without taking
into account the necessary preconceptions to avoid the growth
of CO2 emissions. Quite a number of indicators are directly
correlated with CO2 emissions growth, and it is imperative to
look for the input of these variables in the progress of emissions.
Renewable energy resources (mainly solar and wind energies) are
important in MENA countries that can be harnessed to overcome
environmental pollution in the region, and even in the world.
Compared to the previous studies in the region, this study considers
the case where renewable energy is used for production. The
empirical analysis employs the FMOLS and DOLS estimation
technique developed by Kao and Chang (2001) in a bid to generate
unbiased and consistent long run estimates. The other sections of
this paper are organized as follows; section 2 discusses relevant
literature, while section 3 presents highlights the econometric
methodology. In section 4, we present the results and discussion,
while section 5 concludes the study and provides relevant policy
recommendations.
1.1. Renewable Energy in the MENA Region
Most of the region’s greenhouse gas (GHG) emissions are largely
linked to the region’s role as an energy producer. IEA (2018)
estimates total GHG-emissions from fuel combustion in MENA
was equal to 1.860 million metric tons of CO2 equivalent in 2008,
accounting for 6.3% of the global emissions. By 2010, emissions
from the region’s power sector were estimated to have risen to
2.101 million metric tons of CO2 equivalent (World Bank, 2012).
As reported from Table 1, renewable electricity net consumption
has not been stable within this period (1980-2018), while the per
capita CO2 emissions varies around 50 million metric tons per
capita. For some countries, the consumption of electricity has
the tendency to rise across time such as Egypt and Iran. Egypt
is said to be largest consumer of electricity adopting renewable
energy with Iran as the second. Their respective annual averages of
electricity net consumption are 14.19% and 11.38% respectively.
Saudi Arabia, Qatar and Oman are the three smallest consumers
of electricity with 0.062%, 0.055% and 0.003% respectively.
Indeed, Qatar and UAE are the two biggest in per capita CO2
emissions from the energy consumption. Their annual averages
of CO2 emissions from the consumption are 46.05% and 27.57%
respectively. We thus conclude that if the use of renewable energy
increases, the rate of per capita CO2 emissions will decrease. One
of the solutions proered in the sustainability and improvement of
the energy market is the use of renewable energy. But the pressing
0
500000
1000000
1500000
2000000
2500000
3000000
1980
1983
1986
1989
1992
1995
1998
2001
2004
2007
2010
2013
2016
CO2 emissions (million
metric tons)
Figure 1: The evolution of CO2 emission (million metric tons) in
MENA region over the period 1980-2018

Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020
442
challenge is how to harness it; and how to turn the economy in this
region into a sustainable path. The Intergovernmental Panel on
Climate Change (IPCC, 2011) reveals that the relatively share of
renewable energy can be attributed not only from a single resource,
but to the deployment of a number of renewable resources. As with
the rest of the global community, MENA’s rich-endowment of
renewable energy resources far exceeds its annual energy needs. In
2010, the region’s energy demand was approximately 1,121 TWh.
By 2050, this demand is approximately projected to reach 2,900
TWh (Fichtner, 2011). But only recently, renewable resources
across the region have been accorded priority. Governments of the
MENA countries make eorts to use this potential in order to acquire
additional technological improvements, cost reductions, and the
adoption of favorable policy regimes. The use of renewable energy
(hydro, wind, biomass, geothermal, and solar) seems the greatest
solution to reduce the severity of the environmental problems, to
ensure the improvement of social-welfare, and to innovate and
advance the green-technology of the industrials rm’s payos.
2. LITERATURE REVİEW
Few studies have focused on the connection between renewable
energy consumption, economic growth and CO2 emissions
(Sadorsky, 2009; Apergis et al., 2010; Menyah and Wolde-Rufael,
2010). Sadorsky (2009) estimates an empirical model of renewable
energy consumption, oil prices and CO2 emissions for the G7
countries from 1980 to 2005 using Panel Vector Error correction
Model (VECM). The Panel cointegration techniques estimates
show that in long term, GDP per capita and emissions are the two
major-drivers behind renewable energy per capita. In the short
run, variations in renewable energy consumption per capita are
driven essentially by movements back to long run equilibrium as
opposed to short run shocks. In other works, Apergis et al. (2010)
examined the causal relationship between CO2 emissions, nuclear
energy, renewable energy, and economic growth for a pool of 19
developed and non-developed countries for the period, 1984-2007.
