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INVESTIGATING THE DYNAMIC RELATIONSHIP
BETWEEN ECONOMIC GROWTH, ENERGY
CONSUMPTION, AND CO
2
EMISSIONS IN LEBANON
Imtynan Khalifeh, Mohamed Awada, AND Moustapha Badran*
1. Introduction
Climate change stands as a highly contested environmental concern on a global
scale. The progression of economic development and globalization has played
asignificant role in elevating the concentrations of carbon dioxide (CO
2
)emis-
sions and various other greenhouse gases in our atmosphere. Over the past few
*Imtynan Khalifeh is a Ph.D. candidate in Economics Sciences at the University of Montpellier,
specializing in Finance. She holds dual master’s degrees in Banking and Financial Economics from the
Lebanese University and Banking and Finance from Limoges, establishing her strong academic
background. The author’s research centers around the impact of Basel III regulations on European
banks, showcasing her expertise in finance, banking, economics, and econometrics. She has several
scientific articles published in the Journal of Energy and Development. Additionally, she actively
contributes to academia as a Lecturer of Statistics for Business at Montpellier Business School and
serves as a Teaching Assistant at the Faculty of Economics, University of Montpellier, imparting her
knowledge and fostering analytical skills among her students.
Mohamed Awada earned his Doctorate in Economic Sciences from the University of Montpellier.
In his thesis, he examined the interrelationships between energy supply, public debt, and economic
growth as applied to a group of OECD European countries. His research interests include
macroeconomics, finance, and energy economics with a focus on European countries. He has several
scientific articles published in the Journal of Energy and Development and other journals. His teaching
expertise covers several subjects including corporate finance, financial analysis, statistics,
microeconomics, macroeconomics, and financial mathematics. He is currently working as a contractual
lecturer at the Faculty of Economics in Montpellier, primarily teaching a course on private economic
calculus and leading econometrics tutorials. (continued)
The Journal of Energy and Development, Vol. 49, Nos. 1-2
Copyright #2024 by the International Research Center for Energy and Economic Development
(ICEED). All rights reserved.
107
decades, climate change has emerged as a crucial topic of conversation among
political figures and knowledgeable individuals (Ozturk et al., 2010).
1
Following the influential research conducted by Kraft et al. (1978), which was
likely prompted by the oil price shock of 1973, the correlation between energy
consumption and economic growth, commonly known as the energy-GDP nexus,
has been extensively explored.
2
Numerous studies have delved into this subject,
recognizing its significance.
In their pursuit of sustainable economic development and improved living stan-
dards, developing countries are consuming substantial amounts of energy. How-
ever, this increased energy consumption comes hand in hand with the emission of
pollutants that significantly contribute to climate change (Alkhathlan et al., 2013).
3
TheriseinenergyconsumptionandCO
2
emissions is an ongoing trend observed
across various countries, with particular emphasis on developing nations like
Lebanon. According to the Norwegian Refugee Council (2022), Lebanon hosts the
largest proportion of refugees globally, which present 19.8 percent of the country’s
population.
4
Even more concerning is the impact of the aforementioned scenario on
environmental quality. In Lebanon, the environment has consistently taken a back-
seat, primarily due to the presence of an inadequate institutional and legislative
framework. The country also faces challenges in implementing effective policies to
address environmental issues, alongside political obstacles that hinder the implemen-
tation of sustainable reforms (World Bank, 2022).
5
For example, Lebanon is cur-
rently grappling with an energy deficit, and certain regions, particularly rural areas,
continue to lack access to electricity. The demand for energy in the country sur-
passes the available supply. Compounded by a struggling economy, Lebanon faces
challenges in importing fossil fuels on a significant scale. The combination of energy
deficiencies, limited electrification, and economic constraints presents complex
obstacles for meeting the energy needs of the country. In addition, Lebanon is suf-
fering from CO
2
emissions and experiencing a rise in pollution-related incidents.
The severity of these incidents is more pronounced in urban neighbourhoods, pri-
marily due to the escalating emissions in these densely populated areas, including
Beirut city (Mokalled et al., 2018; Saliba et al., 2010; and Massoud et al., 2011).
6
The aim of this research paper is to investigate the correlation between GDP
growth, energy consumption, and CO
2
emissions in Lebanon from 1970 to 2021.
Moustapha Badran holds a Doctorate in Economic Sciences from the University of Montpellier,
with a research focus on corporate finance, capital structure, and financial development. Currently, he
serves as an adjunct lecturer in Economics and Social Sciences at the University of Grenoble Alpes. He
has several scientific articles published in the Journal of Energy and Development and other journals.
His teaching portfolio spans a range of subjects, including theoretical and time series econometrics, as
well as both macroeconomics and microeconomics.
Acknowledgements: The authors would like to thank anonymous reviewers for their valuable
suggestions and helpful comments which have greatly enhanced the quality of this paper.
