Renewable energy, energy efficiency, and economic complexity in the middle East and North Africa: A panel data analysis

Abstract

This paper aims to investigate how economic complexity and structural transformation affect energy security. This study differs from previous research by focusing on energy efficiency and renewable energy transition as indicators of energy security. The employed methods include econometric techniques such as Panel-Corrected Standard Errors, Driscoll and Kraay's Spatial Correlation Consistent (SCC) method, and Generalized Least Squares, covering data from the Middle East and North Africa (MENA) countries between 1990 and 2017. The results show that economic complexity has a negative effect on energy efficiency but a positive impact on renewable energy. However, economic growth positively affects energy efficiency but negatively influences renewable energy. These results imply that economic complexity is energy-intensive but green, whereas economic growth is energy-saving but brown. The comparative analysis reveals that the negative effects of economic complexity and growth are larger than their positive effects, highlighting the necessity of restructuring economic activities and sectors. Accordingly, decision-makers should encourage the utilization of more energy-efficient technologies in economic activities and production, while promoting renewable energy consumption to enhance energy security in the MENA region.

Full text

Renewable energy, energy efciency, and economic complexity in the
middle East and North Africa: A panel data analysis
Vahid Mohamad Taghvaee
a,*
, Behnaz Saboori
b
, Susanne Soretz
c
, Cosimo Magazzino
d,e
,
Moosa Tatar
f
a
Business School, University of Mannheim, Mannheim, Germany
b
Department of Natural Resource Economics, College of Agricultural and Marine Sciences, Sultan Qaboos University, Muscat, Oman
c
Chair of Economic Growth, Structural Change and Trade, University of Greifswald, Germany
d
Department of Political Science, Roma Tre University, Rome, Italy
e
Economic Research Center, Western Caspian University, Baku, Azerbaijan
f
Department of Pharmaceutical Health Outcomes and Policy, University of Houston, Houston, TX, USA
ARTICLE INFO
Handling editor: Isabel Soares
Keywords:
Economic complexity
Energy security
Renewable energy transition
Energy efciency
Sustainable development
Panel data
ABSTRACT
This paper aims to investigate how economic complexity and structural transformation affect energy security.
This study differs from previous research by focusing on energy efciency and renewable energy transition as
indicators of energy security. The employed methods include econometric techniques such as Panel-Corrected
Standard Errors, Driscoll and Kraay’s Spatial Correlation Consistent (SCC) method, and Generalized Least
Squares, covering data from the Middle East and North Africa (MENA) countries between 1990 and 2017. The
results show that economic complexity has a negative effect on energy efciency but a positive impact on
renewable energy. However, economic growth positively affects energy efciency but negatively inuences
renewable energy. These results imply that economic complexity is energy-intensive but green, whereas eco-
nomic growth is energy-saving but brown. The comparative analysis reveals that the negative effects of economic
complexity and growth are larger than their positive effects, highlighting the necessity of restructuring economic
activities and sectors. Accordingly, decision-makers should encourage the utilization of more energy-efcient
technologies in economic activities and production, while promoting renewable energy consumption to
enhance energy security in the MENA region.
1. Introduction
In recent years, climate change, resource scarcity, and energy secu-
rity have made energy efciency increasingly important [1,2]. Energy
efciency has become a key means of reducing greenhouse gas (GHG)
emissions, conserving energy resources, and boosting economic pro-
ductivity, which are essential components of sustainability [3–5]. En-
ergy efciency plays a vital role in achieving Sustainable Development
Goals (SDGs), allowing countries to meet economic and social objectives
(Omri et al., 2024) while reducing energy consumption [6–8,]. The
global community has recognized the signicance of energy efciency,
as evident in the United Nations’ SDG 7.3, which aims to double energy
efciency improvement rates worldwide by 2030 [9,10]. However,
despite this commitment, the rate of improvement in energy efciency
has fallen short of the 2.6 % annual target required to achieve SDG 7.3,
with only a meager improvement of 0.8 % in global primary energy
intensity in 2020 (Tracking SDG7 Report, 2021). To meet the goal, an
average annual rate of 3 % is necessary from 2018 through 2030.
To mitigate the detrimental effects of the energy sector on the
environment and security, countries have redirected from pure eco-
nomic growth to economic complexity focusing more on knowledge and
innovation [11,12]. Economic complexity refers to the variety and so-
phistication of economic activities within a country or region. Economic
complexity reduces energy consumption and its environmental dangers
by developing energy-efcient technologies as a result of promoting
further research and development. In this way, economic complexity
can increase energy efciency [13–15]. In addition, countries with
higher complexity have a larger share of knowledge-based products
* Corresponding author.
E-mail addresses: vahidestan@yahoo.com (V. Mohamad Taghvaee), b.saboori1@squ.edu.om (B. Saboori), soretz@uni-greifswald.de (S. Soretz), cosimo.
magazzino@uniroma3.it (C. Magazzino), mtatar@uh.edu (M. Tatar).
Contents lists available at ScienceDirect
Energy
journal homepage: www.elsevier.com/locate/energy
https://doi.org/10.1016/j.energy.2024.133300
Received 13 November 2023; Received in revised form 24 April 2024; Accepted 27 September 2024
Energy 311 (2024) 133300
Available online 29 September 2024
0360-5442/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ ).

instead of energy-consuming production. This point is particularly
cogent when it comes to energy-producing states such as the Middle East
and North Africa (MENA) countries with inexpensive and high access to
fossil fuels. To replace fossil fuels, economically complex regions assess
more advanced technologies for diversication of their own energy
portfolios to increase the share of renewable energies [16–18]. Conse-
quently, economic complexity can both promote renewable energy and
improve energy efciency.