They nd a long run relationship between emissions and renewable
energy consumption. Whereas, results from the panel Granger
causality test suggests that renewable energy consumption does
not contribute to reducing CO2 emissions in the short run. In the
same way, Menyah and Wolde-Rufael (2010) explore the causal
relationship between CO2 emissions, nuclear energy consumption
and renewable and real GDP for the United States for the period
1960-2007. The empirical result supports a uni-directional and
negative causality running from nuclear energy consumption to
CO2 emission and proves that nuclear energy consumption can
help ameliorate CO2 emissions.
Table 1: Total renewable net electricity consumption (billion kilowatt-hours) and CO2 emission from the consumption of
energy (million metric tons per capita) for MENA countries
Countries 1990 1995 2000 2005 2010 2015 2016 2017 2018
Algeria
ELEC 0.135 0.193 0.054 0.555 0.182 0.222 0.336 0.579 0.730
CO22.088 3.314 2.830 3.236 3.313 3.846 3.979 4.113 4.247
Egypt
ELEC 9.953 11.192 14.259 13.155 14.389 15.620 16.133 16.120 16.958
CO21.353 1.536 2.053 2.214 2.449 2.155 2.045 1.934 1.823
Iran
ELEC 7.381 8.323 3.818 14.519 10.472 13.512 15.713 17.561 11.191
CO23.734 4.442 5.672 6.720 7.769 8.490 8.638 8.786 8.934
Iraq
ELEC 4.650 7.120 3.197 5.750 3.615 2.603 3.429 2.233 3.165
CO22.738 3.690 3.083 4.217 3.772 5.204 5.380 5.557 5.734
Israel
ELEC 0.003 0.025 0.033 0.039 0.170 1.346 1.838 1.840 2.038
CO27.789 9.215 9.582 8.218 9.035 7.573 7.139 6.705 6.270
Morocco
ELEC 1.220 0.611 0.782 1.171 4.127 4.410 4.657 4.635 6.484
CO20.949 1.125 1.178 1.503 1.730 1.747 1.731 1.715 1.699
Oman
ELEC 0.000 0.000 0.000 0.000 0.000 0.004 0.004 0.009 0.014
CO26.283 7.212 9.654 11.904 15.591 15.031 14.541 14.051 13.561
Qatar
ELEC 0.000 0.000 0.000 0.000 0.000 0.121 0.123 0.124 0.124
CO224.722 61.914 58.619 57.006 39.060 42.297 42.954 43.611 44.268
Saudi Arabia
ELEC 0.000 0.000 0.000 0.000 0.004 0.129 0.129 0.142 0.155
CO211.445 16.908 14.370 17.111 17.610 19.601 19.991 20.381 20.771
U.A.E
ELEC 0.000 0.000 0.000 0.000 0.018 0.309 0.338 0.539 0.954
CO228.445 29.250 35.916 25.314 18.809 33.973 24.234 25.495 26.756
MENA
ELEC 15.321 18.757 10.860 24.203 17.987 20.311 25.231 26.899 23.214
CO23.594 4.148 4.671 5.283 5.885 6.271 6.365 6.459 6.553
Source: International Energy Agency, 2019

Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020 443
Bhattacharya et al. (2017) suggest that, from 85 developed
and developing countries, both renewable energy deployment
and institutions play a signicant role in stimulating economic
growth and reducing CO2 emissions. For a panel of twenty-
veselected African countries, Zoundi (2017) recommend that
CO2 emissions are found to increase with income per capita. Ito
(2017) suggest that, for a panel of forty-two developed countries,
non-renewable energy consumption leads to a negative eect
on growth for developing countries. In the long-run, renewable
energy consumption positively contributes to economic growth.
Previous studies have been examined in order to highlight the
contribution of each variable to the evolution of CO2 emissions,
but by considering dierent sets of variables under consideration.
In previous empirical studies, dierent statistical approaches and
econometric methods are used (two steps generalized method of
moments (GMM), xed eect regression, PVAR, autoregressive
distributed lag (ARDL) model, Granger causality, etc.) either for
the case of panel or time series. From previous studies, the ndings
are dierent and depend mainly on the methodologies, periods,
sample sizes and countries. The directions of both long and short-
run causalities among the variables have been examined in many
studies. Table 2 summarizes some previous empirical studies
and presents their contributions according to the methodology,
variables, samples and the period used, which are discussed under
growth pollution nexus and renewable energy pollution nexus.