108 THE JOURNAL OF ENERGY AND DEVELOPMENT
The choice to focus on Lebanon as a case study is driven by several reasons.
Firstly, despite the abundance of studies exploring the energy-GDP relationship,
there is a scarcity of research that specifically examines Lebanon. Secondly,
Lebanon presents a unique situation as both an energy-consuming country and one
grappling with CO
2
related challenges. Thirdly, Lebanon faces economic, social,
and geographical threats, which adds an additional layer of significance to the
study. Lastly, previous studies in Lebanon have not adequately addressed the role
of CO
2
emissions.
2. Brief Literature Review
Extensive scientific inquiry has been dedicated to investigating the intricate
interplay among energy consumption, carbon dioxide emissions, and economic
growth across diverse contexts. Numerous studies have diligently examined these
relationships within distinct countries and regions, imparting significant under-
standing of the underlying dynamics at play in this domain.
Abbasi et al. (2021) observed the negative impact of CO
2
emissions while
energy consumption, industrial growth, and urbanization had positive impacts on
Pakistan’seconomicgrowth.
7
Acaravci et al. (2010) found a two-way causal rela-
tionship between energy consumption and economic growth in Europe, with
increased CO
2
emissions adversely affecting economic growth.
8
Acheampong
(2018) demonstrated that there is a bidirectional causal relationship between CO
2
emissions and energy consumption, and that economic growth has a unidirectional
impact on CO
2
emissions.
9
Adams et al. (2020) found bidirectional causality
between energy use and carbon emissions and a unidirectional causality between
economic policy uncertainty and carbon emissions.
10
Adedoyin et al. (2020)
emphasized that economic policy uncertainty has a one-way impact on CO
2
emis-
sions, and that there is a two-way causal relationship between energy consumption
and economic growth.
11
Region-specific investigations have elucidated the intricate connections between
energy consumption, carbon emissions, and economic growth across diverse
domains. Adewuyi et al. (2017), Ahmad et al. (2016), Ahmed et al. (2022), Akadiri
et al. (2022), and Akadiri et al. (2019) meticulously explored these dynamics
within the contexts of West Africa, India, and other relevant regions.
12
Akpan et al.
(2012) specifically discerned the interrelationships between electricity consump-
tion, carbon emissions, and economic growth in Nigeria.
13
Ali et al. (2023) focused on emerging markets in Asia and showed the dynamic
relationship between renewable and non-renewable energy consumption, economic
growth, and CO
2
emissions.
14
While Ali et al. (2021) tested the environmental
Kuznets curve hypothesis by studying Pakistan and examining the effects of fossil
energy consumption, economic development, and foreign direct investment on
109LEBANON: GROWTH, ENERGY, AND CO
2
EMISSIONS
CO
2
emissions.
15
Al-Mulali (2014), Al-Mulali et al. (2012), Andreoni et al.
(2012), and Alshehry et al. (2015) examine the impact of energy use and carbon
emissions on economic growth in various regions, including Sub-Saharan Africa,
Italy, and Saudi Arabia.
16
A plethora of comprehensive studies has extensively investigated a wide range
of scenarios, imparting invaluable insights. Researchers such as Ang (2008), Anto-
nakakis et al. (2017), Anwar et al. (2020), Ardakani et al. (2019), Arouri et al.
(2012), Asafu-Adjaye (2000), Asumadu-Sarkodie et al. (2017), and others have
diligently explored the intricate interdependencies among energy consumption, car-
bon emissions, and economic growth in countries including Malaysia, Far East
Asian countries, Middle Eastern and North African countries, as well as Senegal.
17
Furthermore, Audi et al. (2016), Awodumi et al. (2020), Aye and Edoja (2017),
Ayres et al. (2010), Banday et al. (2019, 2020), and several other researchers
have scrutinized these dynamics within Lebanon’s oil-producing regions, various
manufacturing economies, diverse global regions, and specificcountries.
18
More-
over, notable contributions from studies by Begum et al. (2015), Bekhet et al.
(2013, 2017), Benali et al. (2020), Bhat (2018), Boukhelkhal (2022), Bozkurt et al.
(2014), and others have provided significant insights into these relationships in
countries such as Singapore, GCC countries, emerging markets, Africa, Turkey,
and Malaysia.
19
Additionally, a multitude of research endeavors carried out in
France, Turkey, the MENA region, BRICS countries, globally encompassing anal-
yses, Bangladesh, Lebanon, Pakistan, and various other regions and countries have
significantly contributed to our comprehension of the complex interactions among
the variables of interest (Fei et al., 2011; Cowan et al., 2014; Ghosh et al., 2014;
Can et al., 2016; Haseeb et al., 2017; Danish et al., 2018; Gorus et al., 2019; Fan
et al., 2020; El Menyari, 2021; Chen et al., 2022; Chen et al., 2023).
