Environment and Development Economics, page 1 of 21.

© Cambridge University Press 2017

doi:10.1017/S1355770X1700016X

CO2 emissions and militarization in G7

countries: panel cointegration and trivariate
causality approaches

MELIKE BILDIRICI
Yildiz Technical University, FEAS, Department of Economics,
Davutpaşa Campus, Esenler, Istanbul, Turkey.
Email: melikebildirici@gmail.com

Submitted 1 June 2016; revised 10 December 2016, 5 March 2017; accepted 1 April 2017

ABSTRACT. This paper aims to test the relation among militarization, CO2 emissions,
economic growth and energy consumption in G7 countries from 1985 to 2015 via
panel methods. Long- and short-run coefficients and the causal relationship between
the variables are important for G7 countries’ energy policies and strategy. Cointegra-
tion among CO2 emissions, militarization, energy consumption and economic growth
was determined by using panel Johansen and panel autoregressive distributed lag
(PARDL) methods. Further, the panel trivariate causality test was applied and unidirec-
tional causalities from militarization to CO2 emissions and from energy consumption
to CO2 emissions were found. The evidence of bidirectional causality between per
capita GDP and militarization, between per capita GDP and energy consumption, and
between energy consumption and militarization was determined. The paper recom-
mends that environmental and energy policies must recognize the differences in the
relation between militarization, energy consumption and economic growth in order to
maintain sustainable economic growth in the G7 countries.

1. Introduction
Especially since WWII, the carbon dioxide (CO2 ) emission level has risen
due to militarization and economic growth around the world, and thus the
level of CO2 emissions has approached the ceiling of CO2 emissions in
the atmosphere at which environmental degradation will be irreversible.
The level of CO2 emissions throughout the world was 32.1 billion tons in
2015, remaining essentially flat since 2013 (IEA, 2016). Energy-related CO2
emissions increased by 5,406 MMmt in the year 2014 (EIA, 2015).
Except for the years of major recession between 2007 and 2009, after an
average annual increase of CO2 emissions of 3.8 per cent per year since
2003, the annual increase in the years 2012 and 2013 was at about half the
level of the increases in the preceding decade. The main reason why emis-
sions in the years 2012 and 2013 were smaller than predicted is due to the
fact that economic growth in those years was below the annual average.
In 2012 and 2013 the global economy grew at the level of 3.4 per cent and

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 2 Melike Bildirici

3.1 per cent, respectively, while the average annual growth rate of GDP
since 2003 is 3.9 per cent (IMF, 2014; Olivier et al., 2014). After 2013, in
the US, CO2 emissions accounted for 82 per cent of all greenhouse gases
(GHGs) from human activities. Human activities alter the carbon cycle by
adding more CO2 emissions to the atmosphere. Moreover, they affect the
ability of natural sinks to remove CO2 (US Department of State, 2007; NRC,
2010). And within the total increase in global CO2 emissions in 2013, there
are remarkable differences among developed, emerging and developing
countries (Olivier et al., 2014).
The link between militarization, CO2 emissions, economic growth and
energy consumption is very important in this process. On the one hand,
militarization affects CO2 emissions, economic growth and energy con-
sumption; on the other hand, economic growth is supported by the
relation between energy consumption and militarization. Thus the relation-
ship between militarization, CO2 emissions, economic growth and energy
consumption is complex and interrelated.1
In this context, the main sources of CO2 emissions in G7 countries,
especially in the United States, are economic growth and militarization.
Industrial firms and facilities including hundreds of military bases over-
seas, as well as military equipment and vehicles, weapons and personnel,
emit CO2 emissions. EPA (n.d.) determined that the fossil fuel energy con-
sumption from industrial output is only about 15 per cent of total US
CO2 emissions and only 12 per cent of total US GHG emissions in 2013
(EPA, n.d.). The US army emitted more than 70 million tons of CO2 in 2014
(Neslen, 2015).
The institutions, technologies, weapons and behaviors of the military
can produce ecological problems (Jorgenson and Clark, 2011). Milita-
rization contributes to numerous forms of environmental pollution. The
tests of atomic and nuclear bombs produce radioactive fallout expand-
ing throughout the world (Commoner, 1967, 1971). The environmental
effects of militarization are not limited to war, large-scale conflicts (Jor-
genson, 2005; York, 2008) and the testing of nuclear weapons and atomic
and nuclear bombs (Jorgenson et al., 2012). Militarization causes further

1 With regard to the relation between military expenditure and economic growth,
Benoit (1973, 1978) showed that military expenditure accelerated economic
growth in less developed countries. Following this paper, many studies have
tested the relation between military expenditure and economic growth, but there
is still no consensus on the impact of military expenditure on economic growth.
Although it is not a clear relationship, there are four approaches concerning the
relation between military expenditure and economic growth: the Keynesian, the
Neoclassical, the Liberal and the Marxist approaches.
In the context of energy economics, the causal relation between energy con-
sumption and economic growth is very important in terms of economic growth,
since production without energy cannot be carried out. Kraft and Kraft (1978),
Akarca and Long (1980), Yu and Choi (1985) and Erol and Yu (1987) are pio-
neer papers. Following these pioneer papers, many papers have tested the causal
relation between energy consumption and GNP; the relationship has been inves-
tigated by different studies for different countries in different time periods again
and again.