2.1. Growth-pollution Nexus
For the case of Algeria, Bouznit and PabloRomero (2016)
considered the ARDL approach to examine the validity of the
Environmental Kuznets Curve (EKC) hypothesis over the period
1970–2010. The results showed that the EKC hypothesis is thus
validated and that increasing economic growth in Algeria has
increased emissions. Ahmad and Du (2017) adopted the ARDL
bound approach to investigate the dynamics existing between
energy production, CO2 emissions and economic growth in Iran.
Although the production of energy positively has contributed to
economic growth, CO2 emissions are positively linked to economic
growth. Adopting a dynamic-panel model based on the GMM
technique, Jalil (2014) investigated the determinants of CO2
emissions in 18 MENA countries for the period 1971-2009. Their
results showed that real GDP, fossil fuel energy consumption, FDI
and agriculture production had signicant eects on CO2 emissions.
2.2. Renewable Energy–pollution Nexus
Various empirical studies have critically examined the role
renewable energy consumption may contribute in mitigating
CO2 emissions in the world. Empirical studies have found that
renewable energy use can decrease in CO2 emissions. Table 3a
and b reports some studies that investigated the renewable
energy–pollution nexus. Apergis and Payne (2014) examine the
determinant of renewable energy for a panel of seven Central-
American countries. The results from their estimation suggest
that a long run relationship exists between carbon emissions
per capita, renewable energy consumption per capita, real
coal prices, real GDP per capita and real oil prices with the
respective coecients statistically signicant. Jebli and Youssef
(2015) employed the Granger-causality test and the panel
cointegration approach for a group of North-Africa countries
for the period 1971-2008. Their ndings suggest the existence
of a unidirectional short-run causality running from renewable
energy consumption to CO2 emissions. For a panel data set of
seventeen OECD countries, Bilgili et al. (2016) use panel DOLS
and FMOLS estimations. The results revealed that renewable
energy consumption yields negative impact on CO2 emissions.
Bölük and Mert (2015) use the ARDL approach to examine the
potential of renewable energy sources in reducing the impact of
GHG emissions in Turkey. The results show that the coecient of
electricity production as generated from renewable sources with
respect to CO2 emissions is negative and statistically signicant
in the long-run.
3. MATERIALS AND METHODS
The study takes a step further to investigate empirically the
relationship between renewable energy, carbon emission and
economic growth; evidenced for a balanced panel of 8 MENA
countries for the period 1990-2018, generated from the World
Bank (2019) database and the BP statistical Review of World
Energy (2019) database. Data used are for the variables per
capita GDP (constant 2010, PPP), a proxy for economic
growth, CO2 emission per capita (metric tons per capita) and
renewable energy consumption (REW), expressed as the share
of consumption from renewable energy sources in total nal
energy. All the variables are transformed into natural logarithm
so as to obtain unbiased and consistent results by overcoming
the heteroscedasticity problem among the variables (Vogelvang,
2005; Shahbaz et al., 2012; Salahuddin et al., 2015). The 8 MENA
economics included in the sample are: Algeria, Egypt, Iran,
Israel, Jordan, Lebanon, Morocco and Tunisia. These countries
were selected based on data availability on the variables on
interest.