However, economic complexity and growth can reduce renewable
energy and energy efciency through various channels [19–21]. For
example, it can cause technological lock-in where network impacts, path
dependence, and regulatory capture pave the way for the adoption of
specic infrastructure and technologies with an energy-inefcient and
intensive nature [22–24]. In addition, the rapid growth of economic
complexity may outpace the current energy infrastructure decreasing
energy inefciency of the production process. Moreover, economic
complexity may reduce the energy efciency of the global supply chain
by extending distances between more diverse materials in different re-
gions, which requires further transportation and energy consumption.
This increase in energy demand stimulates further consumption of fossil
fuel as more inexpensive and accessible energy in the short-run, leading
to a reduction in the share of renewable energy; in fact, this needs a
long-run horizon to reach high accessibility and low cost [25–27].
Therefore, economic complexity may decrease energy efciency and the
share of renewable energy, damaging the environment and energy se-
curity in countries with high economic complexity.
These conicting views on economic complexity complicate the
understanding of how it affects energy efciency and renewable energy,
necessitating further exploration, especially in countries with high ca-
pacity of fossil fuel energy like the MENA region. Although many
countries in this area have developed strategies to boost their economies
via the promotion of economic complexity, the implementation can
differently and ambiguously impact renewable energy and energy ef-
ciency. The MENA region has traditionally experienced brown economic
growth, heavily dependent on the export of fossil fuels for its economic
development. However, in recent years, many MENA countries have
recognized the need to diversify their economies and reduce their
dependence on fossil fuels. This shift necessitates a structural trans-
formation and the adoption of energy efciency measures. To achieve an
energy-efcient growth structure, they should focus on energy-efcient
development and technologies in the economic sectors. Additionally,
they should develop their economic complexity, which refers to the
diversity and sophistication of their economic activities.
Energy efciency and economic complexity stand as two pillars vital
for the sustainable development of MENA countries. In this diverse re-
gion, characterized by abundant natural resources like oil and gas,
optimizing energy use is paramount to ensure long-term prosperity and
mitigate environmental degradation [28]. Energy efciency measures
encompass a spectrum of strategies ranging from technological ad-
vancements to policy frameworks aimed at reducing energy consump-
tion while maintaining or enhancing productivity. MENA nations, with
their burgeoning populations and expanding economies, face the dual
challenge of meeting rising energy demand and curbing greenhouse gas
emissions. Therefore, embracing energy efciency not only conserves
valuable resources but also fosters economic resilience and environ-
mental sustainability.
The economic complexity of MENA countries, on the other hand,
denotes the intricacy and diversity of their industrial structures and
exports. While endowed with signicant oil and gas reserves, many
nations in the region recognize the vulnerability of their economies to
uctuations in global energy markets. Hence, fostering economic
complexity through diversication becomes imperative for long-term
stability and growth. Energy efciency initiatives play a pivotal role in
this diversication process by unlocking opportunities for innovation
and investment in renewable energy, manufacturing, and services sec-
tors. By reducing reliance on fossil fuels and promoting cleaner
technologies, MENA nations can enhance their economic resilience and
competitiveness in a rapidly evolving global landscape.
Challenges abound in the pursuit of energy efciency and economic
complexity in MENA countries. Despite the region’s vast renewable
energy potential, including solar and wind resources, the transition to-
wards a sustainable energy future requires substantial investments in
infrastructure and human capital. Moreover, entrenched interests in the
fossil fuel industry, coupled with institutional inertia and bureaucratic
hurdles, often impede progress towards energy efciency targets.
Similarly, diversifying economies away from hydrocarbon dependency
demands bold policy reforms, targeted investments, and concerted ef-
forts to develop nascent industries and value chains.
Nevertheless, several MENA countries have embarked on ambitious
energy efciency and economic diversication initiatives, leveraging
their unique strengths and resources. The United Arab Emirates (UAE),
for instance, has emerged as a global leader in renewable energy
deployment, with ambitious targets to increase the share of clean energy
in its total energy mix. Through initiatives like the Masdar City project
and investments in solar power plants, the UAE aims to not only reduce
its carbon footprint but also position itself as a hub for green technology
and innovation.
Similarly, Saudi Arabia, the largest economy in the region, has un-
veiled Vision 2030, a comprehensive blueprint for economic trans-
formation and diversication. Central to this vision is the development
of renewable energy sources, including solar and wind, to meet growing
domestic demand and create new employment opportunities. By
investing in renewable energy projects and fostering partnerships with
international stakeholders, Saudi Arabia seeks to reduce its reliance on
oil exports and foster a more sustainable and inclusive economy.
This paper aims to address the inuence of economic complexity on
energy security, particularly in the context of renewable energy adop-
tion and energy efciency. The transition to renewable energy and en-
ergy efciency plays a crucial role in enhancing energy security and
mitigating climate change. Thus, this study explores the impact of eco-
nomic growth, economic complexity, and oil prices on renewable energy
and energy efciency, while also incorporating industry share as an
indicator of structural transformation in the analysis. Unlike previous
research, this study focuses on energy efciency and renewable energy
transition as key indicators of energy security and examines the role of
economic complexity in shaping these factors. By including structural
transformation as an additional dimension in the empirical investiga-
tion, the paper seeks to provide valuable insights for policymakers and
businesses aiming to foster sustainable economic growth and enhance
energy security.
The research contributes to the literature investigating the inuence
of economic complexity on the transition to renewable energy and en-
ergy efciency, which are essential components of energy security and
SDGs. While many studies have examined the effect of economic
complexity on environmental degradation so far, only a few have
explicitly explored its impact on energy efciency and renewable energy
adoption. This paper aims to ll this gap by analyzing the effect of
economic growth, economic complexity, and oil prices on renewable
energy and energy efciency, while also considering industry share as an
indicator of structural transformation. The study can also provide
valuable insights into the role of economic complexity in promoting the
transition to renewable energy and energy efciency, which can inform
policymakers and stakeholders in developing effective strategies for
sustainable development.