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 Environment and Development Economics 3

environmental pollution via energy consumption. The army’s activities

and related technologies require a great deal of energy consumption during
its operations and maintenance of its infrastructure, troops and equip-
ment. In the 1980s, a quarter of jet fuel consumption and approximately
three-quarters of petroleum was consumed by the armed forces – by their
vehicles, aircraft, sea vessels, other forms of warfare machinery, and so on
(Renner, 1991; Clark et al., 2010).
While militarization and energy consumption tend to increase in a linear
manner, environmental degradation is non-monotonic. The effects of envi-
ronmental pollution are seen throughout the entire world. CO2 emissions
are relatively greater in developed countries shaped by higher military
expenditure (Rice, 2007).
In this paper, CO2 emissions caused by militarization will be explored.
Although the relationship between energy consumption, CO2 emissions
and economic growth has been analyzed by numerous papers, the rela-
tionship between militarization and environmental pollution was analyzed
in a few papers by environmental sociologists in the context of the ‘tread-
mill of destruction’ theory (Clark et al., 2010; Jorgenson et al., 2010, 2012;
Givens, 2014).
In the context of energy economics, Bildirici (2015) examined the
dynamic relationship between economic growth, energy consumption and
militarization in China for the period 1987–2013, using the autoregressive
distributed lag (ARDL) method. She determined the short- and a long-run
relationship between militarization, energy consumption and economic
growth. The results of her paper implied that economic growth and energy
consumption in China are based on militarization.
This study aims to analyze the causal relation among CO2 emissions,
energy consumption, economic growth, and militarization in the G7 coun-
tries (Canada, France, Germany, Italy, Japan, the United Kingdom and
the United States). This study is the first paper that attempts to analyze
the potential relations between these variables, using panel cointegration
and causality methods. Since the determination of a long-run relationship
between the variables is very important for G7 countries’ energy policy and
strategy, we did not wish to depend upon only one testing methodology.
The results obtained by this study are ensured by several tests. Within this
approach, we examine whether the preponderance of the evidence makes a
convincing case for robust evidence. In this paper, four steps were followed
to establish a long-run and a causal relationship between the variables.
At the first step, panel unit root tests were applied. Three unit root test
were used, namely the Levin et al. (2002) test, the Im et al. (2003) test and
Pesaran’s (2007) cross-sectionally augmented IPS test. At the second step,
three cointegration tests, the Pedroni, Johansen and panel autoregressive
distributed lag (PARDL), were applied. The Pedroni and Johansen tests
allowed us to assess whether there is a long-run equilibrium relationship,
but these tests do not provide parameter estimates either for the long run
or for the short run. At the third step, in order to determine and control the
robustness of the PARDL long-run estimators, two additional long-run esti-
mators – the dynamic ordinary least squares (DOLS) developed by Stock
and Watson (1993) and suggested by Kao (1999) and the fully modified

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 4 Melike Bildirici

ordinary least squares (FMOLS) developed by Phillips and Hansen (1990)

and suggested by Pedroni (1996) – were employed. At the fourth step, to
complement the results determined by panel cointegration methods, the
panel trivariate causality test was carried out.
The remainder of the paper is organized as follows. The literature
regarding militarization, energy consumption, CO2 emissions and eco-
nomic growth is discussed in section 2. The relation between militarization,
economic growth, CO2 emissions and energy consumption in the G7 coun-
tries is discussed in section 3. Data specifications are identified in section 4
and the econometric methodology is described in section 5. Section 6 cov-
ers the empirical results, and section 7 includes the conclusions and policy
implications.