Table 2: Estimated renewable electricity potential (TWh
per year) for MENA countries
Countries Year Solar Wind Geothermal,
biomass and
others
Hydro
Algeria 2010 0.009 0.000 0.000 0.173
2018 0.603 0.01 0.000 0.117
Egypt 2010 0.025 1.409 0.000 12.954
2018 1.035 2.438 0.000 13.483
Iran 2010 0.0007 0.208 0.010 10.252
2018 0.037 0.361 0.021 10.77
Iraq 2010 0.000 0.000 0.000 3.615
2018 0.057 0.000 0.000 3.107
Israel 2010 0.07 0.008 0.061 0.031
2018 1.793 0.105 0.115 0.024
Kuwait 2010 0.000 0.000 0.000 0.000
2018 0.088 0.017 0.000 0.000
Morocco 2010 0.0001 0.692 0.000 3.467
2018 0.950 3.840 0.000 1.693
Oman 2010 0.000 0.000 0.000 0.000
2018 0.013 0.000 0.000 0.000
Qatar 2010 0.000 0.000 0.000 0.000
2018 0.009 0.000 0.114 0.000
Saudi Arabia 2010 0.004 0.000 0.000 0.000
2018 0.154 0.000 0.000 0.000
U.A.E 2010 0.018 0.000 0.000 0.000
2018 0.946 0.0007 0.005 0.000
Source: IEA, 2019

Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020
444
The model to be estimated is succinctly hinged on the simple
Cobb-Douglas production framework, which is shown to be a
function of capital (K) and Labour (L), written as;
YfKL=
(,) (1)
Previous studies (Ismail and Mawar, 2012) included energy, N,
as the third factor of production function, thus equation (1) is
augmented to be;
YfKNL=
(,
,)
(2)
For modeling purposes, this paper adopts a Cobb-Douglas
production function;
YK NL

**
(3)
Where β, θ and η, represents output elasticity to changes in capital,
energy and labour; where β+θ+η=1. Converting equation (1) into
logarithm, the empirical equation is modeled thus;
2
ln ln ln
ln ln
it
pcap i i it i it
i it i it it
GDP c K L
E CO u
αβ
λϖ
=++
++ +
(4)
Where In GDPpcapit represents gross domestic product per capita; In
Kit represents capital formation; In Lit represents labour participation;
In Eit represents renewable energy; In CO2it represents per capita
Greenhouse gas emission; uit represents the error term assumed
to be normally distributed with zero mean and constant variance.
4. EMPIRICAL RESULTS
The analysis begins with the summary statistics of variables used
in the sample of 8 MENA countries which is presented in Table 4.
Then we investigate the variables time series plots (in logarithm
form) for each country.
Figure 2 shows the time plots of renewable energy consumption
for each of the countries. On the average, Morocco is the biggest
Table 3a: Summary of related studies
Authors Sample Period Estimation technique Findings
Regional studies
Wang (2012) 98 countries 1971-2007 Dynamic panel
threshold model
EKC is supported. Economic growth negatively aects CO2
emission
Farhani and Rejeb
(2012a)
15 MENA
countries
1973-2008 Panel cointegration
methods and panel
cointegration
No causal link between GDP and energy consumption and
between CO2 emission and energy consumption in the short
run. In the long-run, there is a uni-directional causality
running from GDP and CO2 emission to energy consumption
Arouri et al.
(2012)
12 MENA
countries
1981-2005 Unit root test and
cointegration techniques
Energy consumption had a positive and signicant eect on
CO2 emission. Economic growth had a positive impact on
CO2 emission
Apergis and Payne
(2014)
7 Central
American
countries
1980-2010 Panel cointegration with
structural breaks
CO2 emission aects renewable energy consumption
Jalil (2014) 18 MENA
countries
1971-2009 GMM Gross domestic product (GD), energy consumption,
foreign direct investment and agricultural production have
signicant eect on CO2 emissions in the region
Saidi and
Hammami (2015)
58 countries 1990-2012 Dynamic panel data
model with GMM
All variables exhibited positive and mostly signicant
impact on energy consumption in all four panels
Al-mulali et al.
(2016)
18 Latin America
and Caribbean
countries
1980-2010 KAO panel
cointegration test,
FMOLS, VECN granger
causality test
EKC between GDP and CO2 supported. Energy
consumption had no long run eect on CO2
Salahuddin et al.
(2015)
Six Gulf
Cooperation
Council (GCC)
countries
1980-2012 DOLS, FMOLS,
dynamic xed eect,
panel granger causality
test
Electricity consumption and economic growth have a
positive long run relationship to CO2
Jebli and Youssef
(2015)
North African
countries
1971-2008 Panel cointegration
approach and Granger
causality test
Short-run unidirectional causality running from renewable
energy to CO2 emission
Magazzino (2016) 10 Middle East
countries
1971-2006 Panel VAR For 6 countries, the eect of CO2 emission on growth
is negatively related. CO2 emission is driven by energy
consumption. CO2 emission and energy consumption have
no impact on growth in the remaining four countries
Apergis (2016) 15 countries 1960-2013 Panel, time series and
time-varying approaches
of cointegration
EKC hypothesis holds in 12 out of the 15 countries
Kais and Mbarek
(2017)
3 North African
countries
1989-2012 Panel cointegration test
and panel VECM
Unidirectional causality running from economic growth to
CO2 and also from energy consumption to CO2 emission
Bhattacharya et al.