20
Additionally, the relationship between energy consumption, carbon emissions,
and economic growth has also been investigated in specificcountries.Ocaletal.
(2013) and Ozturk et al. (2010) examined this relationship in Turkey, Odhiambo
(2009) explored it in Tanzania, Odhiambo (2012) focused on South Africa, and
Odugbesan et al. (2020) studied the MINT countries (Mexico, Indonesia, Nigeria,
and Turkey).
21
Ohlan (2015) examined the nexus in India, while Omri (2013,
2014) focused on Middle East and North Africa (MENA) countries.
22
Ozcan et al.
(2020) explored the relationship in OECD countries.
23
Studies focused on specific regions or countries include Rahman et al. (2022)
and Rahman (2020) in Bangladesh, Rahman et al. (2020) in South Asia, and Rah-
man et al. (2021) in emerging economies (NICs).
24
Raihan et al. (2022) explored
Peru, and Rasoulinezhad et al. (2018) focused on the Commonwealth of Indepen-
dent States, while Raza et al. (2019) investigated the relationship in the United
States and studied the impact of the three variables in different sectors.
25
Peng et al.
(2020), Saboori et al. (2014), and Tamba (2017) studied the transport sector, while
110 THE JOURNAL OF ENERGY AND DEVELOPMENT
Raihan et al. (2022) focused on the agricultural sector and Zhang et al. (2021) stud-
ied the tourism sector.
26
Moreover, researchers explored the role of additional factors in shaping the
relationship between energy use, carbon emissions, and economic growth. Salman
et al. (2019) investigated the impact of institutional quality, while Vasylieva et al.
(2019) explored the role of corruption.
27
Sufyanullah et al. (2021) focused on the
impact of globalization, Zafar et al. (2022) studied the effects of urbanization, and
Ziaei (2015) studied the effects of financial development.
28
Taken together, these scientific studies have greatly enhanced our understanding
of the complex interplay between energy use, carbon emissions, and economic
growth in different countries and regions.
3. Model and Methodology
Theoretical Framework —Unit Root Tests: This paper builds upon theo-
retical frameworks used in prior studies to examine the relationship between
energy consumption, economic growth, and CO
2
emissions. To explore this causal
relationship, we conducted tests for stationarity of the series using the augmented
Dickey-Fuller (1979-1981)
29
and Phillips-Perron (PP) tests. Perron (1988, 1989)
30
proposed a unit root test that allows for structural breaks, considering three alterna-
tive models: the crash model, which involves a shift in the intercept; the changing
growth model, which involves a change in the slope; and the model with changes
in both the intercept and slope.
Previous investigations have highlighted that conventional unit root tests do not
provide sufficient evidence to reject the unit root hypothesis for series exhibiting
trend stationarity with a structural break. Conversely, the Perron (1989) test has
faced criticism due to its assumption of exogeneity regarding the time of the break,
meaning that the timing of the break is known beforehand. To address this limita-
tion, Zivot et al. (1992)
31
made significant advancements to the Perron unit root
tests by incorporating endogeneity in the consideration of the breakpoint (TB).
To examine the presence of a unit root against the alternative of a trend statio-
narity process with a structural break in slope and intercept, we utilized the follow-
ing regression model:
Ln GDPt5
b
01
b
1ln ECt1
b
2ln CO2t1
«
t
Ln ECt5
a
01
a
1ln GDPt1
a
2ln CO2t1
«
t
Ln CO2t5
u
01
u
1ln GDPt1
u
2ln ECt1
«
t
Where, Ln GDPtis the logarithm of GDP per capita, Ln ECtthe logarithm of energy
consumption per capita, and Ln CO2tthe logarithm of CO
2
emissions per capita.
111LEBANON: GROWTH, ENERGY, AND CO
2
EMISSIONS
The significance of testing for stationarity lies in the fact that non-stationary
series regression can lead to inefficient coefficients and forecasts, as well as invalid
significance tests (Granger, 1969).
32
In contrast, stationary time series exhibit
regression of integration, where the mean and variance remain constant over time.
To assess the presence of a unit root, we employed either the Dickey-Fuller Simple
or Augmented Test (Dickey and Fuller, 1979-1981) or the Phillips-Perron Test
(1988), depending on the presence of homoscedastic or heteroscedastic variances,
respectively.
33
Theoretical Framework —Cointegration Analysis within a VAR
Framework: The methodology employed in this study involved several steps to
examine the long-run relationship between the variables. Initially, the stationarity
of the data was tested to ensure the suitability of applying cointegration analysis.
