Journal of Empirical Economics Vol. 3, No. 3, 2014, 108-120

Energy Consumption and Economic Growth in Nigeria: Correlation or Causality? Bernard O. Muse1 Abstract This study examined the causal relationship between economic growth and energy consumption in Nigeria during period 1980-2012 and employed the co-integration test, OLS analysis, error correction model and pairwise granger causality test techniques. The cointegration test result revealed that there was a long run relationship among our variable of interest. The study found that electricity is an important factor in economic growth in Nigeria. The result is thus an indication that energy consumption enhances economic growth enormously. Furthermore, the result from causality test shows that there is a bi-directional casual relationship between total energy consumption and economic growth in long run. The policy implication of this finding was that the policy maker need improve the power sector so as to harness the potential of electricity in growing the economy. 1. Introduction The casual relationship between economic growth and energy consumption had been examined in numerous studies, though the direction of the causality relationship remains unresolved. The discussion has focused on whether economic growth affects energy consumption or energy consumption affects economic growth, or whether a bidirectional relationship exists between them or they are independent variable. It suffices to say here that findings of these empirical studies have different policy implication for the economy. For instance, In a case of uni-directional causality that runs from economic growth to energy consumption, the economic growth rate would be not be affected by a rise in energy tax rate for the simple reason that as the national income increases, its energy consumption also will increase. In a case of uni-directional causality that runs from economic growth to energy consumption, the economic growth rate would be not be affected by a rise in energy tax rate for the simple reason that as the national income increases, its energy consumption also will increase. Therefore, it is important to determine the causality relationship energy consumption and economic growth or to determine whether energy consumption is a significant determinant of economic growth or vice versa? As indicated above, the solution to this question will assist the governments to formulate an appropriate policy on energy conservation. Specifically, if energy consumption is a vital component in economic growth, energy conservation policies which reduce energy consumption may adversely affect real GDP, employment rate, low income, etc. Therefore the thrust of this study is to re-investigate and establish the relationship between energy consumption and economic growth using an updated time series data from 1970 to 2012. The research question the study seeks to address among others is; what is the causal relationship between energy consumption and economic growth? Does Nigeria data support energy-led growth of growth-led energy consumption? Analyzing the causal relationship between energy use and economic growth is important given the threat of hydrocarbon and, hence, the need to reduce energy use so as to stem CO2 emissions and help halt climate change. The study evaluates whether the economic benefit from the high energy consumption can neutralize the positive externalities inflicted on the society or not. If the marginal social benefit of economic 1

lecturer: Department of Mathematics and Statistics at Rufus Giwa Polytechnics, Owo, Ondo State, Nigeria.

© 2014 Research Academy of Social Sciences http://www.rassweb.com

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Journal of Empirical Economics growth is greater than the marginal cost of environmental impact, it is therefore worthwhile to increase energy intensity to improve growth and vice versa. We provide a concise review of some literature in Table 1. Summary of Literature Review on the Relationship between Energy Consumption and Economic Growth Period Number Study/ Econometrics Of of Findings/Conclusion Author Techniques Study Countries OlatunjiAdeniran

19802006

Nigeria

Granger casuality

GDP→EC(Coal, Electricity) Positive relationship between current period energy consumption and economic growth. With the exception of coal which was positive

Gbadebo Olusegun et al

19702005

Nigeria

Co-integration

Douglason G. Omotor

19702005

Nigeria

Johansen cointegration and granger casuality

Yusuf Umar,YahyaZakari

19802010

Nigeria

Autoregressive distributed lag(ARDL) bound test

Bright Orhewere,Machame Henry

19702005

Nigeria

Omisakin A. Olusegun

19702005

Nigeria

Olusanya, Samuel Olumuyiwa

19852010

Menyah, K. &Wolde-Rufael, Y.

19652006 19802008 19972007

Yusuf Umar,YahyaZakari RoulaInglesi-Lotz

Nigeria

South Africa Nigeria South Africa Nigeria and South Africa

vector error correction based Granger causality test Bounds Testing Cointegration Approach

Ordinary Least Square method

Bounds test approach to cointegration ARDL-bounds testing procedure ARDL-bounds testing procedure Hsiao’s Granger Casuality

Harrison O. Okafor

19802010

AnsgearBelke,ChristianDreg er Bampatsou Christina, Hadjiconstantinou George

19812007 19802004

John Asafu-Adjaye

19711995

4

cointegration and ECM techniques

Adaidoo Rexford

19782010

Ghana

Sims (1972) test based on Granger’s (1969) definition of

25 31

Unit root, co integration analysis Data Envelopment Analysis (DEA)

EC ↔ GDP petroleum consumption, coal consumption, and electricity consumption have a long run relationship with economic growth. Elec → GDP in the short-run and long-run. Oil → GDP in the long run Elec → GDP TEC → GDP Petroleum, Electricity are positively related to Nigeria economic growth while coal and Gas shows that there is a negative relationship with Nigerian economic growth. EC→GDP EC→GDP TEC→GDP growth EC↔GDP ELC↔GDP EC↔GDP EC→GDP EC→GDP India and Indonesia EC↔GDP Thailand and the Philippines EC→GDP 109

B. O. Muse causality Shahidan,S., Hussain,E. and Mohammed. I.(2012).

19802010

Malaysia

Arshia Amiri

19602005

France

Asghar Zahid

19712003

5

Seetanah Boopen, Neeliah Harris

19602011

Mauritius

Granger casuality test

Mohammed El Hedi,Adel Ben Youssof et al

19812005

12

panel unit root tests and cointegration techniques

ELC↔GDP

Najid, A. et al

19732006

Pakistan

Augmented Dicky Fuller test

GDP→EC

Tolga Omay,Mubariz

Jude c. Eggoh,Chrysost Bangake S. Noor, M. W. Siddiqi

19772007

19702006 19712006

7

21 5

Phung Thanh Binh

19762010

Vietnam

Inuwa Nasiru

19802010

Nigeria

Gudarzi Farahani

19802010

Iran

Davoud Sadeghi et al

19732006

1

Aviral Kumar

19712011

India

Helmi Hamdi

19802008

Bahrain

Rexford Abaidoo

19962010

Ghana

Anjum, A, & Butt, M

1980-

Pakistan

Johansen cointegration

EC→GDP

geostatistical models (kiriging and IDW) Error Correction Model and Toda and Yamamoto(1995) approach.