(2017)
85 developed and
develop countries
1991-2012 System GMM and fully
modied OLS
Renewable energy sources play a signicant role for CO2
emission
Source: Compiled by Author

Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020 445
renewable consumer, followed by Tunisia and Algeria is the least
consumer of renewable energy. However, most of the countries still
have undulating trends of renewable energy consumption. Figure 3
show the time series plots of GDP per capita for each country. In
fact, most countries have experienced increased GDP per capita
for the period under study. Israel has the biggest GDP per capita
size, followed by Algeria, while Morocco is at the bottom of the
ladder. Figure 4 shows the time series plot of carbon emission per
capita. On the average, Israel has the highest CO2 emission overt
the period, followed by Iran, while Morocco is at the tail end of
the emission ladder.
Table 5 shows the average annual growth rates for each variable
over the period 1990-2018. We can deduce that the annual growth
rate for renewable energy consumption vary between countries
and ranges from as low as –1.976 in Egypt, to as high as 9.235
in Algeria. For all countries used for this study do not exceed 5%
per year except for Algeria. This result conrms that most of the
aforementioned countries have not yet suciently invested in
green technologies using renewable energy. In fact, some countries
such as Egypt, Iran, Lebanon, Morocco and Tunisia stand out for
having high growth rate per capita. Succinctly, the average annual
growth rate of renewable energy consumption in these countries
is similar to their average annual GDP per capita growth rate. In
Table 3b: Summary of related studies
Authors Sample Period Estimation technique Findings
Zoundi (2017) 25 African
countries
1980-2012 Panel cointegration
approach
No evidence of total validation of EKC. Renewable energy use
negatively related to CO2 emission
Country-specic studies
Authors Sample Period Estimation technique Findings
Omotor (2008) Nigeria 1970-2005 Johansen cointegration,
Hsiao granger causality
There existed a bi-direction causality between energy consumption
and growth
Halicioglu
(2009)
Turkey 1960-2005 Panel cointegration test Economic growth has signicant eect impact on CO2 emission
Chang (2010) China 1982-2004 Multivariate cointegration
and VECM
Energy consumption and GDP had positive and signicant
relationship
Saboori and
Sulaiman (2011)
Iran 1971-2007 Cointegration approach;
ARDL
EKC hypothesis assumes an inverted U-shaped relationship.
Energy consumption had a positive and signicant eect on CO2
emission
Saboori and
Sulaiman (2013)
Malaysia 1980-2009 ARDL and VECM Energy consumption and GDP had positive and signicant
relationship
Shahbaz et al.
(2015)
Tunisia 1971-2010 VECM and ARDL Energy consumption and GDP had positive and signicant
relationship
Long et al.
(2015)
China 1952-2012 Unit root and
cointegration; granger
causality
Energy consumption and GDP had positive and signicant
relationship
Bouznit and
Pablo-Romero
(2016)
Algeria 1970-2010 ARDL EKC curve conrmed. Income has not yet reach the required
threshold
Ahmad and Du
(2017)
Iran 1971-2011 ARDL-FMOLS and
Dynamic OLS
There is a positive relationship between CO2 emission and
economic growth
Dogan and
Ozturk (2017)
USA 1980-2014 EKC model structural
break ARDL model
Renewable energy consumption mitigates environmental
degradation
Ishioro (2018) Nigeria - Multivariate unit root,
Johansen cointegration
and Granger causality test
Energy consumption has improved the performance of
manufacturing, health, agriculture, transport, utilities and nance
sectors in Nigeria
Ishioro (2019) Nigeria - VA R For each of the energy components and growth variables,
own shocks were more profound and there were evidences of
substitutability of shocks
Source: Compiled by author
Figure 2: Plot of renewable energy (share of consumption from
renewable energy sources in total nal energy)
Table 4: Descriptive statistics
Statistics LNGDPpcap LNCAP LNLAB LNREW LNCO2
Mean 9.255 3.190 3.887 1.300 1.167
Maximum 10.422 3.762 4.240 3.157 2.290
Minimum 8.262 2.521 3.609 -2.830 -0.052
Std. Dev. 0.495 0.217 0.152 1.311 0.588
Skewness 0.450 -0.078 0.389 -0.855 0.255
Kurtosis 2.872 3.221 2.140 2.899 2.359
Jarque-Bera 8.006 0.708 10.000 28.420 6.487
Probability 0.018 0.701 0.001 0.000 0.039
Observation 232 232 232 232 232
Source: Author’s computation using E-views 10

Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020
446
Table 5: Average growth rates over the period 1990-2018
Country Renewable energy
consumption
GDP per capita CO2 emission
Algeria 9.235 1.059 1.538
Egypt –1.976 2.269 1.300
Iran 0.998 2.096 3.284
Israel 2.685 1.742 –0.593
Jordan 0.497 1.054 0.332
Lebanon –1.333 2.979 1.158
Morocco –1.111 2.460 2.181
Tunisia –0.166 2.486 2.135
Source: Author’s computation
Figure 3: Plot of CO2 emission per capita (metric tons per capita)
Figure 4: Plot of GDP per capita (constant 2010, PPP)
Table 6: Panel unit root test results
Method REW CO2GDPpcap Labour Capital
LLC-t* (level)
(1st Di.)
