Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
A Bivariate Causality between Energy Consumption and
Economic Growth in Ghana
Mustafa Özer and Charles Mensah
As every nation continues to aspire for continues economic growth, the issue about
energy and its contribution to economic growth has become an economic topic for
discussion in most energy research papers. Understanding the factors that affect
economic growth (GDP growth) is very crucial in policy making since its helps in
macroeconomic stability and livelihood improvement in the economy. This study looks
at the causality relationship between total aggregate energy consumed in Ghana and
GDP growth (economic growth). The data used was from the World Bank Indicators
with data spanning from 1971 to2013. Using the VAR Granger causality and lag
order 1, it was established that GDP growth granger causes energy consumption in
Ghana. The individual energy components and its relationship to GDP growth were
examined in this study. Electricity was found to have a long run causality to economic
growth using the VECM granger causality/Block erogeneityWaldtest at lag 1. A shortrun VAR granger causality was found from fossil consumption to GDP growth and a
bi-directional causality was found between Biomass consumption and economic
growth in Ghana.
JEL Code: E32, E21
Keywords: GDP, Energy, VAR, VECM, Fossil and Biomass.
Introduction
As every nation continues to aspire for continues economic growth, the issue about
energy and its contribution to economic growth has become an issue for discussion.
Understanding the factors that affect economic growth (GDP growth) is very crucial
in policy making since its helps in macroeconomic stability and livelihood
improvement in the economy.
Ucan et al. ( 2014) explain that in both developing and developed countries, energy
is an important porduction factor like capital and labour. The oil prices shock that hit
most of the world economies in the 1970s,1990s and 2000s are clear indications of
how energy consumption affects economies.
This study uses a bivariate framework to study the causative relationship between
aggregate energy consumption and GDP growth using the Vector Autoregression
Regression (VAR) system approah. The study also looks at the individual
components that makeup energy in Ghana and their individual causality to GDP
growth in Ghana. In this way, the study tends to fill in the gap in literature where
most study have been done on the energy consummed in various sectors and their
effect on GDP.
The Energy Commission of Ghana defines the composite of energy as Crude Oil,
Hydro power, Natural gas and wood. The five major final consumers of energy in
Ghana are the Residential Sector, Industrial Sector, Transport Sector, Agriculture
and Fisheries Sector and the Commerce and Service sector. In 2013 for instance,
__________________________________________________________________
Prof.Dr. Mustafa Özer, Anadolu University, the Faculty of Economics and Administrative Sciences,
Department of Economics, Yunus Emre Kampusu 26470, Eskişehir, Turkey.
Charles Mensah, Graduate School of Social Sciences, Anadolu Üniversitesi. Yunus Emre Kampusu
26470, Eskişehir-Turkey
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
the Residence sector consumed the highest energy of about 2795.7, followed by
Transport with 2644.8 and Industry was 1050. The Agriculture and Commerce sector
consumed the least energy in 2013 with 102.8 and 292.9 respectively.
Energy Consumed (ktoe unit)
2644.8
2795.7
Residence
Industry
Commerce and Service
Agric and Fisheries
1050
Transport
102.8
292.9
Source: Energy Commission Report 2013
1. Energy Consumption in Ghana over the period of 2000 and 2013
Ghana’s energy trend has hovered around 5000ktoe over the period of 13years. Petroleum has
been the most heavily consumed component of energyin Ghana. Petroleum consumption
steadily rose from 1535.3 ktoe in 2000 to 3300.1 ktoe in 2013. Fossil energy (natural gas and
oil) consumptions have been used in the transport sector, industry and most agriculture and
fishing equipments.The Biomass which is mostly wood is the second consumed energy type
in Ghana. This type of energy is burnt as charcoal and used for cooking in both the rural and
urban areas. In the early 2000s the use of biomass was twice the use of petroleum because of
its wide coverage and affordable price. Electricity though does not cover the whole region of
Ghana plays an important role in the economy. Since 2011-2015, Ghana has been facing
serious issues of power shortage that has badly affected business and homes in Ghana. It has
gradually risen from 596.8ktoe in 2000 to about 910ktoe due to increase in population and
industrialization of the economy. Below is a chart to display the trend in how the various
energies were consumed from 2000 to 2013.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Source: Energy Commission Ghana Report 2014
2. Empirical Literature
Theoreticalliteratures from Solow Model of growth to recent models that have energy as part
of the factors for economic growth over the years have been developed. However, this study
focuses mainly on the recent empirical literature on the issue of energy and economic growth.