Subsequently, the Johansen cointegration test was conducted within a Vector Auto-
regressive (VAR) framework. The VAR model is based on a system of delayed
equations, where each variable acts as both an explanatory and an explained vari-
able. The optimal delay order (P) for the VAR model was determined through the
minimization of the Akaike criterion. Furthermore, the Engle-Granger test statistics
were employed to identify the rank of the matrix capturing the cointegration rela-
tionships among the selected variables. Specifically, considering two stationary
time series, denoted as xtand yt, the VAR(P) model can be represented as follows:
xt5
a
11X
P
j51
b
1,jxt2j1X
P
j51
g
1,jyt2j1
«
1,t
yt5
a
21X
P
j51
b
2,jxt2j1X
P
j51
g
2,jyt2j1
«
2,t
(
a
i,
b
i,j,
g
i,j) represent the parameters of the VAR(P) and
«
i,tthe innovations that
follow an i.i.d. process. (0,
s
2
«
)withi51, 2 and j 51….P.
Theoretical Framework —Granger Causality Test: In order to analyze
the causality among variables, we employed a vector error correction model. This
model allowed us to estimate the lagged error correction terms ðECTt21Þderived
from the long-run co-integration relationship, indicating the presence of co-integration
among the variables. Granger (1969) defined causality as the ability of variable ytis
included in xt.
34
Using a VAR model, we tested the hypothesis that ytdoes not cause
xtby imposing restrictions on its parameters, such as
g
1;15
g
1;25
g
1,P50. This
hypothesis was examined using statistical tests such as the Fisher, Wald, or
Likelihood-Ratio test. Additionally, we also investigated the inverse relationship, i.e.,
whether xtdoes not cause yt,bytestingif
b
1;15
b
1;25
b
1,P50.
Theoretical Framework —Sims Causality Test: Sims (1980) presents a
slightly different specification of the test, considering that if the future values of y1t
112 THE JOURNAL OF ENERGY AND DEVELOPMENT
can explain the present values of y2t,theny2tis the cause of y1t.
35
This is a Fisher
test of coefficient nullity. This is represented by the following expression:
y1t5a0
11X
P
i51
a1
1iy1t2i1X
P
i51
a2
1iy2t2i1X
P
i51
b2
iy2t1i1
«
1,t
y2t5a0
21X
P
i51
a1
2iy1t2i1X
P
i51
a2
2iy2t2i1X
P
i51
b1
iy1t1i1
«
2,t
y1tdoes not cause y2tif the following hypothesis is accepted
H0: b2
15b2
25::: 5b2
p50
y2tdoes not cause y1tif the following hypothesis is accepted H0:
b1
15b1
25::: 5b1
p50
Data and Variables: This study used the annual time series data of the Leba-
non economy from 1970-2021. Annual data used in this study includes GDP per
capita, energy consumption per capita, and CO
2
emissions per capita. In this study,
we measure the annual GDP per capita in U.S. dollar (constant prices) and CO
2
emissions per capita in metric tons and the energy consumption per capita in kilo-
grams of oil equivalent (kgep). The data of this study are collected from World
Bank Database. All the variables used in natural logarithms to address the problem
of heteroscedasticity and reduce the differences between variables related to differ-
ences in their units of measurement (Rahman et al., 2020; Lee, 2006; Coon-
doo, 2002).
36
Figure 1 presents the time plots of GDP, the energy consumption, and the CO
2
emissions in Lebanon for the period 1970-2021. Visual inspection of the graph
indicates positive correlation between the three series. Lebanon is confronted with
multiple challenges in the near future due to its heavy reliance on energy imports,
inadequate capacity to meet domestic electricity needs, escalating global prices,
and the persistent pressure on developing nations to adopt binding obligations for
CO
2
emissions reduction. To effectively address these challenges, it becomes cru-
cial to gain a comprehensive understanding of the causal connections among
energy consumption, economic growth, and CO
2
emissions. Such understanding
will facilitate the development and implementation of appropriate national energy
and environmental policies.
4. Empirical Results
Unit Root Tests: The Philips-Perron (PP) unit root was considered more
appropriate to address the heteroscedasticity exclusion. As observed in Table 1, the
results demonstrated that all the variables were non-stationary at constant and trend
assumptions but became stationary at the first difference. Hence, it can be
113LEBANON: GROWTH, ENERGY, AND CO
2
EMISSIONS
concluded that the variables of log GDP per capita, energy consumption per capita,
and CO
2
emissions per capita are not stationary. Additionally, the null hypothesis
of a unit root is rejected for all series when examining their first differences.
Cointegration Tests: Once we have verified that our variables are integrated
at the same first order, we proceed to conduct Johansen’s cointegration test. This
test utilizes the Trace and Max-Eigen Statistics to examine whether there is a long-
term equilibrium among logEC, logGDP, and logCO
2
, indicating their cointegra-
tion. The Trace test’s null hypothesis states that there are, at most, rcointegration
relationships, while the alternative hypothesis suggests there are more than rrela-
tionships. On the other hand, the Max-Eigen test’s null hypothesis assumes exactly
rcointegration relationships, while the alternative hypothesis proposes r11
relationships.