2. Literature review
Orthodox economic theory failed to consider environmental pollution.
When energy is included in the production process, systemic disorder
increases due to entropy. The environmental pollution caused by energy
consumption is a result of energy degradation. But orthodox theory
accepted that economic growth can solve environmental problems.
As a differentiation from an neoclassical economist’s perspective,
Meadows et al. (1972) determined that economic growth has signifi-
cant environmental impacts and the future world will collapse. The
world economy will reach its physical limits in terms of fossil resources,
population, agricultural production, industrial production, environmen-
tal pollution and consumption of non-renewable natural resources
(Kaika and Zervas, 2013). Furthermore, the relation between environmen-
tal problems and economic growth was analyzed by many papers with
regard to the Environmental Kuznets Curve (EKC) hypothesis, following
Grossman and Krueger (1991). Grossman and Krueger (1991, 1995), Shafik
and Bandyopadhyay (1992), Shafik (1994), Selden and Song (1994), Tucker
(1995), Cole et al. (1997) and De Bruyn et al. (1998) analyzed the inverted
U-shaped EKC. Table 1 summarizes the literature addressing the causal
link between energy consumption, CO2 emissions and economic growth.
The present literature generally focuses only on the relationship between
energy consumption, CO2 emissions and economic growth and it accepts
that energy consumption is one of most important reasons. However, not
only economic growth, but also militarization affects environmental pollu-
tion. The majority of CO2 emissions originate from the phases of economic
growth and militarization.

3. Militarization, economic growth and energy consumption in G7

countries
Environmental pollution is closely related to the G7 countries’ eco-
nomic growth, energy consumption and militarization, and especially
the US’s militarization and energy consumption. After the World Wars,
new weapons capable of immense destruction changed warfare and
affected the pace of militarization. While the army became more distinct,
energy consumption and environmental pollution increased as a result of

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Table 1. CO2 emissions, energy consumption and GDP relation

Authors Country and time period Methodology Variables Causation

A
. ng (2007) France (1960–2000) Johansen Juselius, Energy, GDP, CO2 E→Y
VECM, ARDL
S
. oytas et al. (2007) US (1960–2004) Toda–Yamamoto Energy, GDP, CO2 E → CO2
causality, generalized
variance
decomposition
(VDC)
A
. pergis and Payne 6 central American Panel VECM, causality Energy, GDP, CO2 CO2 ↔ Y; E → CO2

Environment and Development Economics

(2009) countries (1971–2004)
Z
. hang and Cheng China (1960–2007) MWALD Energy, GDP, CO2 Y → E; E → CO2
(2009)
C
. hang (2010) China (1981–2006) Johansen cointegration, Energy, GDP, CO2 Miscellaneous
VECM
A
. caravcı and Öztürk 19 European countries ARDL bounds test, Energy, GDP, CO2 Miscellaneous
(2010) (1960–2005) VECM
L
. otfalipour et al. Iran (1967–2007) MWALD ARDL Y → C (in the long run)
(2010)
P
. ao and Tsai (2011) Brazil, Russia, India Panel cointegration CO2 emissions, energy, SR: E → CO2 ; E ↔ Y
and China GDP, FDI LR: Y → CO2 ; Y → E
(1992–2007)

(Continued.)

5
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6
Melike Bildirici
Table 1. (Continued)
Authors Country and time period Methodology Variables Causation

W
. ang et al. (2011) China (1995–2007) Pedroni, Granger CO2 , energy, GDP SR: E ↔ Y, CO2 ↔ ELR,
causality E ↔ CO2 ; Y → CO2
O
. mri (2013) 14 MENA countries Panel Granger causality E → CO2 , Y ↔ CO2
L
. im et al. (2014) Philippines (1965–2012) ARDL, cointegration GDP, oil (O), CO2 O ↔ Y, O ↔ CO2 ,
CO2 → Y
G
. ao and Zhang 14 sub–Saharan African Panel cointegration, ELK, GDP, CO2 elk ↔ Y, elk ↔ CO2
(2014) (1980–2009) panel Granger
causality
B
. ildirici and Bakirtas BRICTS (1969–2011) ARDL, FMOLS, DOLS, GDP, oil, coal, CO2 O → CO2 , coal → CO2
(2016) Granger causality in China, India,
Turkey and South
Africa; and O ↔ CO2 ,
coal ↔ CO2 in Brazil
and Russia
B
. ildirici (2017) MENAP (1980–2012) ARDL, ANOVA, GDP, O, oil production, Y → CO2 for Bahrain,
causality CO2 Egypt, Morocco,
Oman, Syria and
Tunisia; CO2 → Y for
Pakistan and Yemen;
and Y ↔ CO2 for
Jordan, Kuwait,
Lebanon and Qatar

Notes: Y denotes GDP.