–0.647 (0.258)
–4.517 (0.000)**
1.764 (0.961)
–2.132 (0.016)**
2.119 (0.983)
–5.171 (0.000)**
1.050 (0.853)
–5.582 (0.000)**
0.147 (0.558)
–4.668 (0.000)**
IPS-@ stat. (level)
(1st Di.)
–1.053 (0.146)
–5.710 (0.000)**
2.435 (0.992)
–5.898 (0.000)**
0.922 (0.821)
–2.156 (0.015)**
0.885 (0.812)
–2.248 (0.012)**
0.326 (0.627)
–5.022 (0.000)**
Source: Author’s Computation using E-views 10. N.B: The variables are expressed in natural logarithms; **Denotes signicant at 5% level; lag selection based on akaike ınformation
criterion
Algeria, Iran and Jordan, the average growth rate for renewable
energy consumption tends to grow more rapidly culminating in
a positive average growth rate of CO2 emission. Also, negative
growth rate of renewable energy in Lebanon, Morocco and Tunisia
also produce positive CO2 emission. Only Israel generates negative
growth rate of CO2 emission, which is traceable to a positive annual
growth rate of renewable energy.
4.1. Panel Unit Root Analysis
In this paper, the panel unit root tests are computed in order to
assess the stationarity of variables including Levin et al. (2002) and
Im et al. (2003) test. Levin et al. (2002) proposes a panel based on
augmented Dickey-Fuller (ADF) test that assumes homogeneity
in the dynamics of the autoregressive coecients for all pane
units with cross sectional independence. The following equation
is considered;

YY
HY
it iiit iiji
ti
t
k
  


  
,,11
(5)
Where
∆
is the rst dierence operator, Yit is the dependent variable,
µit is a white-noise disturbance with a variance, i represents indexes
country, and t represents indexes on time. The test involves the null
hypothesis (H0:ηi=0) for all i against the alternative (H0:ηi≠0) for all
i. Im et al. (2003) test is not restrictive as Levin et al. (2002) test,
since it allows for heterogeneous coecients. The null hypothesis
is that all individuals follow a unit root process, (H0:ηi=0) for all
i. The alternative hypothesis allows some of the individuals to
have unit roots, then H
iN
iN N
ii
ii
1
01
01
:;,...,
;,
...,









. The results of
the unit root test in Table 6 indicate that each variable is integrated
of order one, I(1).
4.2. Panel Cointegration Test
We employ the Pedroni (2004) cointegration test. The panel
cointegration test result of Pedroni (2004) is presented in
Table 7. Pedroni proposes two cointegration tests based on the
within approach which include four statistics (panel test) and the
between approach which includes three statistics. However, the
Pedroni cointegration test is based on the residuals and variants
of Phillips and Perron (PP, 1988) and Dickey and Fuller (ADF,
1979). The Pedroni’s cointegration result indicates that we reject
the null-hypothesis of no cointegration at 5% signicant level,
which implies that there exist a long run relationship between
renewable energy, carbon emission and economic growth in
MENA countries.
4.3. Panel Fully Modied OLS and Dynamic OLS
Although, OLS estimators of the cointegrated vectors are
convergent, their distribution is asymptotically biased and thus
depends on nuisance parameters connected with the presence of
serial correlation in the series (Pedroni, 2001). Such problems,
existing in the time series arise for the panel data and tend to
be more pronounced even in the presence of heterogeneity. In

Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020 447
carrying out tests on the cointegrated vectors, it is necessary to
use an eective estimation technique. Various techniques exist
such as the fully modied ordinary least square (FMOLS) as
initially suggested by Phillips and Hansen (1990) or the dynamic
ordinary least square (DOLS). In the case of the panel data, these
two techniques lead to normally distributed estimators, implying
that both the OLS and FMOLS exhibit small-sample bias and that
DOLS estimator appears to out-perform both estimators (Phillips
and Moon, 1999 and Pedroni, 2001). Thus our empirical model
is based on the regression analysis between the three variables as
evident in equation 4.