There have been various views that have been empirically backed on the direction causation
between energy consumption and growth. Studies have been done considering a single
country and cross country panel framework and results indicated diverse views on the
causation.
2.1.Single Country Time Series Study
Altinay et al.(2005) examined the causal relationship between energy consumption and
economic growth in Turkey using electricity as a proxy for energy from 1950 to 2000. The
study employed the Granger causality approach and found a unidirectional causality that runs
from electricity consumption to income or GDP growth. This means that energy consumption
is contributing effective to economic growth in Turkey.
Akan et al.(2010)also looked at the relationship between energy consumption and GDP in
Turkey with data from 1970 to 2007 and used the Error Correction Model. Their study
yielded a bidirectional relationship between energy consumption and economic growth.
Abaidoo(2011) also did a similar study focusing on Ghana and looking at the magnitude and
causal relationship between energy consumption and economic growth. The study also
adopted electricity as the energy proxy using Sims (1972) Granger Test and found causality
running from economic growth to energy. Adom(2011) also used Toda and Yamamoto
Granger causality test with data from 1971 to 2008 and found found granger causality
running from economic growth to electricy consumption in Ghana.
Kraft and Kraft (1978) found from their study about USA that causality runs from GDP to
Energy consumption. They used the Sims technique as their research methodology and the
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
data used spanned from 1947 to 1974. This means that gowth in GDP in America will
significantly contribute to energy use.
Stern(1993) also used Multivariate VAR model in USA and found the same rsult that
supports (Kraft & Kraft, 1978) that, causality runs from GDP to energy consumption. Stern
also replaced gross energy with an index of final energy use wieghted for the changing fuel
composition of energy input and found causality from energy consumption to GDP. This
study used data that spanned from 1947 to 1990.
Oh and Lee (2004) undertook a similar study in Korea using the Granger causality empirical
method over the period from 1981 to 2000. The conclusion from this study was that, there
existed a long run unidirectional causal relationship from GDP to energy.
Paul and Bhattacharya (2004) examined the causality between energy to GDP in Indıa using
data spanning from 1950 to 1996 and used Granger causality method. They found a
bidirectional relationship between GDP and energy consumption.
In China, energy was found to granger cause GDP growth when the researcher used Granger
causality method with data spanning from 1952 to1999 (Wolde and Rufael,2004)
2.2.Cross Country evidence
Lee and Chang (2008) used Granger causality method and found energy granger causing
economic growth. Their study comprised on 16 Asian countries with data from 1971 to 2002.
This means that if god energy policies are put in place in Asia, GDP is expected to grow and
the state of the economy is expected to improve.
Soytaş and Sar (2003) considered the same causality relation between energy consumption
and economic growth using G-7 nations and found growth granger causes energy
consumption. The study used data that spanned from 1950 to 1992 concluded that, when
nations improve on GDP growth, it affects their energy consumption and therefore the need
to know which type of energy provide less harm to the environment and can be generated
with less resources.
Chen et al.(2007) considered 10 Asian countries using Panel cointegration, Error Correction
Model and Panel Causality tests with data from 1971 to 2001. They found a bidirectional
causality between electric consumption and GDP.
Mehrara (2007) in his study decided to consider 11 oil exporting countries to know what kind
of relationship that exist between GDP and energy consumption. Interestingly, the result of
the study was that, there was causality from GDP to energy consumption.