 Environment and Development Economics 7

high-tech equipment and new weapons, especially the nuclear weapons

developed by scientists.
As military supremacy has been very important since the second half
of the 20th century, scientists and engineers turned their research focus
toward addressing the technological needs of the military. In addition to
this, the expansionary dynamics of militarism produced specific forms of
environmental degradation. Nowadays militarization continues under the
impact of technological advances, infrastructural development, geopoliti-
cal competition and growth in troop size. Military powers cause environ-
mental degradation by their military equipment, tests of nuclear bombs,
military bases, weapons production and war (Hooks and Smith, 2004, 2005;
Jorgenson et al., 2010; Clark and Jorgenson , 2012).
Warfare in the last half of the 20th century and first half of the 21st cen-
tury differs with regard to its use of technological weapons, the lethality of
weaponry and the scope of environmental devastation (Hooks and Smith,
2005). The Center for Disarmament estimated that global military oper-
ations utilized aluminum, copper, nickel and platinum which contribute
to environmental pollution (Renner, 1991). During the six weeks of the
1991 Persian Gulf air war, more weapons were used than during the pro-
tracted Vietnam War (Levy et al., 2000). According to US Army estimates,
during the first three weeks of war in Iraq in 2003, 40 million gallons of
fuel were consumed (Sanders, 2009). Moreover, the Pentagon, for example,
accounted for 93 per cent of the fuel consumption of the US government
in 2007 (Air Force: 52 per cent; Navy: 33 per cent; Army: 7 per cent; other
DoD: 1 per cent) (Lengyel, 2007).
The defense industry and department of the G7 countries consumes
large amounts of energy in planes, ships and tanks. Even in peacetime, mil-
itary institutions and their activities consume vast amounts of fossil energy
for research and development, maintenance and operation of the over-
all infrastructure. Capital-intensive militaries in the G7 countries employ
equipment, personnel and advanced weaponry that require an enormous
amount of fossil fuel energy to facilitate the rapid movement of troops. Fur-
thermore, in the G7 countries, the Department of Defense consumes more
energy than any other organization in the world. The energy consumption
of the military causes environmental pollution via the effects of various
toxins and chemicals involved in the use of dangerous weapons.
The above-mentioned effects of militarization are further magnified by
the testing of nuclear weapons and atomic and nuclear bombs. Therefore,
even without the existence of any war, the defense sector consumes huge
amounts of fossil fuel energy.

4. Data specifications
The annual data used in this study cover the period from 1985 to 2015 for
Canada, France, Germany, Italy, Japan, the United Kingdom and the United
States. The energy consumption (c), defense expenditure (m) as a measure
of militarization, CO2 emissions as a measure of environmental pollution

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 8 Melike Bildirici

(co), and real per capita GDP (y) are used as variables. In order to minimize
skewness, all variables were measured in natural logarithms.
The data on energy consumption were taken from the World Bank and
BP statistics. Defense expenditure was measured in constant 2005 US dol-
lars and obtained from the World Bank. Defense expenditure data comprise
all current capital expenditures. Real per capita GDP data were obtained
from the World Bank and the International Financial Statistics of the IMF.
CO2 emissions data were taken from World Bank statistics which repre-
sent the total CO2 emissions from fossil fuels in 1,000 metric tons for the
1985–2015 period.

5. Econometric methodology
As the first stage, three panel unit root tests – the Levin et al. (2002) test,
hereafter referred to as the LLC test; the Im et al. (2003) test, hereafter
referred to as the IPS test; and Pesaran’s (2007) cross-sectionally augmented
IPS test, hereafter referred to as the CIPS – are used in this study. At the sec-
ond stage, three panel cointegration tests – the Pedroni, Johansen and panel
ARDL tests – are used. At the third stage, the FMOLS and DOLS meth-
ods were applied. Pedroni’s (1999) methodology allows us to test for the
presence of cointegration, but it does not provide an estimation of a long-
run relationship. Pedroni (1996) suggests a FMOLS estimator to obtain the
panel data estimates. At the fourth stage, panel causality tests were applied
to determine the causal relation. The panel trivariate Granger causality test
was preferred.

5.1. Panel unit root tests

The LLC test, the IPS test and the CIPS tests were used. While the LLC
test depends on pooled data, the IPS test is obtained as an average of aug-
mented Dickey–Fuller (ADF) test statistics. The LLC panel unit root test is
based on the following equation:

yit = ρi yi,t−1 + z it γ + u it , i = 1, . . . , N ; t = 1, . . . , T,

where z it can be a fixed effect or time trend as well as a constant like

zero and 1. The LLC test assumes that residuals are independently and
identically distributed with mean zero and variance σu2 and ρi = ρ for all
values of i. The null hypothesis can be constructed as H0 : ρ = 1 and the
alternative, H1 : ρ < 1, means that all series are stationary. The t-statistic is:
 
N T
(ρ̂ − 1) i=1
2
t=1 ỹi,t−1
tp = 1  N T 2
NT i=1 t=1 ũ it

and
1  2
N T
se2 = ũ it .
NT
i=1 t=1

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 Environment and Development Economics 9