The accuracy of the Johansen cointegration test depends on the determination
of the number of lags. We select this number using the Akaike information crite-
rion (AIC), Schwarz information criterion (SBC), and Hannan-Quinn (HQ) infor-
mation criterion. The result indicates that the chosen number of lags is P52. These
findings are presented in Table 2.
Figure 1
THE VARIATION OF ENERGY, GROWTH, AND TRANSITION IN LEBANON OVER TIME
FROM 1970-2021 (LOG)
1
0
3
2
1970 1974 20181978 1982 1986 1990 1994 1998 2002 2006 2010 2014
Energy Consumption CO2Emissions Economic Growth
5
4
7
6
9
8
114 THE JOURNAL OF ENERGY AND DEVELOPMENT
In Table 2, each line represents a null hypothesis, denoted as r5{0, 1, 2},
along with the corresponding Trace and Max-Eigen statistics and their critical
values. If the Trace or Max-Eigen statistic exceeds the critical value at the
5-percent threshold, the null hypothesis is rejected. In this case, the null hypothesis
suggesting a cointegration rank of r50 is rejected at the 5-percent threshold.
Therefore, we cannot reject the null hypothesis that r51, indicating a cointegra-
tion rank of 1.
Long-Run Adjustment Analysis: After conducting cointegration tests, we
proceeded with the estimation of Vector Error Correction Model (VECM) to ana-
lyze the short-term relationships and assess the speed of long-run adjustment. The
VECM allows us to examine both the direction and intensity of these relationships.
Table 1
PHILLIPS-PERRON UNIT-ROOT TEST RESULTS
a
Variables
Philips-Perron
Constant and trend First Difference
T-Statistic Prob. T-Statistic Prob.
lnEC 21.2355 (0.8921) 26.0029*** (0.000)
lnGDP 23.0643 (0.1258) 28.3083*** (0.000)
lnCO
2
23.4519 (0.0558) 28.6708*** (0.000)
a
*, **, and *** indicates 1%, 5%, and 10%, respectively, while lag selection is based on automatic
Schwarz Info Criterion (SIC). Abbreviations: lnEC 5log of energy consumption per capita; lnGDP 5
log of gross domestic product per capita; and lnCO
2
5log of CO
2
emissions per capita.
Table 2
JOHANSEN COINTEGRATION TEST: TRACE STATISTIC AND MAX-EIGEN STATISTIC
a
Trace statistic Max-Eigen statistic
Null Hypothesis
83.3780* 83.3780*
r500.000 0.000
[35.0109] [35.0109]
83.3780* 83.3780*
r510.000 0.000
[35.0109] [35.0109]
83.3780* 83.3780*
r520.000 0.000
[35.0109] [35.0109]
a
Trace test indicates 3 cointegrating equations at the 0.05 level using the trace statistic and 3
cointegration equations using the Max-Eigen statistic; * denotes rejection of the hypothesis at 0.05
level; ** Mackinnon-Haug-Michelis(1999) p-values.
115LEBANON: GROWTH, ENERGY, AND CO
2
EMISSIONS
Our findings reveal significant long-term causal relationships between the vari-
ables. The coefficient for CO
2
is negative and statistically significant (p ,0.05),
indicating an inverse relationship between CO
2
emissions and the GDP growth.
Similarly, energy consumption exhibits a negative and significant relationship
(p ,0.05). These results highlight the importance of endogenous variables (CO
2
and
EC) in the adjustment process when the system deviates from its equilibrium state.
Table 3 presents the estimated long-term adjustment factors. The coefficient for
CO
2
emission per capita indicates an annual adjustment speed of 0.734 percent,
suggesting a gradual reduction in emissions over time. Likewise, energy consump-
tion demonstrates an annual adjustment speed of 0.301 percent. These figures pro-
vide insights into the rate at which the variables converge towards their long-run
equilibrium.
Overall, our analysis reveals significant long-term relationships and emphasizes
the role of CO
2
emissions and energy consumption in the adjustment process. The
estimated adjustment speeds further contribute to our understanding of the dynam-
ics among the variables over the long term.
Short-Run Analysis: Based on the findings presented in Table 4, the variable
being examined is the GDP growth. The error correction term is both negative and
statistically significant, indicating the presence of a long-term equilibrium relation-
ship. Among the variables considered, only CO
2
with a lag of one period shows
statistical significance. This suggests that when CO
2
with a one-period lag
increases, there is a corresponding decrease in GDP growth, as indicated by the
negative coefficient associated with this variable.