The remaining sections of the paper are structured as follows. To
provide a comprehensive understanding of the research area, Section 2
presents a review of the relevant literature. Section 3describes the data
used, the model employed, and the methodology applied in the study.
Section 4presents the empirical results, while Section 5discusses the
implications of the paper’s ndings for policy. Finally, Section 6pro-
vides the conclusions.
V. Mohamad Taghvaee et al.
Energy 311 (2024) 133300
2

2. Literature review
Several studies in the literature have explored the concept of eco-
nomic complexity and its environmental, social, and economic impacts.
An expanding body of research indicates that directing investments to-
wards more complex and less ubiquitous goods can enhance a country’s
prospects for economic growth, while also recognizing the signicance
of physical assets, human capital, and institutional arrangements
[29–31]. Recently, some papers contributed to the understanding of the
inuence of economic complexity on various aspects, such as economic
growth [32], income inequality [33,34], national health [35], human
development [36], gender equality [37], environmental degradation
[38–40], natural resource rents [41], and energy consumption [42].
A cluster of studies focused on scrutinizing the inuence of economic
complexity on environmental degradation, yielding mixed results. Ali
et al. [43] investigated the impact of economic complexity on ecological
footprint in China, revealing that energy innovation and economic
complexity have a positive short- and long-term effect on reducing
ecological footprint. Yilanci and Pata [44] examined the effect of eco-
nomic complexity and energy consumption on the ecological footprint in
China, nding that both factors positively inuence the ecological
footprint. Conversely, Can and Gozgor [45] reported that economic
complexity amplies carbon dioxide (CO
2
) emissions in France. Do˘
gan
et al. [38] disclosed that economic complexity spurs environmental
degradation in lower- and higher-middle-income countries but curbs
CO
2
emissions in high-income countries. Lapatinas et al. (2019) revealed
that economic complexity decreases the aggregate indicators of envi-
ronmental pollution while increasing some indicators of air quality in-
dicators in 88 developing and developed countries. Neagu and Teodoru
[46] determined that an increase in economic complexity elevates GHG
emissions in both low- and high-economic complexity EU countries.
Swart and Brinkmann [47] demonstrated that economic complexity
heightens forest res, diminishes waste generation, and has no bearing
on deforestation and air pollution in Brazil. Magazzino [48] analyzed
the relationship among ecological footprint, electricity consumption,
and Gross Domestic Product (GDP) in China using annual data ranging
from 1960 to 2019, highlighting that electricity consumption and real
GDP increase environmental degradation, while trade and urbanization
reduce the ecological footprint.
Despite numerous studies investigating the relationship between
economic complexity and environmental degradation, there is still a gap
in understanding the specic effects of economic complexity on energy
efciency and renewable energy adoption. While some research focused
on environmental indicators such as ecological footprint and CO
2
emissions, only a few explicitly examined the impact of economic
complexity on the transition to renewable energy and energy efciency.
3. Theoretical framework
To achieve green growth, many countries are now adopting renew-
able and clean energy sources to improve energy efciency [49–51]. To
this end, they generate signicant structural changes and introduce
technological innovations [52,53]. The shift towards renewable energy
is mainly driven by concerns about climate change, reducing depen-
dence on foreign fossil fuels, and securing energy supplies amidst
changing global energy markets [54,55]. Although the transition to
renewable energy poses challenges, many countries consider it a vital
approach to ensure long-term energy security while reducing their
carbon footprint [56–59]. Even though this transition slowly progresses,
as evidenced by the 1.4 % increase in CO
2
emissions in 2017 after
maintaining steady levels for three years [36,56,60], it is strongly ex-
pected to increase energy efciency and renewable energy.
Countries implement these initiatives by focusing on economic
complexity with an energy-saving feature. Complexity refers to the
range and diversity of economic activities within a region or country,
which can inuence the efciency of energy use and the adoption of
renewable energy sources. Countries with higher economic complexity
are adopting knowledge-intensive technologies, which facilitate
renewable energy generation and more efcient energy use, thereby
contributing to environmental quality [61–64]. Such economies with
energy-efcient technologies have energy-saving complexity. Incentives
to promote renewable energy technologies have resulted in the expan-
sion of renewable energy sources and the development of electricity
infrastructures, stimulating the creation of smart grid systems and
enabling more energy-efcient processes (Sampaio et al., 2018; [65]).
These economies with a high share of renewable energy have green
complexity.
The analysis proposes the following two research hypotheses.
Hypothesis 1. Economic complexity increases energy efciency.
Hypothesis 2. Economic complexity increases renewable energy.
4. Model’s specication
This study uses a panel dataset including 22 countries in the MENA
region to estimate how economic complexity affects energy intensity
and renewable energy consumption between 1990 and 2017.
Following Lee and Ho [66] and Zhou et al. [67], this research esti-
mates the effect of economic complexity on energy intensity as sum-
marized in Equation (1) [66]:
EIit =C+
α
1ECit +
α
2POit +
α
3Yit +
ε
it (1)
where EI is the energy intensity, measured as the proportion of energy
supply in Megajoules (MJ) to GDP in Purchasing Power Parity (2017 US
Dollar in PPP); EC is the economic complexity; PO is the oil price
measured as US dollars per barrel; Y is the GDP at constant 2015 US
Dollar; C is the intercept;
ε
is the residuals’ series;
α
are the parameters; i
gives the cross-section dimension; and t is for the time dimension. If
α
1 is
negative, Hypothesis 1 is not rejected.
Equation (2) follows Kazemzadeh et al. [68], Lee and Ho (2023), and
Numan et al. [69] to estimate the impact of economic complexity on
renewable energy:
REit =C+β1ECit +β2POit +β3Yit +
ε
it (2)
where RE is renewable energy consumption measured in Exajoules and β
s are the parameters. If β
1
is positive, Hypothesis 2 is not rejected.