Huang et al. (2008) took their research on a larger scale for 82 developed and developing
countries and had some interesting conclusions. The study concluded that, GDP and energy
consumption had no directional causality in the low income countries. This meant that energy
consumption and GDP may be granger caused by other economic factors. The study however
revealed that, for middle income and high income countries, GDP granger caused energy
consumption. This study used the Panel VAR and GMM approach with data from 1972 to
1992.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Apergis et al. (2010) used Panel Error Correction Model for 19 developed and developing
countries from 1984 to 2007 and found that there is bidirectional causality between
renewable energy consumption and economic growth. This means that, as these countries
improve upon their GDP, renewable energy consumption will also be improved with
efficiency.
3. Data and Methodology
In this section, the study reports a step by step approach in achieving causality among energy
consumption and GDP growth, and the various components of energy consumption in Ghana
and their causality to economic growth. The results are divided into two groups; the bivariate
causality between Energy consumption and GDP growth, and the bivariate causality between
the individual energy components (petroleum, biomass and electricity from Hydro) and GDP
growth.
3.1.Data
All data used in this study were collected from the World Bank data source. GDP growth data
and energy use for Ghana are directly reported as secondary data in the World Bank database.
Also, total electricity consumed, fossil consumed and Biomass consumed can all be found
from the same data base. The data spans from 1971 to 2013.
3.2.Methodology
In this study, we employed a research procedure that consist of three steps. In the first
step, we examine the time series properties of the variables by using unit root tests of the
Augumented Dickey Fuller and the Phillip Perron and Johansen Cointegration Tests. In the
second step, we form A VAR and VECM model based on the results of Johansen
cointegration tests. Finally, we use tools of VAR/VECM to investigate the dynamic linkages
between energy consumption, components of energy and economic growth.
4. Empirical Results
The results reported for this study are in two parts; the causal relation between Energy
Consumption and Growth, and the causal relation between the energy components
(Fossil,Biomass and Hydro-Electricity) and GDP growth.
4.1.The Empirical Results of the causal relations between Energy Consumption and
Growth
The first empirical work was check for the presence of unit root in the variables. To achieve
this, the Augmented Dickey Fuller Test and the Philip Perron Test of stationarity. The table
below shows that the variables were stationary after the first differencing using the Philip
Perron Test and the ADF test. Table 1 results is a combination of all unit root tests
undertaken.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Table 1 Unit Root Tests
Philip Perron Test for Stationarity
Variable
PerronTest statistic
P value
Results
GDPG
-7.13
0.000*
I(1)
E
-6.38
0.000*
I(1)
ADF TEST FOR STATIONARITY
Variable
ADF Test statistic
P value
Results
GDPG
-6.17
0.000*
I(1)
E
-6.38
0.000*
I(1)
Number of observations = 42
*significance at 1% , Lag(1)
From both the Philip Perron and the ADF test shows that the log of GDP growth and Energy
consumption becomes stationary after the first difference. Since it now stationary, it can be
used for any meaningful econometric analysis.
Having verified that Energy consumption and GDP growth were stationary after the first
difference, it was essential to find how many cointegrating equations existed in the systems
(Johansen 1988). Trace statistic and the Maximum Eigen value Tests were used to find out if
there was a cointegrating equation in the model. Table 2 includes the results of Johansen
cointegration tests 1 . The Johansen trace statistic result indicated the was no cointegration
among the variables. The Maximum Eigen value cointegration test it also confirmed that
there was no cointegration among GDP growth and Energy.
5.1.1. Johansen Cointegration Test
Johansen Cointegration test is a very important and essential test after identifying the
integrating order of the variables. Having verified that Engergy consumption and GDP
growth were stationary after the first difference, it was essential to find how many
cointegrating equations existed in the systems (Johansen 1988). Trace statistic and the
Maximum Eigen value Tests were used to find out if there was a cointegrating equation in the
model. The Johansen trace statistic result indicated the was no cointegration among the
1
The selection for the best appropriate lag is an integral part in the cointegrationprocess.To accomplish this, the
sequential modified LR test statistics (each at 5%), Final Prediction Error (FPE), the Schwarz Information
Criterion (SC), the Hannan- Quinn (HQ) and the Akaike Information Criterion (AIC) were used. The table
below shows that from these selection criteria, the most appropriate lag to use was Lag 1. A residual test was
done to check for autocorrelation at lag 3 using the VAR Residual LM test and the null hypothesis of no serial
correlation was not rejected.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
variables. The Maximum Eigen value cointegration test it also confirmed that there was no
cointegration among GDP growth and Energy. Below is a table of the results of cointegration
tests.