The IPS unit root test can be specified as:


pi
yit = ρi yi,t−1 + ϕi j yi,t− j z it γ + εit .
j=1

The null hypothesis states that all series in the panel have unit root H0 :
ρi = 1 and, alternatively, part of the series is stationary: H1 : ρi < 1. The
IPS t-statistic is
√
N (t¯ − N1 E[ti T |ρi = 1])
tIPS =  ⇒ N (0, 1).
1
N var [ti T |ρi = 1]

Pesaran’s (2007) CIPS test can be given as follows:


k 
k
yit = αi + ρi yi,t−1 + ci ȳt−1 + di j  ȳt− j + βi j yi,t− j + εit .
j=0 j=1

The CIPS statistic, based on the average of individual covariate augmented

Dickey–Fuller CADF statistics proposed in Hansen (1995), is:

1 
N
CIPS = ti (N , T ),
N
i=1

where ti (N , T ) is the t-statistic of the estimate of pi .

5.2. Panel cointegration tests: Pedroni, Johansen and PARDL

5.2.1. Pedroni test
The Pedroni (1999) test is performed as follows:

yit = λ0 + χi t + ∂1i xit + εit ,

where i = 1, . . . , N for each country in the panel and t = 1, . . . , T refers to

the time period.
The parameters allow for the possibility of country-specific fixed effects
and deterministic trends. To test the null hypothesis, ρi = 1, the unit root
test is
εit = ρi εit−1 + u it .

Pedroni (1999, 2001) has proposed seven tests. The first four tests are
known as the within-dimension and the last three as the between-
dimension.

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 10 Melike Bildirici

5.2.2. Panel Johansen test

For a panel data set with N cross-sections and T time periods, a panel
vector autoregression (VAR) model is written as (Bildirici et al., 2011):


ki
X it = ik X i,t−k + εit ,
k=1

where i = 1, 2, . . ., N cross-section units and t = 1, 2, . . . , T time periods

and εit ∼ N P (0, i ). The error representation of the VAR is


ki−1
X it = i X i,t−1 + ik X i,t−k + εit ,
k=1

where i is of order pxp. If i has reduced rank, then extending the

assumption i for panels, we obtain, i = αi βi where αi are the adjust-
ment parameters and β  are the long-run parameters, respectively. αi and
β  are of order pxri and full column rank. The cointegrating rank hypoth-
esis is stated as H (r ) : rank( ) ≤ r and tested against the alternative H (r ) :
rank( ) = r . Also,


p
−2I n Q T {H (r ) | H ( p)} = −T I n(1 + λ̂i ),
i=r +1

where the λ̂i trace statistic is the ith eigenvalue to a certain eigenvalue
problem (Larsson et al., 2001; Larsson and Lyhagen, 2007; Bildirici et al.,
2011).

5.2.3. Panel ARDL test

The panel ARDL model for the standard log-linear functional specification
of the long-run relationship between variables is


p 
q−1
Yit = φi + γi j Yi,t− j + μi j X i,t− j + δ1i Yi,t−1 + δ2i X it + εit ,
k=1 k=0

where
⎛ ⎞

q 
q
δ1i = − ⎝1 − λij ⎠ δ2i = ωi j
j=1 j=0


p 
q
γi j = − λim , μi j = − ωim ,
m= j+1 m= j+1

where i = 1, . . ., N are cross-section units, t = 1, . . . , T are time periods, φi

is the group-specific intercept and ωi j and λi j are kx1 vectors for explana-
tory variables. The null hypothesis of no cointegration among the variables

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 Environment and Development Economics 11

is H0 : δ1i = δ2i = 0 and the alternative hypothesis is H1 : δ1i = δ2i = 0.

The null hypothesis of no cointegration is H0 : ωi j = 0 and the alternative
hypothesis is H1 : ωi j = 0.
The long-run slope coefficients for the specific countries (Offermanns,
2006) is
Yit = − (δ2i /δ1i ) X it − φi /δ1i = ηi X it + u i .
i ≡ ηγi was assumed and tested with the Hausman (1978) test:

h = T q̂  v̂(q̂)−1 q̂,

where q̂ = η̂u − η̂r is the difference between the unrestricted mean group
(MG) and the restricted pooled mean group (PMG) estimators proposed
by Pesaran and Smith (1995) and Pesaran et al. (1999) (Pesaran, 2004;
Offermanns, 2006). The error-correction model (ECM) is reparametrized as
follows:


p 
q−1
Yit = αi + γi j Yi,t− j + μi j X i,t− j + φi (yi,t−1 − ϕi xt ) + ϑit + εit
k=1 k=0
⎛ ⎞

p 
q 
ϕi = −(1 − γik ), θi = μik / ⎝1 − γi j ⎠ ,
k=1 k=0 j


p
γik = − γim k = 1, 2, . . . p − 1
m=k+1


q
and μik = − μim k = 1, 2, . . . q − 1.
m=k+1

where φi is a parameter indicating the speed of adjustment to the equilib-

rium level following a shock (Bildirici, 2014).