Furthermore, this research follows Taghvaee et al. [70], considering
a sector-wise analysis by adding different sector value-added variables
into Equations (1) and (2) to specify Equations (3) and (4):
EIit =C+θ1ECit +θ2POit +θ3INit +θ4SEit +θ5AGit +
ε
it (3)
where IN, SE, and AG are industry, services, and agriculture value
added, respectively, as a percentage of GDP; and θ are the parameters. If
θ
1
is negative, Hypothesis 1 is not rejected.
REit =C+γ1ECit +γ2POit +γ3INit +γ4SEit +γ5AGit +
ε
it (4)
where γ are the parameters. If γ
1
is positive, Hypothesis 2 is not rejected.
All the variables are in natural logarithms, except for EC which
shows a lower range of uctuations compared with other variables.
The estimated coefcients of economic complexity in these equations
represent the relationships of this variable with energy intensity and
renewable energy consumption. In addition, the estimated coefcients
of economic sectors in Equations (3) and (4) show the relationships of
each economic sector with energy intensity and renewable energy.
Economic complexity and growth are energy-saving if complexity
and GDP with its disaggregated sectors have a negative relationship with
energy intensity; otherwise, they are energy-intensive. The economic
complexity and growth are green if complexity and GDP with its dis-
aggregated sectors have a positive effect on renewable energy; other-
wise, they are brown. The concept of brown growth is adopted following
V. Mohamad Taghvaee et al.
Energy 311 (2024) 133300
3

Shirazi et al. [71], who revealed that oil-exporting countries have brown
growth at initial levels of the growth process, but green growth at later
steps. In addition, these relationships are comparable with each other
since the coefcients are in natural logarithms, and they can be inter-
preted as elasticities.
5. Estimation techniques
Before the estimations phase, the stationarity of the variables is
examined using the Cross-section Augmented Dickey–Fuller (CADF)
test, which is a second-generation unit root test [72]. Here,
cross-sectional dependence is considered in accordance with one single
common factor existing among all the states impacting the variables of
the model. This test is advantageous since it does not need to estimate
the factor. Instead, the test uses the cross-section means of the lagged
levels in addition to the rst difference of the variable according to the
following CADF regression in Equation (5) [72,73]:
Δyit =
α
i+biyi,t−1+cyt−1+diΔyt+eit (5)
where y is the tested variable, yt−1 represents the cross-section mean of
the lagged values of y, Δyt denotes the rst difference of the cross-
section mean of y. Equation (5) estimates the t-statistics of b
i
to check
the null hypothesis.
After checking the stationarity of the variables, the eventual pres-
ence of a long-run relationship between the variables is analyzed
through the Kao [74] cointegration test. This test is based on the
Engle-Granger approach and follows the Pedroni [75] cointegration test
to allow heterogeneous intercepts as well as the existence of trend co-
efcients across the cross-section. However, the rst stage regression
presumes a certain intercept and homogenous coefcients across sec-
tions [76–79]. Equation (6) represents a bivariate Kao’s model [80]:
Yit =
α
i+bxit +cit (6)
where Yit =yi,t−1+uit , xit =xi,t−1+vit , t=1,…,T;i=1,…,N. This
model can conduct the rst stage regression. Kao’s techniques examine
the residual series using a mix of auxiliary regressions [74].
Finally, Equations (1)–(4) are estimated using the Panel-Corrected
Standard Errors (PCSE) estimator, the Driscoll and Kraay’s method
[81,82], and the Generalized Least Squares (GLS) estimator [82]. PCSE
may produce inaccurate results for data with a short time dimension;
however, it can reach reliable estimates even in the presence of
contemporaneous correlation and heteroskedasticity in panel data [83,
84]. Driscoll and Kraay’s method may produce biased results if it dis-
regards the cross-sectional correlation of the variables. In other words, it
can produce accurate estimates when associated with a large number of
cross-sectional units [85]. For this reason, the study examines the
cross-sectional dependence. However, this method creates highly reli-
able results if cross-sectional dependence exists. Although GLS has some
challenges in precisely estimating covariance structure in small samples,
the dataset that we collected covers more than 500 observations
enabling the method to provide valid results. The GLS estimator is
reliable when the Ordinary Least Squares (OLS) estimator is found not to
be the Best Linear Unbiased Estimator (BLUE) because of hetero-
skedasticity and serial correlation [86].
6. Data collection
All the data is derived from the World Development Indicators (WDI)
by the World Bank [87], except for the economic complexity, which is
collected from the Observatory of Economic Complexity (2021). The
sample includes Algeria, Azerbaijan, Egypt, Georgia, Iran, Israel, Jor-
dan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Pakistan,
Qatar, Saudi Arabia, Sudan, Syria, Tunisia, Turkey, United Arab Emir-
ates, and Yemen. Table 1 gives the symbols, denitions, units, and
sources of each variable. Table 2 provides the relevant descriptive
statistics. Fig. 1 reports the scatterplot matrix of the variables.
7. Empirical results
The regression estimates show a signicant and positive relationship
between economic complexity, energy intensity, and renewable energy
consumption in the MENA region. This result implies that economic
complexity has a damaging role in economic efciency and a positive
impact on the environment. However, economic growth and its main
sectors indicate a substantial and negative relationship with energy in-
tensity and renewable energy consumption. This result implies that
economic growth and its main sectors have a positive effect on economic
efciency but a negative impact on the environment.
Table 3 presents the results of CD tests. The ndings indicate that
cross-sectional dependence emerges. In fact, the Pesaran, Power
enhanced, and Biased corrected CD tests reject the null hypothesis of
weak cross-sectional dependence, against the alternative hypothesis of
strong cross-sectional dependence. Therefore, all variables are cross-
sectional dependent, paving the way for using the second-generation
unit root test.