Based on the findings presented in Table 5, the variable under examination is
CO
2
.TheCO
2
variable with a one-period lag is found to be negative and statistically
significant, indicating that an increase in CO
2
emissions in the previous period is
associated with a decrease in the current CO
2
levels. Additionally, the variable repre-
senting energy consumption (EC) with a two-period lag shows a positive and statis-
tically significant relationship. This suggests that an increase in energy consumption
two periods ago is associated with a subsequent increase in CO
2
emissions.
Based on the findings presented in Table 6, the variable being examined is
energy consumption (EC). The error correction term is positive and statistically
Table 3
VECTOR ERROR CORRECTION ESTIMATIONS: LONG-RUN ADJUSTMENT
a
Variable Coefficient Standard Error t-statistic
lnGDPD(21) 1.000 N/A N/A
lnCO
2
(21) 20.734* 0.150 24.888
lnEC(21) 20.301* 0.082 23.648
a
*denotes statistical significance at p ,0.05.
116 THE JOURNAL OF ENERGY AND DEVELOPMENT
significant, suggesting the existence of a long-term equilibrium relationship among
the variables. Furthermore, the variable representing GDP with a two-period lag is
found to be negative and statistically significant. This implies that a decrease in GDP
two periods ago is associated with a subsequent decrease in energy consumption.
Furthermore, the observation that the adjusted R-squared is lower than the
R-squared suggests that the inclusion of further independent variables in the model
may not substantially enhance its ability to explain the variation in the dependent
variable.
Table 4
VECTOR ERROR CORRECTION ESTIMATIONS: EMPIRICAL RESULT OF SHORT-RUN
TAKING GDP GROWTH AS DEPENDENT VARIABLE
a
Variable Coefficient Standard Error t-statistic
CointEQ1 21.325 0.435 23.04
GDP (21) 0.339 0.317 1.06
GDP (22) 0.014 0.223 0.063
CO
2
(21) 20.850* 0.309 22.752
CO
2
(22) 0.036 0.256 0.144
EC (21) 20.147 0.128 21.152
EC (22) 20.051 0.088 20.590
c20.0003 0.002 20.111
R-squared 0.545
Adj. R-squared 0.465
a
*denotes statistical significance at p ,0.05.
Table 5
VECTOR ERROR CORRECTION ESTIMATIONS: EMPIRICAL RESULT OF SHORT-RUN
TAKING CO
2
AS INDEPENDENT VARIABLE
a
Variable Coefficient Standard Error t-statistic
CointEQ1 0.459 0.347 1.323
GDP (21) 20.346 0.252 21.371
GDP (22) 20.122 0.178 20.689
CO
2
(21) 20.512* 0.246 22.080
CO
2
(22) 0.013 0.020 0.063
EC (21) 0.188 0.102 1.844
EC (22) 0.170* 0.070 2.426
c20.0002 0.002 20.128
R squared 0.561
Adj. R-squared 0.484
a
*denotes statistical significance at p ,0.05.
117LEBANON: GROWTH, ENERGY, AND CO
2
EMISSIONS
Granger Causality: Our aim in conducting the Granger Causality analysis
was to determine the directional relationship between GDP growth, CO
2
emissions,
and energy consumption in Lebanon. The findings presented in Table 7 reveal a
significant unidirectional relationship between energy consumption and GDP
growth. This is supported by the probability value of 0.0503, which is close to the
conventional significance level of 0.05. As a result, we can reject the null hypothe-
sis and conclude that there is evidence of a Granger causality relationship from
energy consumption to GDP growth.
Sims Causality: The objective of our study was also to employ Sims Causality
analysis to ascertain the causal directionality between GDP growth, CO
2
emissions,
and energy consumption in Lebanon. The outcomes reported in Table 8 reveal a
statistically significant bidirectional causality relation between economic growth
and the other variables. This assertion is substantiated by the calculated probability
Table 6
VECTOR ERROR CORRECTION ESTIMATIONS: EMPIRICAL RESULT OF SHORT-RUN
TAKING ENERGY CONSUMPTION AS INDEPENDENT VARIABLE
a
Variable Coefficient Standard Error t-statistic
CointEQ1 1.999* 0.781 2.558
GDP (21) 20.521 0.568 20.917
GDP (22) 20.839* 0.400 22.094
CO
2
(21) 0.402 0.553 0.726
CO
2
(22) 0.860 0.459 1.873
EC (21) 20.058 0.229 20.254
EC (22) 20.197 0.157 21.252
c20.002 0.005 20.412
R-squared 0.467
Adj. R-squared 0.373
a
*denotes statistical significance at p ,0.05.
Table 7
GRANGER CAUSALITY TEST
a
Null Hypothesis Obs F-statistic Probability (p-value)
EC does not Granger Cause CO
2
49 2.339 0.108
CO
2
does not Granger Cause EC 0.036 0.964
GDP does not Granger Cause CO
2
49 0.218 0.804
CO
2
does not Granger Cause GDP 2.354 0.106
GDP does not Granger Cause EC 49 2.412 0.101
EC does not Granger Cause GDP 3.201** 0.05
a
*, **, and *** indicate the level of significance at 10%, 5%, and 1%, respectively.