Table 2: Cointegration Tests
Trace Test
Hypotheses number Trace Stats
0.05 critical value
Prob
of CE(s)
None*
13.307
15.495
0.104
At most 1
0.766
3.841
0.381
Max. Eigen value
Prob val
Hypothised number Eigen Value
of CE(s)
None*
0.361
12.541
0.092
At most 1
0.027
0.766
0.381
The probability value of no cointegrating equation is more the 0.05, we can conclude that ther
is no cointegrating equations.
Since the results of Johansen Cointegration tests indicate no cointegration between variables,
we estimate a VAR model in first differences of the variables with one lag, and then carry out
Granger causality test, compute IRFs and VDCs.
5.1.2. VAR Granger Causality/ Wald Exogeneity Test
This test is conducted to know the directional causality of the variables in the VAR system.
Table 3 below indicates that, there is a unidirectional causality that runs from GDP growth to
Energy consumption in Ghana.
Table 3: VAR Granger Causality
Dependent Variable: LGDP
Excluded
Chi-sq
df
Prob
Direction
LEnergy
0.94
1
0.3315
Energy --- GDP
All
0.94
1
0.3315
Dependent Variable:
LEnergy
Excluded
Chi-sq
df
Prob
Direction
LGDP
9.63
1
0.0019
GDP
All
9.63
1
0.0019
*----- means no directional causality.
Means a unidirection running from GDP growth to Energy use
Energy
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
From the VAR granger causality test, we can observe that Energy consumption in Ghana
does not granger cause GDP growth. This means that, the values of energy use can not
predict GDP growth in Ghana. However, it was also discovered that, GDP growth granger
causes Energy consumption in Ghana. This means that, as the economy of Ghana grows, we
can predict the amount of energy to be consumed.
Since Granger causality test only indicates direction of causality, to indicate the sign of the
causality, we use Impulse Response Function (IRF) between two variables. A one standard
deviation applied to GDP growth positively affects its self in a decreasing manner. Also, an
innovation applied to energy use does not have a visible dynamic linkage with GDP growth
in Ghana.Based on the IRFs below, we can conclude that GDP growth increases the energy
consumption. In other words, GDP growth has a positive dynamic linkage with energy use or
consumption. From this, we can conclude that GDP growth granger causes Energy use
positively in Ghana. A shock applied to energy use gives a positive dynamic linkage in
response to itself.
Impulse Response Analysis
Response to Cholesky One S.D. Innovations ± 2 S.E.
Response of LGDP to LGDP
Response of LGDP to LENERGY
.5
.5
.4
.4
.3
.3
.2
.2
.1
.1
.0
.0
-.1
-.1
1
2
3
4
5
6
7
8
9
10
1
Response of LENERGY to LGDP
2
3
4
5
6
7
8
9
10
Response of LENERGY to LENERGY
.05
.05
.04
.04
.03
.03
.02
.02
.01
.01
.00
.00
-.01
-.01
-.02
-.02
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Variance Decomposition Analysis
The Variance Decomposition test tends to support the result of the IRF graphs and solidifies
the conclusions. From the Variance Decomposition graph below, we notice that for a period
of 10 years, GDP positively explains the variation in Energy consumption.
Variance Decomposition
Percent LGDP variance due to LGDP
Percent LGDP variance due to LENERGY
100
100
80
80
60
60
40
40
20
20
0
0
1
2
3
4
5
6
7
8
9
10
1
Percent LENERGY variance due to LGDP
2
3
4
5
6
7
8
9
10
Percent LENERGY variance due to LENERGY
100
100
80
80
60
60
40
40
20
20
0
0
1
2
3
4
5
6
7
8
9
10
1
2
3
4
5
6
7
8
9
10
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
4.2.Analysis, Results and Finding of the individual components of Energy
consumption in Ghana and their causality direction with relation to economic
growth.