Table 4 shows the results of the CADF second-generation panel unit
root test, which implies that all the variables have the same integration
order. Based on these ndings, all variables have a unit root at levels, but
they are stationary at rst differences.
Table 5 gives the results of the Kao cointegration test. The null hy-
pothesis of no cointegration is rejected by all four t-statistics.
Table 6 shows the positive effect of economic complexity and the
negative effect of oil price and economic growth on energy intensity as
the economic efciency indicator. According to the results, the coef-
cient of economic complexity is positive and statistically signicant at a
5 % level. This result implies that economic complexity positively affects
energy intensity. However, the coefcient of oil price is −0.2693, which
is negative and statistically signicant at a 1 % level. It shows that oil
price has a negative impact on energy intensity. Nonetheless, one unit
increase in oil price cause only 26 % decrease in energy intensity,
implying that energy intensity has a low elasticity corresponding to
changes in oil price. The estimated negative coefcient of GDP means a
negative effect of economic growth on energy intensity. Despite this
negative nexus, energy intensity is inelastic in response to the changes in
economic growth as a whole since an increase in economic growth can
reduce energy intensity only by 3 %. Although the results from alter-
native estimators show different P-values, the coefcients are consis-
tently similar, conrming the robustness of the results. Therefore,
economic complexity increases energy intensity as a proxy for economic
efciency while oil price and economic growth can decrease it in favor
of the economic pillar of sustainability, albeit slightly.
Table 1
Overview of the dataset.
Symbol Variable Denition Unit Source
EI Energy
intensity
Energy supply/GDP Megajoules/US
Dollar 2017 PPP
[87]
RE Renewable
energy
Renewable power
generation
Exajoules [88]
Y GDP Gross Domestic
Product
Constant price of
US Dollar 2015
[87]
EC Economic
complexity
Diverse export
capability
[89]
PO Oil price Spot Prices for Crude
Oil and Petroleum
Products
US dollars per
barrel
[88]
AG Agriculture
sector
Agriculture value
added/GDP
Percent [87]
IN Industry
sector
Industry value added/
GDP
Percent [87]
SE Services sector Services value added/
GDP
Percent [87]
V. Mohamad Taghvaee et al.
Energy 311 (2024) 133300
4

Table 7 conrms the positive effect of economic complexity and oil
price and the negative effect of economic growth on renewable energy
consumption as a factor of environmental sustainability. The coefcient
of economic complexity is positive and statistically signicant at a 1 %
level. This result implies the positive effect of economic complexity on
renewable energy consumption. In addition, the coefcient of oil price is
positive and statistically signicant at a 1 % level, implying a positive
effect of oil price on renewable energy consumption as the price of a
substitution good, but also the high elasticity of this effect. Renewable
energy consumption is elastic in response to oil price since an increase in
oil price can raise the level of renewable energy consumption. In
contrast, the coefcient of GDP is negative and statistically signicant at
Table 2
Descriptive statistics.
Variable Count Mean Standard Deviation Minimum Maximum Skewness Kurtosis
EI 570 1.7893 0.8767 0.8776 5.8823 3.5174 15.5911
RE 519 1.1641 2.4573 −7.6009 4.4500 −1.0957 3.8976
Y 559 9.3770 3.4649 6.6502 23.9084 −0.8238 4.6654
EC 576 −0.4244 0.6538 −2.0815 1.3146 0.1631 3.3540
PO 588 1.2797 0.1733 0.9822 1.5262 0.0197 1.5561
AG 549 2.8102 4.5817 −2.3623 22.0058 3.5036 14.9140
IN 523 3.3951 0.5010 1.7442 4.4750 −0.4407 3.4501
SE 490 4.9453 4.3744 3.0798 23.1961 3.8040 15.5224
Fig. 1. Scatterplot matrix.
Table 3
Cross-sectional dependence test results.
Variable Pesaran CD Power enhanced CD+Bias corrected CD*
EI 2.39** (0.01) 537.36*** (0.00) 4.40*** (0.00)
RE 10.14*** (0.00) 365.16*** (0.00) −3.70*** (0.00)
Y 36.87*** (0.00) 716.44*** (0.00) −3.72*** (0.00)
EC −3.83*** (0.00) 1218.51*** (0.00) −3.73*** (0.00)
PO 73.11*** (0.00) 1107.73*** (0.00) 60.82*** (0.00)
AG 33.23*** (0.00) 852.54*** (0.00) −0.37 (0.71)
IN 11.45*** (0.00) 390.88*** (0.00) −3.63*** (0.00)
SE 48.46*** (0.00) 352.95*** (0.00) −3.74*** (0.00)
Notes: ***p <0.01, **p <0.05, *p <0.10. P-values in parentheses.
Table 4
Second generation unit root test results.
Variable Level First difference
EI −0.115 (0.45) −3.973*** (0.00)
RE 0.845 (0.80) 5.209*** (0.00)
Y−0.196 (0.42) −1.189** (0.02)
EC −0.146 (0.49) −2.289*** (0.00)
PO −0.201 (0.43) −1.202** (0.03
AG 1.705 (0.95) −1.899** (0.02)
IN 2.200 (0.98) −5.087*** (0.00)
SE 3.561 (1.00) 4.584*** (0.00)
Notes: ***p <0.01, **p <0.05, *p <0.10. P-values in parentheses.
V. Mohamad Taghvaee et al.
Energy 311 (2024) 133300
5

a 1 % level. This result indicates not only the negative effect of economic
growth on renewable energy consumption but also its high elasticity.
Therefore, economic complexity and oil price increase renewable energy
consumption in favor of energy security of sustainability; on the other
hand, economic growth can decrease renewable energy consumption.