Table 8 presents results of individual and panel FMOLS and
DOLS. The estimated coecient from the long run cointegration
relationship can be interpreted as long run elasticities. Beginning
with the country specic results, we nd that renewable energy
exhibits signicant impact on GDP for countries like Algeria,
Egypt, Iran and Jordan under the FMOLS while the other countries
exhibited insignicant relationship to GDP. For the DOLS, only
Algeria, Egypt and Morocco exhibited signicant eect on GDP.
From the FMOLS results, only Algeria, Iran and Jordan had
positive and signicant renewable energy consumption eect
on GDP, while for the DOLS, Algeria, Egypt and Morocco
exhibited positive and signicant in relation to renewable energy
consumption to GDP. Turning to the eect of CO2 emission on
GDP, Egypt, Israel, Jordan and Morocco exhibited positive and
signicant eect on GDP under the FMOLS. As regards the DOLS
results, Algeria, Egypt and Morocco exhibited positive and eect
on GDP. From Table 8, it is evident from the FMOLS that GDP has
positive and signicant impact on renewable energy for countries
such as Algeria, Iran and Jordan, while it exhibited negative and
signicant impact in Egypt and Tunisia. From the DOLS, GDP
showed a positive and signicant impact on renewable energy
Table 9: Long run elasticity result
Panel/Countries CO2 as dependent variable
FMOLS DOLS
REW GDPpcap REW GDPpcap
Panel –0.096 (–2.960)** 0.596 (4.053)** 0.020 (0.353) 0.341 (1.457)
Algeria –0.022 (–0.578) 0.088 (0.159) –0.227 (–4.458)** 2.447 (4.537)**
Egypt 0.564 (1.713) 3.528 (5.927)** 0.460 (1.331) 2.418 (3.359)**
Iran –0.049 (–1.665) 0.014 (0.078) 0.008 (0.116) –0.182 (–0.791)
Israel –0.065 (–1.197) 2.514 (3.566)** 0.111 (1.850) –0.347 (–0.650)
Jordan –0.359 (–7.176)** 0.368 (3.697)** –0.264 (–3.016)** 0.441 (2.604)**
Morocco 0.054 (1.082) 0.529 (2.677)** 0.014 (0.151) 1.103 (2.448)**
Tunisia –0.300 (–1.499) 0.162 (0.479) –1.106 (–1.160) –1.057 (–1.296)
Source: Author’s computation using E-views 10.**, ***Denotes signicant at 5% (10%) level; t-statistics in parenthesis
Table 7: Pedroni’s (2004) cointegration result (GDPpcap as
dependent variable)
Pedroni cointegration test
Common AR coecients (within dimensions)
Group coecients Statistics P-value Weighted
Statistics
P-value
Panel v-Statistics 4.458 0.000** 4.937 0.000**
Panel rho-Statistics –0.273 0.392 0.647 0.741
Panel PP-Statistics –5.648 0.000** –2.399 0.008**
Panel ADF-Statistics –8.133 0.000** –4.432 0.000**
Individual AR coecients (within dimensions)
Group rho-Statistics 1.707 0.956
Group PP-Statistics –2.059 0.019**
Group ADF-Statistics –3.776 0.000**
Source: Author’s Computation using E-views 10. **Denotes signicant at 5% level
Table 8: Long run elasticity result
Panel/Countries GDPpcap as dependent variable REW as dependent variable
FMOLS DOLS FMOLS DOLS
REW CO2REW CO2GDP CO2GDP CO2
Panel 0.035
(1.463)
0.289
(4.268)**
0.058
(3.327)**
0.160
(1.870)***
0.698
(1.489)
–0.811
(–2.776)**
1.411
(1.706)***
–0.795
(–1.463)
Algeria 0.032
(2.446)**
–0.001
(–0.022)
0.058
(3.327)**
0.160
(1.878)***
6.495
(2.623)**
–0.563
(–0.562)
0.068
(0.007)
–6.066
(–1.836)***
Egypt –0.146
(–2.104)**
0.167
(5.763)**
–0.217
(–3.904)**
0.270
(12.644)**
–1.243
(–2.547)**
0.216
(1.909)***
–0.630
(–0.311)
–0.742
(–1.243)
Iran 0.074
(2.149)**
0.026
(0.129)
0.209
(1.249)
–1.529
(–1.658)
2.071
(1.853)***
–1.376
(–1.291)
–0.648
(–0.428)
–10.502
(–3.436)**
Israel –0.006
(–0.450)
0.115
(2.676)**
0.004
(0.076)
0.524
(1.391)
–0.985
(–0.305)
–0.849
(–1.166)
–0.472
(–0.067)
–6.338
(–2.458)**
Jordan 0.305
(2.650)**
0.698
(3.100)**
0.367
(1.488)
0.269
(0.373)
0.675
(2.702)**
–1.379
(–5.626)**
1.275
(3.051)**
–1.929
(–2.501)**
Morocco –0.029
(–0.749)
0.435
(2.970)**
–0.100
(–3.604)**
0.545
(5.693)**