118 THE JOURNAL OF ENERGY AND DEVELOPMENT
value of 0.0503, which approximates the conventional significance level of 0.05.
Moreover, we are able to reject the null hypothesis and establish that there exists
empirical support for a Sims unidirectional relationship from energy consumption
to CO
2
emissions.
Test Validation: The Granger analysis of causality conducted on Lebanese
data demonstrates a singular unidirectional causal link, where energy consumption
influences economic growth. Energy consumption plays a dominant role in driving
economic growth, both directly in the production process and indirectly as a com-
plement to labor and capital. In this context, energy is considered as an additional
factor of production alongside the traditional factors of capital and labor. As noted
by Yu (1985), Tsani (2010), Belke (2011), and Destek (2016), the implementation
of energy policy affects the level of production.
37
Examining the relationship between energy consumption (EC) and CO
2
emis-
sions, the historical analysis based on Granger causality indicates no significant
association between these two variables. However, an anticipation analysis utiliz-
ing the Sims test reveals a unidirectional causality from energy consumption to
CO
2
emissions. This implies that the present values of energy consumption can
explain the future values of CO
2
emissions, indicating that the increase in CO
2
emissions is a consequence of excessive energy consumption. Consequently, it is
advisable for the Lebanese government to prioritize renewable energy sources to
mitigate the environmental impact.
In terms of the association between GDP and CO
2
emissions, the historical
analysis employing Granger causality fails to reveal any significant relationship
between these two variables. However, a predictive investigation utilizing the Sims
test demonstrates a bidirectional connection between GDP and CO
2
emissions.
This implies that the increase in CO
2
emissions is a consequence of rising GDP,
and conversely, GDP growth is influenced by CO
2
emissions. Hence, it under-
scores the importance of implementing renewable energy sources within industries
and manufacturing sectors in Lebanon to mitigate CO
2
emissions while simulta-
neously promoting GDP growth.
Table 8
SIMS CAUSALITY TEST
Null Hypothesis Obs F-statistic Fisher value
EC does not Sims Cause CO
2
49 15.81 4.05
CO
2
does not Sims Cause EC 2.87 4.05
GDP does not Sims Cause CO
2
49 16.36 4.05
CO
2
does not Sims Cause GDP 14.01 4.05
GDP does not Sims Cause EC 49 8.29 4.05
EC does not Sims Cause GDP 17.30 4.05
119LEBANON: GROWTH, ENERGY, AND CO
2
EMISSIONS
When examining the relationship between energy consumption (EC) and GDP
growth, it is evident that both the Granger causality analysis and the Sims test vali-
date the influence of EC on GDP growth. The Granger causality analysis establishes
a unidirectional causality, indicating that changes in EC lead to subsequent changes
in GDP growth. This suggests that energy consumption is a crucial driver of eco-
nomic expansion. Additionally, the Sims test further expands our understanding by
revealing a bidirectional relationship between EC and GDP. This implies that not
only does energy consumption affect GDP growth, but GDP growth also reciprocally
influences energy consumption. The interdependence between these two factors high-
lights the intricate dynamics at play within the energy and economic systems.
In summary, based on the Vector Error Correction Model (VECM) analysis, we
can deduce that both energy consumption (EC) and CO
2
emissions have a long-
term impact on GDP growth in Lebanon. Energy consumption plays a fundamental
role in reducing CO
2
emissions and stimulating GDP growth. It is crucial for Leba-
nese authorities to exercise conscious energy consumption practices and recognize
the significance of EC in the economic cycle.
5. Conclusion
Lebanon, a country nestled in the eastern Mediterranean region, finds itself fac-
ing a challenging and complex economic and environmental landscape. The nation
has been grappling with a series of socio-political and economic crises that have
significantly impacted its overall development trajectory. As Lebanon navigates
through these turbulent times, it is essential to examine key indicators such as
GDP, energy consumption, and CO
2
emissions to gain a deeper understanding of
the current situation. Given the intricate relationship between GDP, energy con-
sumption, and CO
2
emissions, it becomes evident that addressing these challenges
necessitates a comprehensive and integrated approach.
The utilization of the Granger approach to analyze causality patterns in Leba-
nese data provides compelling evidence of a robust unidirectional causal
Table 9
SUMMARY CAUSALITY TESTS
Null Hypothesis Obs Granger Sims
EC does not Cause CO
2
49 NO YES
CO
2
does not Cause EC NO NO
GDP does not Cause CO
2
49 NO YES
CO
2
does not Cause GDP NO YES
GDP does not Cause EC 49 NO YES
EC does not Cause GDP YES YES
120 THE JOURNAL OF ENERGY AND DEVELOPMENT
relationship. Specifically, it establishes that energy consumption wields a signifi-
cant and influential role in driving economic growth. The impact of energy con-
sumption on economic expansion is twofold: it directly contributes to the
production process and functions indirectly as a complementary factor to both
labor and capital. In this analytical framework, energy assumes a notable position
as an additional factor of production, alongside the traditionally recognized factors
of labor and capital. This recognition underscores the intricate interdependence of
these factors in shaping economic outcomes.