Table 8 shows the signicant and negative effects of all economic
sectors of industry, services, and agriculture on energy intensity as an
economic efciency indicator. The coefcient of industry, services, and
agriculture value added are all negative and statistically signicant at a
1 % level, except for agriculture value added in the SCC and GLS esti-
mates. These results have two main implications. The rst one is the
relevance of the services sector’s role in this model, as the most effective
sector in economic efciency. The second implication concerns the es-
timations of this disaggregated model are consistent with the results of
Table 6 about the negative effect of aggregated economic growth on
energy intensity. This nding implies that energy intensity is negatively
affected by each single economic sector. These estimations conrm the
economically efcient structure. In addition, the other estimations
regarding economic complexity and oil price are consistent with previ-
ous results Thus, the economic structure, specically the services sector,
is economically efcient in developing countries, in line with the eco-
nomic pillar of sustainability.
The results in Table 9 clarify that industry and services sectors have
negative effects on renewable energy consumption, while the opposite is
found for the agriculture sector. The coefcients of industry and services
value added are negative and statistically signicant at a 1 % level.
These results in the disaggregated model are consistent with those of the
aggregated model in Table 7. This consistency implies that renewable
energy consumption receives a negative effect not only from the
aggregated economic growth as a whole but also from each of the dis-
aggregated sectors. In sharp contrast, the coefcient of the agriculture
sector is positive and statistically signicant at a 1 % level. This result
shows the unique and exceptional role of the agriculture sector in
renewable energy promotion. The estimated coefcients of the other
variables of economic complexity and oil price are consistent with
previous results of the aggregated mode, which support the robustness
and reliability of the estimations. Therefore, economic structure im-
pedes the promotion of renewable energy, while the agriculture sector is
the only one to enhance renewable energy, consistent with the energy
security of sustainability (see Table 10).
Finally, the results of the Granger causality tests are reported in
Table 10. It emerges that energy intensity is caused by economic
complexity, oil price, services sector, and agriculture sector; while
renewable energy is only caused by GDP (see Table 11).
8. Discussion
This research shows that economic complexity decreases energy ef-
ciency (i.e., energy-intensive complexity) but increases renewable
energy (i.e., green complexity) in MENA. In contrast, economic growth
increases energy efciency (i.e., energy-saving growth) while
decreasing renewable energy (i.e., brown growth
1
). Nonetheless, the
total effect of the economic pillar is negative on the energy security of
sustainability. Table 10 summarizes the results of models 1–4. It shows
how economic complexity and growth affect energy intensity and
renewable energy consumption over the sample. It indicates economic
complexity and economic growth as indicators for the economic pillar of
Table 5
Kao cointegration test results.
Equation DF ADF UMDF UDF
1−2.8667***
(0.00)
−1.3567*
(0.08)
−1.7321**
(0.04)
−3.3876***
(0.00)
2−2.3377***
(0.00)
−1.7867***
(0.00)
−1.3661***
(0.00)
−3.8526***
(0.00)
3−1.3566*
(0.08)
−1.0840
(0.13)
−3.5860***
(0.00)
−3.1670***
(0.00)
4−1.8526***
(0.00)
−1.7867***
(0.00)
−2.1540***
(0.00)
−2.3865***
(0.00)
Notes: ***p <0.01, **p <0.05, *p <0.10. P-values in parentheses. DF: Dickey-
Fuller; ADF: Augmented Dickey-Fuller; UMDF: Unadjusted Modied Dickey-
Fuller; UDF: Unadjusted Dickey-Fuller.
Table 6
Estimated results of model 1 (dependent variable: energy intensity).
Variable PCSE SCC GLS
EC 0.0640** (0.03) 0.0640 (0.32) 0.0640** (0.02)
PO −0.2693*** (0.00) −0.2693*** (0.00) −0.2693*** (0.00)
Y−0.0393** (0.03) −0.0393 (0.36) −0.0393*** (0.00)
Constant 2.0985*** (0.00) 2.0985*** (0.00) 2.0985*** (0.00)
Wald
χ
2
/F 200.13*** (0.00) 15.36*** (0.00) 23.19*** (0.00)
R-squared 0.0475 0.0475 
RMSE 0.3391 
Notes: ***p <0.01, **p <0.05, *p <0.10. P-values in parentheses.
Table 7
Estimated results of model 2 (dependent variable: renewable energy
consumption).
Variable PCSE SCC GLS
EC 1.2833*** (0.00) 1.2833*** (0.00) 1.2833*** (0.00)
PO 1.6303*** (0.00) 1.6303*** (0.00) 1.6303*** (0.00)
Y−2.0828*** (0.00) −2.0828*** (0.00) −2.0828*** (0.00)
Constant 17.1405*** (0.00) 13.2906*** (0.00) 13.2906*** (0.00)
Wald
χ
2
/F 714.15*** (0.00) 63.88*** (0.00) 546.54*** (0.00)
R-squared 0.5506 0.5506 
RMSE 1.7533 
Notes: ***p <0.01, **p <0.05, *p <0.10. P-values in parentheses.
Table 8
Estimated results of model 3 (dependent variable: energy intensity).
Variable PCSE SCC GLS
EC 0.1158*** (0.00) 0.1158 (0.11) 0.1158*** (0.00)
PO −0.3404*** (0.00) −0.3404*** (0.00) −0.3404*** (0.00)
IN −0.2732*** (0.00) −0.2732*** (0.00) −0.2732*** (0.00)
SE −0.9111*** (0.00) −0.9111*** (0.00) −0.9111*** (0.00)
AG −0.0361*** (0.00) −0.0361 (0.18) −0.0361* (0.09)
Constant 6.5962*** (0.00) 6.5962*** (0.00) 6.5962*** (0.00)
Wald
χ
2
/F 1508.93*** (0.00) 40.26*** (0.00) 92.34*** (0.00)
R-squared 0.1820 0.1820 
RMSE 0.3019 
Notes: ***p <0.01, **p <0.05, *p <0.10. P-values in parentheses.
Table 9
Estimated results of model 4 (dependent variable: renewable energy
consumption).