5.3. Long-run estimators

The panel Johansen and Pedroni tests did not give long-run coefficients.
Since it was preferable not to be contingent upon only one testing method-
ology, the FMOLS and DOLS estimators are applied to long-run coeffi-
cients.

5.3.1. FMOLS estimators

The model is
Yit = αi + βi X it + μit ,
and these are cointegrated with slopes βi . If εit = û it , X it , then it is
⎡  T  T  ⎤
 
it = lim E ⎣T −1 εi T εi T ⎦ and i = i0 + i + i ,
T →∞
t=1 t=1

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 12 Melike Bildirici

where i0 is the contemporaneous covariance and i is a weighted sum of

auto-covariances. The FMOLS estimators are given as:

 T −1  

N  
T
−1
φ=N (xit − ȳi ) 2
x (xit − x̄i )2 sit∗ − T λi ,
i=1 i=1 i=1

where

ˆ 21it

yit∗ = (sit − s̄it ) − xit ∗
ˆ 22it

ˆ 21it

λ̂it = ˆ 21it + 
ˆ
2̇1it − (ˆ 22it + 
ˆ
2̇2it ).
ˆ 22it

N
The between-dimension estimator is constructed as φĠ F M = N −1 i=1 φκ M,t ,
where φκ M,t is the FMOLS estimator. And the associated t-statistic is
given as

N
tĠ F M = N −1/2 tφκ̇ M,t ,
t=1

where
 1/2

T
t φ̂κ̇ M,t = (φ̂κ̇ M,i ˆ −1
− φ0 )  (xit − x̄it )2 .
11t
t=1

5.3.2. DOLS estimators qi

The DOLS model is given as yit = αi + βi xit + j=−qi λiq xit−q + u it∗ and

 N T 
−1
the group-mean panel DOLS estimator is φ̂ B ∗ S = N −1 i=1 t=1 n it n it
 
T
n s̃
t=1 it it , where n it is the 2(K + 1) × 1 vector of regressors n it =
1
(xit − x̂it , xit−K , . . . , xit+K ). ỹit = yit − ȳit . The associated t-statistic for
the between-dimension estimator is given as

 1/2

T
tφDj∗ = (φ D,i ∗ − φ0 ) σ̂t−2 (xit − x̄it )2
.
t=1

5.4. Panel trivariate Granger causality test

Bivariate Granger causality models received many criticisms for being
incomplete models that omitted important variables. This is because, when
a third variable was included in the causality test, the bivariate models pro-
duce unreliable results (Caporale and Pittis, 1997; Odhiambo, 2008). Some
papers on the relation between economic growth, energy consumption and

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 Environment and Development Economics 13

CO2 emissions used trivariate Granger causality models (see Odhiambo,

2009; Acaravcı and Öztürk, 2010; Gao and Zhang, 2014).
The trivariate Granger causality model between variables based on the
ECM was constructed as follows:


m 
m
yit = λ1 j + αik yit−k + ϑik X it−k + ζ0 EC Mt−1 + ε1t (1)
k=1 k=1


m 
m
xit = λ0 + α2ik xit−k + ϑ2ik yit−k + ζ1 EC Mt−1 + ε2t , (2)
k=1 k=1

where residuals εt are independently and normally distributed (i.i.d) with

zero mean and constant variance, and ζ is a parameter indicating the speed
of adjustment to the equilibrium level after a shock. Granger causality can
be examined in short-run Granger causalities by H0 : ϑik = 0 and H1 : ϑik =
0 in equation (1), and H0 : ϑ2ik = 0 and H1 : ϑ2ik = 0 in equation (2) for all
i. With regard to the long –run causality in equations (1) and (2), long-
run Granger causalities are tested from the error-correction terms (ECTs)
in those equations by H0 : ζ0 = 0, H0 : ζ1 = 0 for equations (1) and (2). And
lastly, strong causality was tested. The strong causalities are tested by H0 :
ϑik = ζ0 = 0 and H0 : ϑ2ik = ζ1 = 0 in equations (1) and (2).

6. Empirical results
In this paper, the econometric application was applied in four steps.

(i) Three panel unit root tests – LLC, IPS and CIPS – were used.
(ii) Cointegration was tested with the use of panel Johansen, Pedroni
and PARDL tests.
(iii) The long-run coefficients were determined by PMG, FMOLS and
DOLS methods.
(iv) Lastly, the causal relationship was determined with the panel
trivariate Granger causality test.

6.1. The results of panel unit root tests

The results determined by panel unit root tests are reported in table 2. The
parameters in the panel data variables are stationary in the first difference.