–0.695
(–0.702)
0.626
(0.679)
4.467
(1.500)
0.480
(0.322)
Tunisia –0.209
(–1.556)
0.015
(0.108)
–0.208
(–0.247)
–0.248
(–1.005)
–0.368
(–1.961)***
–0.234
(–1.942)***
–1.951
(–3.191)**
–0.465
(–1.598)
Source: Author’s computation using E-views 10. **, ***Denotes signicant at 5% (10%) level; t-statistics in parenthesis

Maku and Ikpuri: A Multivariate Analysis between Renewable Energy, Carbon Emission and Economic Growth: New Evidences from
Selected Middle East and North Africa Countries
International Journal of Energy Economics and Policy | Vol 10 • Issue 6 • 2020
448
consumption in Jordan only and a negative and signicant eect
in Tunisia.
As regards CO2 emission-renewable energy consumption
relationship, it is observed that from the FMOLS, there was a
positive and signicant relationship in Egypt only, while Jordan
and Tunisia exhibited negative and significant relationship.
Under the DOLS, for countries such as Algeria, Iran, Israel, and
Jordan, CO2 emission exhibited negative and signicant eect
on renewable energy consumption. Under both the FMOLS
and DOLS panel results, with renewable energy as dependent
variable, we nd out that the elasticity of CO2 emission exhibits
negative eect at 5% signicant level. This implies that with the
increase in CO2 emission, demand for renewable energy decreases.
Furthermore, the results proves that most of the aforementioned
countries do not utilize renewable energy mainly as a result of the
investment cost in green technologies; as such government do not
encourage their respective economies to adopt clean technologies
using renewable energy.
Table 9 shows the relationship between renewable energy, GDP
and CO2 emission. From the Fully Modied OLS, it is evident that
renewable energy shows negative relationship to carbon emission.
Thus implies that renewable energy consumption plays a vital
role in decreasing CO2 emission. Critically, GDP in most of the
countries triggers signicant increase in CO2 emission as evident
from both the FMOLS and DOLS.
5. CONCLUSION AND POLICY
IMPLICATION
In this paper, we have examined the relationship among
renewable energy, CO2 emission and GDP in 8 MENA countries
from 1990 to 2018. To specify what matter, the study adopted
the panel unit root test, cointegration test and the FMOLS/DOLS
test. Our panel cointegration results reveal the existence of panel
long run equilibrium between renewable energy, CO2 emission
and GDP. An important emerging result from the analysis is that
renewable energy consumption plays a vital role in lowering
CO2 emission. Furthermore, we can say that policies in these
countries may stabilize output and income while attempting to
consume more ecient energy. As such policy makers should
then take it into consideration the degree of output (growth) in
each country when renewable energy policy is formulated. In
this case, policy makers should encourage a multilateral eort
in promoting and increasing output in each country where
renewable energy and thus reduce CO2 emission in the region.
Regional cooperation on the development if renewable energy
markets between public and private sector stakeholders could
begin with sharing fundamental information across countries
with respect to technologies as well as nancing and investment
strategies (Apergis and Payne, 2010)
In addition, pollution can be reduced if governments improve
the industrial sector by importing cleaner technology to attain
maximum benet from international trade (Shahbaz et al., 2012;
Tiwari et al., 2013) and also implement eective economic and
nancial development policies which improves the environment,
which will help in redirecting resources to environmental friendly
projects.
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