Variable PCSE SCC GLS
EC 0.1570 (0.19) 0.1570 (0.35) 0.1570 (0.24)
PO 1.9622*** (0.00) 1.9622*** (0.00) 1.9622*** (0.00)
IN −4.3001*** (0.00) −4.3001*** (0.00) −4.3001*** (0.00)
SE −1.3565*** (0.00) −1.3565 (0.14) −1.3565** (0.01)
AG 1.1243*** (0.00) 1.1243*** (0.00) 1.1243*** (0.00)
Constant 16.3665*** (0.00) 16.3665*** (0.00) 16.3665*** (0.00)
Wald
χ
2
/F 18648.48*** (0.00) 1494.66*** (0.00) 1280.45*** (0.00)
R-squared 0.7504 0.7504 
RMSE 1.3204 
Notes: ***p <0.01, **p <0.05, *p <0.10. P-values in parentheses.
1
Brown growth is adopted following Shirazi et al. [71], who used this term
to indicate the polluting nature of economic development at the primary stages
of the growth process in oil-exporting countries.
V. Mohamad Taghvaee et al.
Energy 311 (2024) 133300
6

sustainable development. It also denes energy intensity and renewable
energy as indicators for the energy security aspect of sustainability. In
this way, it concisely displays the estimated effects of economic
complexity and economic growth on energy intensity and renewable
energy including a comparative representation. In this table, energy-
intensive complexity and energy-saving growth imply that economic
complexity and economic growth increase and decrease energy in-
tensity, respectively. Economic complexity is energy-intensive but
growth is energy-saving, whereas economic complexity is green but
growth is brown. This nding shows that economic complexity and
growth have two different and conicting effects on the energy security
of sustainability, while each one can be mitigated and complemented by
the other. To consider this aspect, this table compares the estimated
coefcients to provide a comparative analysis. It adds a comparative
signal by the unequal symbols to compare the estimated effects in the
middle column. According to the estimated comparison, the effect of
energy-intensive complexity and brown growth is greater than the
energy-saving growth and green growth, respectively.
The results show that economic complexity increases energy in-
tensity due to the "energy-intensive complexity" of the MENA region
with energy-rich countries. This result implies that economic complexity
reduces energy efciency, rejecting Hypothesis 1, in line with Kahia
et al. [19] but contrary to Khezri et al. [61] and Abbate et al. [62].
Although this result might seem contradictory at rst, this research
explains it with the idea of energy-intensive complexity. It is expected
that economic complexity, as a proxy for economic technological
progress, would decrease the level of energy intensity and increase en-
ergy efciency. However, the results contrast this statement. The
explanation lies in the energy-intensive complexity of the region.
The concept of energy-intensive complexity suggests that the eco-
nomic complexity of MENA countries focuses on energy-intensive
diversication and exportation due to the high availability of energy
resources in the region. With abundant energy sources at relatively low
prices, entrepreneurs and exporters are motivated to produce and export
energy-intensive goods. This leads to an increase in the diversity and
exportation of energy-intensive goods, contributing to the overall eco-
nomic complexity. Based on this analysis, economic complexity and
energy intensity show a positive relationship in this sample.
Another piece of evidence supporting the idea of energy-intensive
complexity is the negative relationship between oil price and energy
intensity in the results. The estimations demonstrate a reverse rela-
tionship between oil price and energy intensity, as the price negatively
relies on the supply of energy. Due to the high availability of energy
resources in this area, the energy price is relatively low, motivating
producers and exporters to focus on energy-intensive goods. This anal-
ysis further supports the phenomenon of energy-intensive complexity,
indicating that the low price of energy resources is a key variable.
In contrast to economic complexity, economic growth shows a
negative relationship with energy intensity, in contrast with results by
Chu [39], Do˘
gan et al. [38], Romero & Gramkow [40], and Ali et al.
[43]. This result points to the case of "energy-saving growth". This
relationship holds not only for economic growth as a whole but also for
its disaggregated sectors (industry and services). Thus, the economic
structure in MENA countries reduces the share of energy in production
by redistributing the share of production inputs in an equal way. Ac-
cording to this nding, economic growth and economic complexity have
different effects on the energy sector, resulting in energy-intensive
complexity and energy-saving growth.
Furthermore, economic complexity supports the energy security of
sustainability by displaying a positive relationship with renewable en-
ergy consumption, conrming that complexity is green in the region.
This result conrms Hypothesis 2, and is consistent with Khezri et al.
[61], Abbate et al. [62], and Kahia et al. [16], but inconsistent with
Kahia and Ben Jebli [25] and Kahia et al. [19]. A reason for that resides
in the "inertia effect" of energy-intensive complexity. According to this
phenomenon, complexity increases energy intensity, which not only
leads to an overall increase in energy demand but also specically drives
up the demand for renewable energy. Therefore, the positive relation-
ship between economic complexity and renewable energy consumption
conrms that the inertia effect of energy-intensive complexity inuences
renewable energy consumption, contributing to a green complexity in
the region.
On the other hand, economic growth goes against the energy security
of sustainability by showing a negative relationship with renewable
energy consumption, conrming that growth is brown rather than green
in these countries. The brown growth is evident both in the aggregate
economic structure and the disaggregated economic sectors.
In a comparative analysis, the harmful effects of economic
complexity and growth on the energy security of sustainability are larger
than their positive effects. The energy-intensive effect of complexity and
the green nature of growth are greater than the energy-saving effect of
growth and the brown nature of complexity. This nding shows that the
current economic structure of developing countries is insufciently
capable for the achievement of sustainable development. In this way, the
more the economy grows and develops in these countries, the more they
diverge from a sustainable economy and clean environment.