Table 2. Panel unit root test

Level First difference

LLC IPS CIPS LLC IPS CIPS

y −1.012 −1.785 13.85 y −9.872 −9.442 153.81

ml −0.825 −0.605 10.91 ml −7.034 −7.896 98.58
c 1.98 1.005 11.49 c −5.601 −8.848 98.814
co −1.01 −0.998 10.556 co −5.965 −6.869 95.369

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 14 Melike Bildirici

6.2. Panel cointegration results

The Johansen cointegration results are given in table 3. While the H0
hypothesis was rejected for all countries, the H1 hypothesis was accepted.
The results of the Pedroni test are shown in table 4. Based on the results,
the null hypothesis is rejected.
The Pedroni and Johansen tests accepted the existence of one cointegrat-
ing vector but they did not determine the coefficients of parameters.
In this process, the PMG estimation and MG estimation are shown in
table 5.
The PMG and MG results indicated the existence of a strong relationship
between CO2 emissions and militarization, between CO2 emissions and
GDP, and between CO2 emissions and energy consumption. In the long
run, the militarization elasticity of CO2 emissions was found to be 0.881

Table 3. Johansen cointegration results

Cointegration rank test results
r = 0 40.52; r ≤ 1 13.78; r ≤ 2 2.639

Individual cross-section results

r =0 r ≤1 r ≤2

Canada 48.9909 13.0534 2.6756

France 49.5711 7.0416 0.4674
Germany 51.1817 11.6840 2.9236
Italy 46.1063 7.0765 1.7340
Japan 47.5177 9.7684 1.1677
United Kingdom 42.9058 6.8614 0.0861
United States 50.4677 7.0100 0.0018

Table 4. Pedroni cointegration test results

Alternative hypothesis: common AR coefficients (within-dimension)
Panel v-stat = 4.3496; panel rho-stat = −4.175; panel pp-stat = −8.847; panel
adf-stat = −4.367
Alternative hypothesis: individual AR coefficients (between-dimension)
Group rho-stat = −4.090; group pp-stat = −8.885; group adf-stat = −4.792

Table 5. PMG and MG tests

Long-run coefficients Short-run coefficients and ECM

PMG MG PMG MG

Y 0.9369 (0.001) 0.91756 (0.002) 0.8923 (0.03) 0.7266 (0.001)

ml 0.8813 (0.04) 0.7291 (0.01) 0.6938 (0.05) 0.7096 (0.001)
c 0.5513 (0.02) 0.5190 (0.01) 0.4946 (0.02) 0.4743 (0.03)
ecm – – −0.66 (0.001) −0.737 (0.02)

Notes: The values in parentheses show P > |z|.

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 Environment and Development Economics 15

in PMG and 0.729 in MG. The income elasticity of CO2 emissions is deter-
mined as 0.936 (PMG) and 0.917 (MG) for the long run and as 0.892 (PMG)
and 0.73 (MG) for the short run.
In comparing the PMG and MG estimators, it was determined that
the estimated long-run elasticities are statistically significant as expected.
However, the PMG estimate of the militarization, energy consumption and
income elasticity is larger in magnitude than the coefficients estimated by
the MG model.
The ECM coefficient indicated a mechanism to correct the disequilib-
rium between variables. The ECM coefficients are estimated as −0.66 and
−0.737. They showed a rapid speed of adjustment of any disequilibrium
towards long-run equilibrium.

6.3. FMOLS and DOLS results

Table 6 showed FMOLS and DOLS results. The coefficients determined
from models are statistically significant at the 1 per cent significance level.
The FMOLS and DOLS results exhibited similar results in magnitude and
sign with each other and panel ARDL. The FMOLS results indicated that:
a 1 per cent increase in m increases CO2 emissions by 0.913 per cent; a
1 per cent increase in y increases CO2 emissions by 0.923 per cent; and
a 1 per cent increase in energy consumption increases CO2 emissions by
0.51 per cent. In the DOLS results, a 1 per cent increase in militarization
increases CO2 emissions by 0.91 per cent, and CO2 emissions increase by
0.92 per cent if there is a 1 per cent increase in GDP.
The results obtained from panel ARDL, FMOLS and DOLS methods
determined that the militarization, GDP and energy consumption have
a significant positive impact on CO2 emissions. The results show that
an increase in militarization, economic growth and energy consumption
increase environmental pollution. Similarly to our findings, Givens (2014)
found that militarization has a significant effect on environmental degra-
dation, and Bildirici (2015) determined a statistically significant causal
relation between defense expenditure and energy consumption.