Our findings provide valuable insights for policymakers and stakeholders,
emphasizing the need to develop and implement effective energy policies that
account for the intricate dynamics between energy consumption, labor, capital, and
overall economic outcomes. By doing so, it is possible to harness the potential of
energy as a catalyst for sustainable and robust economic development in Lebanon.
The meticulous examination of the relationship between energy consumption
(EC) and CO
2
emissions reveals intriguing insights. Employing the Granger causal-
ity approach, the historical analysis fails to provide substantial evidence indicating a
significant association between these variables. However, employing a forward-
looking perspective through the Sims test, a unidirectional causal connection
emerges, specifically from energy consumption to CO
2
emissions. This noteworthy
finding indicates that current energy consumption levels possess the capacity to
shed light on future CO
2
emission patterns, suggesting that the observed increase in
CO
2
emissions primarily stems from excessive energy consumption.
The implications of these findings are substantial and warrant attention. It is
strongly advised that the Lebanese government gives utmost priority to the adop-
tion of renewable energy sources. This strategic shift towards renewable energy
not only helps mitigate the adverse environmental impact associated with CO
2
emissions but also cultivates a sustainable and environmentally friendly energy
sector. By embracing alternative and renewable energy sources, Lebanon can take
significant strides towards achieving its environmental objectives while concur-
rently contributing to global endeavors aimed at combating climate change. This
transformative step not only promotes ecological balance but also paves the way
for a more resilient and prosperous future.
Thorough investigation of the association between GDP and CO
2
emissions,
employing the rigorous Granger causality approach, fails to reveal substantial evi-
dence of a significant relationship between these variables based on historical anal-
ysis. However, a forward-looking and anticipatory investigation using the Sims
test uncovers a noteworthy bidirectional connection between GDP and CO
2
emis-
sions. This insightful finding suggests that the increase in CO
2
emissionsisadirect
consequence of the expanding GDP, and conversely, the magnitude of GDP
growth is influenced by the level of CO
2
emissions.
Our findings underline the paramount importance of integrating renewable
energy sources within the industrial and manufacturing sectors in Lebanon. By
121LEBANON: GROWTH, ENERGY, AND CO
2
EMISSIONS
adopting this approach, it becomes possible to effectively mitigate CO
2
emissions
while simultaneously fostering GDP growth. This strategic shift aligns harmoni-
ously with the overarching sustainable development goals, facilitating the transition
towards a greener and more resilient economy. By embracing renewable energy
sources, Lebanon can actively contribute to global efforts aimed at addressing cli-
mate change and promoting environmental sustainability. This transformative
approach not only mitigates the adverse environmental impact of CO
2
emissions
but also enhances the long-term economic prospects of the nation. It creates an
environment conducive to sustainable growth, enabling Lebanon to achieve a har-
monious balance between economic progress and environmental stewardship.
The relationship between energy consumption (EC) and GDP growth reveals
compelling evidence from both the Granger causality analysis and the Sims test,
supporting the significant impact of EC on GDP growth. The Granger causality
analysis establishes a unidirectional causality, illuminating that changes in EC lead
to subsequent changes in GDP growth. This finding underscores the pivotal role
played by energy consumption as a fundamental driver of economic expansion.
Moreover, the Sims test enhances our understanding by unveiling a bidirectional
relationship between EC and GDP. This signifies that not only does energy con-
sumption exert an influence on GDP growth, but reciprocally, GDP growth also
influences energy consumption. The interconnectedness between these two factors
accentuates the intricate dynamics within the energy and economic systems.
Our results shed light on the mutual influence and interdependence between
energy consumption and GDP growth. They emphasize the complex relationship
and underscore the necessity of comprehensively understanding the underlying
dynamics. By gaining a comprehensive understanding of these dynamics, policy-
makers can make informed decisions regarding energy policies, thereby fostering
sustainable economic growth and ensuring the efficient allocation of resources
within the energy sector. Such informed policy decisions can pave the way for a
more resilient and prosperous economic future.
Against this backdrop, it is imperative to explore innovative solutions, leverage
international collaborations, and engage in concerted efforts to ensure the sustain-
able development of Lebanon’s economy while mitigating its environmental
impact. By doing so, Lebanon can strive towards achieving an appropriate balance
between economic growth, energy consumption, and environmental preservation,
ultimately paving the way for a prosperous and sustainable future.
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