Furthermore, the ndings conrm that all economic sectors improve
energy efciency, implying an energy-efcient structure of the economy
in the analyzed countries. Comparatively, the services sector shows the
most benecial role, highlighting the potential of the sector for envi-
ronmental protection policies. Moreover, the industry and services
sectors reduce renewable energy consumption. Nonetheless, the agri-
culture sector increases renewable energy consumption, stressing its
benecial role in the energy transition process.
Table 10
Pairwise Granger Panel Causality test results.
Null Hypothesis F Stat. P-Value
EI ⇎ EC 0.5014 0.4792
EC ⇎ EI 3.2888 0.0703*
EI ⇎ PO 0.0108 0.9171
PO ⇎ EI 3.1946 0.0745*
EI ⇎ Y 7.4698 0.0065***
Y ⇎ EI 1.6122 0.2048
EI ⇎ IN 3.4883 0.0624*
IN ⇎ EI 0.8618 0.3537
EI ⇎ SE 0.5317 0.4663
SE ⇎ EI 11.8731 0.0006***
EI ⇎ AG 0.1779 0.6734
AG ⇎ EI 3.7556 0.0532*
RE ⇎ EC 11.2300 0.0009***
EC ⇎ RE 0.1027 0.7487
RE ⇎ PO 0.6112 0.4347
PO ⇎ RE 0.0003 0.9856
RE ⇎ Y 1.5318 0.2165
Y ⇎ RE 5.5018 0.0194**
RE ⇎ IN 0.6855 0.4081
IN ⇎ RE 2.0807 0.1499
RE ⇎ SE 1.6303 0.2024
SE ⇎ RE 0.0691 0.7927
RE ⇎ AG 0.1122 0.7378
AG ⇎ RE 0.0987 0.7535
Notes: 2 lags. ***p <0.01, **p <0.05, *p <0.10.
Table 11
Estimated effects of economic pillar of sustainability on energy security.
Energy
security
Sustainability Economic pillar
Variables Economic
complexity
Comparison Economic
growth
Energy
intensity
Energy-intensive
complexity
>Energy-saving
growth
Renewable
energy
Green
complexity
<Brown growth
V. Mohamad Taghvaee et al.
Energy 311 (2024) 133300
7

9. Conclusions and policy implications
This paper investigates how economic complexity affects renewable
energy and energy efciency in the MENA region. Specically, we aim to
inspect the impact of economic growth, economic complexity, and oil
prices on renewable energy and energy efciency in the MENA countries
for the 1990–2017 years, using PCSE, SCC, and GLS estimators.
The results show that economic complexity increases energy in-
tensity, which implies that it contributes to environmental degradation
by decreasing energy efciency in the MENA countries. This result in-
troduces the energy-intensive nature of economic complexity. However,
economic complexity increases renewable energy consumption, which
means that it improves the environment through the promotion of clean
energies, highlighting the green nature of economic complexity. More-
over, it was found that the impacts of economic growth are diverse.
Economic growth decreases energy intensity, showing a benecial
impact on the environment by increasing energy efciency. This result
noties the energy-saving character of economic growth. Nonetheless,
economic growth negatively affects the environment by decreasing
renewable energy consumption, pinpointing the brown nature of eco-
nomic growth. Overall, the economic complexity of these countries is
energy-intensive but green, while their economic growth is energy-
saving but brown. The negative effects surpass the positive ones,
implying that the economy of developing countries needs restructuring
to become more environmentally friendly.
Based on these ndings, policymakers should adopt new policies to
improve the structure of economic sectors in an environmentally
friendly way. To achieve this end, they should formulate some plans to
encourage economic activities increasing the share of other inputs
compared to the energy in the productions, which helps reach an energy-
saving complexity [90]. In addition, they should promote consumption
and production of renewable energy to have a greener economy. In this
way, the structure of the economy would be in line with the energy
security of sustainable development. Moreover, a strategic approach to
addressing shared energy and environmental challenges among the
MENA countries may be to encourage collaboration and
knowledge-sharing among them. To address common issues related to
energy security and sustainable development, countries can pool their
resources and expertise by working together through regional
initiatives.
MENA countries should diversify their economy to create new job
opportunities, foster innovation, and reduce their reliance on oil and gas
exports. Moreover, they should carefully consider the transition towards
renewable and clean energy sources in their structural transformation to
reduce their dependence on fossil fuels and promote sustainable eco-
nomic growth [91]. To this end, they should signicantly invest in
technology and infrastructure to create new jobs and support the
development of new industries. By combining energy efciency mea-
sures with a focus on economic complexity and renewable energy
adoption, MENA countries can pave the way for a more sustainable and
resilient economic future.
Concerning the limitations of the study, here we focused only on
developing and emerging economies. For this reason, the innovative
results of energy-intensive complexity and green growth are acceptable
only for this type of country and they remain questionable for developed
countries. Therefore, future studies may inspect this topic for the
developed countries, to nd a more comprehensive insight into the
energy-saving and green characteristics of the economic complexity and
growth.
CRediT authorship contribution statement
Vahid Mohamad Taghvaee: Conceptualization, Data curation,
Methodology, Software, Writing – original draft, Writing – review &
editing. Behnaz Saboori: Data curation, Methodology, Resources, Su-
pervision, Validation, Writing – original draft. Susanne Soretz:
Supervision, Validation, Visualization. Cosimo Magazzino: Data cura-
tion, Methodology, Software, Writing – review & editing. Moosa Tatar:
Supervision, Validation.
Declaration of competing interest
None.
Data availability
Data will be made available on request.
Abbreviations
CADF Cross-section Augmented Dickey–Fuller
CO
2
carbon dioxide
GDP Gross Domestic Product
GHG greenhouse gas
GLS Generalized Least Squares
MENA Middle East and North Africa
OLS Ordinary Least Squares
PCSE Panel-Corrected Standard Errors
SCC Spatial Correlation Consistent
SDGs Sustainable Development Goals
UAE United Arab Emirates
WDI World Development Indicators
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