6.4. Causality results

When the ECT is statistically significant, a change in one variable is
expected to influence the other variable through a feedback system, signing
a causal link among variables: co, y, ml and c (table 7).
In the long run, the coefficients of ECTs in all equations are signif-
icant. The result of the significance of the interactive term of energy
consumption–ECT, militarization–ECT and economic growth–ECT in the
emissions equation is in line with the causality which runs from energy

Table 6. FMOLS and DOLS results

FMOLS results DOLS results

0.913 m (6.001) 0.923 y (1.89) 0.907 m (5.99) 0.919 y (2.01)

0.509 c (4.97) 0.51 c (5.01)

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16
Table 7. Panel trivariate Granger causality test

Melike Bildirici
Source of causation (independent variables)

Short-run Long-run Strong causality results

Dependent variable y m c co ECT y and ECT m and ECT c and ECT co and ECT

y – 13.15 10.736 0.68 12.28 – 16.16 20.53 0.46

m 14.89 – 8.065 0.83 18.76 19.56 – 12.89 1.85
c 17.07 14.35 – 0.35 8.35 21.05 18.91 – 1.11
co 9.33 12.56 9.8 – 9.56 22.96 19.98 26.85 –

Causality direction

Short-run causality results

ml → y c → y c → ml y → co ml → co c → co

y → ml y → c ml → c

↔ ↔ ↔ → → →

Strong causality results

ml and c and c and

ECT → yy and ECT → yy and ECT → mlml and y and ml and c and
ECT → ml ECT → c ECT → c ECT → co ECT → co ECT → co

↔ ↔ ↔ → → →
 Environment and Development Economics 17

consumption, economic growth and militarization to CO2 emissions. This

finding reveals that whenever there is a shock to the system, energy con-
sumption, militarization and economic growth would make a short]run
adjustment to restore long]run equilibrium. Evidence was found of bidi-
rectional causality between militarization and economic growth, between
energy consumption and economic growth, between energy consumption
and militarization, and between CO2 emissions and economic growth.
Furthermore, there is unidirectional causality from militarization to CO2
emissions, from economic growth to CO2 emissions and from energy con-
sumption to CO2 emissions. This supports the coefficients determined by
the FMOLS, DOLS and panel ARDL models.
According to the results, the two-way causal relation between economic
growth and militarization postulates that military expenditure plays a
crucial role in economic growth and the reverse. Since an increase in milita-
rization has a positive impact on economic growth, the policies that reduce
militarization can negatively affect the economic growth. This result and
results of a causal link between militarization and energy consumption,
and between militarization and economic growth are similar to the findings
of Bildirici (2015).
Bidirectional causality between energy consumption and economic
growth supports the feedback hypothesis. The results of causality between
energy consumption and CO2 emissions are consistent with the findings of
Soytas et al. (2007) for the US, Zhang and Cheng (2009) for China, Pao and
Tsai (2011) for BRIC countries, Omri (2013) for 14 MENA countries, and
Bildirici and Bakirtas (2016) for BRICTS countries.
According to our results, militarization, economic growth and energy
consumption have a positive significant impact on CO2 emissions per
capita for all the countries. This indicates that an increase in militarization,
economic growth and energy consumption raises the carbon emissions in
G7 countries.

7. Conclusions and policy implications

In this paper, the relation between CO2 emissions, economic growth,
energy consumption and militarization as the most ecologically destructive
human production was analyzed by panel cointegration methods (Pedroni,
Johansen, PARDL) and panel trivariate causality methods. The relationship
between CO2 emissions, militarization, economic growth and energy con-
sumption was positive and significant and there was a short-run as well as
a long-run relationship.
The two-way causal relation between economic growth and mil-
itarization and between energy consumption and economic growth
postulates that militarization and energy consumption play a cru-
cial role in economic growth. The two-way causal relation between
militarization and energy consumption, and between per capita GDP
and energy consumption postulates that the energy conservation policies
that reduce energy consumption negatively affect militarization and eco-
nomic growth. The evidence of bidirectional causality between energy

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 18 Melike Bildirici

consumption and militarization determined that militarization increases

energy consumption. Militarization and economic growth showed impacts
on energy consumption. Unidirectional causality was found from milita-
rization to CO2 emissions, from economic growth to CO2 emissions and
from energy consumption to CO2 emissions. Militarization and energy
consumption have an impact on CO2 emissions.
Our results determined that military establishments have very impor-
tant impacts on energy consumption and that energy consumption and
militarization play a crucial role in economic growth as crucial factors in
improving the quality of life.
Causality results show a one-way causal link from militarization to
CO2 emissions and from energy consumption to CO2 emissions, all with-
out feedback. If G7 countries would like to reduce their emissions levels,
they have to reduce not only energy consumption per unit of output but
they also can sacrifice their militarization. A most important alternative
option is to increase the use of renewable energy resources. Fighting against
environmental pollution should be the most important policy.

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