Table 4: Presents the results of unit root tests, cointegration Granger causality tests.
Energy type
Unit
Root
Tests
ADF:(-5.58)
PP: (-5.78)
I(1)*
Lag
Selection
SC: 1
FPE: 1
HQ: 1
AIC: 1
Cointegration
Fossils
ADF: (-8.28)
PP: (-7.36)
I(1)*
SC: 1
FPE: 1
HQ: 1
AIC: 1
None
VAR
Biomass
ADF: (-6.75)
PP: (-6.75)
I(1)*
SC: 1
FPE: 1
HQ: 1
AIC: 1
None
VAR
Electricity
Method
Trace Test: 1 VECM
cointegrating
equation
Maximum
Eigen Value: 1
cointegrating
equation
Causality
A significant
long
run
causality was
identified
between GDP
growth
and
Electricity.
However, no
shortrun
VECM
Granger
causality
existed
between
the
GDP growth
and Electricity
VAR Granger
causality was
found
from
fossil
consumption
to
GDP
growth
A
bidirectional
causality was
found between
biomass
consumption
and
GDP
growth
Source:Researcher’s analysis and all data were collected from the World Bank data on Ghana. I(1)* means,
stationary after the first differencing. Fossil is combination of oil and natural gas energy consumed and Biomass
energy data was taken as combustible renewals and waste as a percentage of total energy use by the World
Bank. Also electricity consumption data was taken from the world bank data source.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Table 5; Table of Long run and causality hypothesis
Long-run
Null Hypothesis
Short-run
Trace
Statistics
Granger causality
Prob
value
∆Lelectricity ---∆GDP_growth
0.429
(prob
value)
∆Lelectricity does not
have a long run
16.7
relationship with
∆LGDP_growth
(0.0328*)
∆ GDP_growth ---∆electricity
0.757
∆Lfossils does not
have a long run
relationship
with 7.47
∆LGDP_growth
(0.524)
∆Lfossils
∆GDP_growth
∆ GDP_growth ---∆fossils
∆LBiomass does not 11.64
have a long run
relationship
with (0.175)
∆LGDP_growth
∆LBiomass
∆GDP_growth
0.0005*
0.3670
0.0428**
∆ GDP_growth
∆Biomass
0.0001
*Indicates significance at 1% whiles ** indicates significance at 5%
5. Conclusions and Policy Recommendation
From the first bi-variate framework which used a VAR approach between FDI and GDP
growth, no directional causality was found from energy use to GDP growth. However, GDP
growth had a positive directional granger causality with Energy consumption. This means
that Ghana can predict how much energy it will be consuming in the future by looking at the
rate of economic growth. The policy recommendation based on this finding are that, as the
country seeks to increase in economic growth, government should put in place measures on
how to sustain the country’s energy use since it will also be expected to rise so that industries
and household do not run out of energy for production and efficiency.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
The study also found out that there exist a Lon run cointegration relationship between
electricity consumed in Ghana and economic growth. This economically means that, there
exist an economic relationship between electricity and growth in Ghana. Also, it was
discovered from the results of the bivariate framework between electricity consumption and
economic growth that, there is no directional granger causality between the two. Simply put,
values of electricity consumption cannot predict economic growth in Ghana and vice versa.
However, the study found a positive dynamic linkage between electricity use and GDP
growth. Policy recommendation to this effect is that, since there exist an economic
relationship, government should place effective measures to stop the continues blackouts in
the country since it will affect the economy in the long run.When this is properly addressed,
this may cause a directional causality between the two.
Another bi-variate analysis the study undertook was between fossil consumption and
economic growth. It was revealed that there exists a directional granger causality flowing
from fossil consumption to economic growth. The dynamic linkage between the two was also
positive. This generally means that, when government is able to make petroleum (most
commonly used fossil) available for the industry and transport sector with no difficulty, the
economy will growth positively. Therefore, policies that will be targeted on making constant
and available petroleum products should be strongly encouraged so that the Ghanaian
economy can grow positively.
An interesting observation established in this study is the bi-directional granger causality that
exist between Biomass consumption and economic growth in Ghana. The Biomass product
mainly used as energy source in Ghana is burnt wood (charcoal). This is largely used in both
the rural and urban areas of Ghana for household purposes. The results indicate that we can
predict economic growth in Ghana by looking at the amount of biomass we consume.
However, the dynamic linkage between the two is negative. This means as we rely on burnt
wood for energy in Ghana, we will experience a decrease in GDP growth. Therefore, policies
reduce biomass consumption in Ghana must be encouraged in Ghana.
References
Abaidoo, R. (2011). Economic growth and energy consumption in an
emergingeconomy: augmented granger causality approach. Research in
Business and Economics Journal .
Adom, P. K. (2011). Electricity Consumption-Economic Growth Nexus: The
Ghanaian Case. International Journal of Energy Economics and Policy, 1(1) ,
pp.18-31.
Akan, Y., Doğan, M. E., & Işık, C. (2010). The Causality Relationship Between
Energy Consumptionand Economic Growth: The Case of Turkey. Enerji, Piyasa
ve Düzenleme , 101-120.
Altinay, G., & Karagol, E. (2005). Electricity consumption and economic
growth:Evidence from Turkey. Journal on Energy Policy. Vol 27 , 849-856.
Apergis, N., & Payne, J. E. (2010). Renewable energy consumption and economic
growth: Evidence from a panel of OECD countries. Energy Policy, 38 , 656-660.
Proceedings of 7th Annual American Business Research Conference
23 - 24 July 2015, Sheraton LaGuardia East Hotel, New York, USA, ISBN: 978-1-922069-79-5
Chen, S., Kuo, H., & Chen, C. (2007). The relationship between GDP and electricity
consumption in 10 Asian countries. Energy Policy , 2611-2621.
GHANA, E. C. (2014). NATIONAL ENERGY STATISTICS FROM 2000-2014. Accra:
Strategic Planning and Policy division.
Gujarati, N. D. (1995). Basic Econometrics. New York: McGraw-Hill.
Gujarati, N. D. (1995). Basic Econometrics. New York: McGraw Hill.
Huang, B., Hwang, M. J., & Yang, C. W. (2008). Causal relationship between energy
consumption and GDP growth revisited: A dynamic panel data approach.
Ecological Economics,66 , 41-54.
Juselius, K. (2006). The Conintegrated VAR Model; Methodology and Applications.
Great Clarebdon Street, Oxford: Oxford press.
Kraft, J., & Kraft, A. (1978). On the Relationship between Energy and GNP. Journal
of Energy Development , 401-403.
Lee, C. C., & Chang, C. P. (2008). Energy Consumption and Economic Growth in
Asian Economies: A More Comprehensive Analysis Using Panel Data.
Resource and Energy Economics 30 , 50-65. .
Mehrara, M. (2007). Energy consumption and economic growth: The case of oil
exporting countries. Energy Policy, 35 , 2939-2945.
Oh, W., & Lee, K. (2004). Energy Consumption and Economic Growth in Korea:
Testing the Causality Relation. Journal of Policy Modeling,26 , 973–981.
Paul, S., & Bhattacharya, R. N. (2004). Causality between Energy Consumption and
Economic Growth in India: A Note on Conflicting Results. Energy Economics
26, , 977–983.
Soytas, U., & Sarı, R. (2003). Energy Consumption and GDP: Causality Relationship
in G-7 Countries and Emerging Markets. Energy Economics 25 , 33–37.
Stern, D. (1993). Energy and economic growth in the USA, A multivariate approach.
Energy Economics15(2), , 137-150.
Ucan, O., Aricioglo, E., & Yucel, F. (2014). Energy Consumption and Economic
Growth Nexus: Evidence from Developed Countries in Europe. International
Journal of Energy Economics and Policy , pp.411-419.
Wolde-Rufael, Y. (2004). Disaggregated Industrial Energy Consumption and GDP:
The Case of Shanghai,1952 TO1999. Energy Economics 26 , 69–75.