EC→GDP

Non-linear panel cointegration test and nonlinear Panel Vector Error Correction Model panel cointegration and causality tests Panel cointegration, ECM and FMOLS threshold cointegration and vector error correction models for Granger causality tests two-step residualbased approach to co integration and Granger causality test cointegration and error correction model cointegration and error correction model Granger approach (VECM framework) along with the Dolado and Lütkepohl‟s approach cointegration and error correction model Sims (1972) test based on Granger’s (1969) definition of causality techniques of co-

EC→GDP EC→GDP

EC→GDP

EC↔GDP EC→GDP

EC→GDP

GDP→Coal GDP→EC EC→GDP Energy consumption does not Granger-cause GDP

EC↔GDP bi-directional relationship between electricity consumption and GDP EC→GDP 110

Journal of Empirical Economics 2001

Aslan Alper et al Mohd Shahidan Eggoh, J. C., Bangake,C., and Rault,C. (2011)

19972009 19952012 19702006

Melike Bildirici

19702010

Joyeux, R & Ripple, R

19802007

BabajideFawowe

19712004

Ziramba Emmanuel

19802009

USA Malaysia

21

12

30 OECD and 26 non-OECD 14subsaharan countries (Egypt included)

integration and Hsiao’s version of Granger causality panel co-integration method Johansen cointegration

EC↔GDP

Autoregressive Distributed Lag(ARDL) bounds testing approach and vector errorcorrection models (VECM)

The causality analysis reports that growth hypothesis exists in Cameron, Congo Rep., Ethiopia, Kenya and Mozambique and the conservation hypothesis in Senegal and Zambia. For Gabon, Ghana and Guatemala, there exists the bidirectional causality between economic growtth and electricity consumption.

Panel cointegration

EC↔GDP

Egypt

Cointegration

the authorities in Egypt and South Africa can implement conservation policies which seek to reduce hydroelectricity consumption without adversely affecting their economies‟ growth rates

19772007

7

Chiou-Wei, S., Chen, C., Zhu, Z.

19712011

Taiwan

Granger causality

Greece

ECM

Altinay and Karagol (2004) Oh and Lee (2004)

Lee and Chang (2005)

19701999 19542003

GDP→EC

Pane cointegration test

Tolga Omay,Mubariz

19601996 19502000

ELC→GDP

Panel cointegration and causality tests

Non-linear panel cointegration test and nonlinear Panel Vector Error Correction Model

Hondroyiannis et al. (2002)

EC↔GDP

EC→GDP

Causality does not run from electricity consumption to economic growth EC↔GDP

Granger causality

GDP~EC

Korea

Granger causality and ECM

EC→GDP

Taiwan

Johansen, Cointegration, VEC

EC→GDP

Turkey

111

B. O. Muse Zamani (2007)

19672003

Iran

VECM

GDP→EC

Ang (2008)

19711999

Malaysia

Co-integration, VECM

GDP→EC

19702006

Turkey

Causality, Cointegration

EC↔GDP

Co-integration, Causality

EC→GDP (India, Indonesia)

Erdal et al. (2008)

Asafu-Adjaye, J. (2000)

Soytas and Sari (2003)

Yang (2000) Odhiambo,N.M,(2011) S. Noor and M. W. Siddiqi

19711995

19501992

G-7 countries

19541997 19712006 19712006

Taiwan Tanzania 5 31

Bampatsou, C. &Hadjiconstantinou, G.(2010).

2004 only

Mahmoudinia,D., et al. (2013).

19732006

Alkhathlan, K. ,Alam, M., &Javid, M. (2012).

India, Indonesia Philippine, Thailand

Iran Saudi Arabia

Co-integration, Causality

Granger causality ARDL Bounds testing ECM and FMOLS

EC→GDP (Turkey, France, Japan, Germany) GDP→EC (Italy, Korea) EC↔GDP (Argentina) GDP~EC (Brazil, India, Indonesia, Mexico, Poland, South Africa, US, UK, Canada) ELC↔GDP ELC→GDP EC→GDP

Data Envelopment Analysis (DEA) method

EC→GDP for India and Indonesia EC↔GDP for Thailand and Philippines

ARDL Bounds testing

EC→GDP

A Multivariate Cointegration Analysis

EC→GDP

The goal of this study is to employ relevant and more up-to-date data to re-investigate the relationship between energy demand and economic growth employing five energy components - gas, coal, electricity, petroleum and total energy consumption. The paper is organized as follows; next section discusses the methodology and model follow by data analysis and interpretation of result. The final section contains concluding remarks. 2. Material and Method Data and Model Annual time series data from 1980 to 2012 for Nigeria were utilized in this study. The sample period was based on uniform available data in the study countries. Gross domestic product based on purchasingpower-parity (PPP) per capita in 2012 US dollars was used as proxy for economic growth. Furthermore, time series data for electricity consumption is in billion kilowatt, coal in thousand short tons, natural gas inbillion cubic feet, petroleum in thousand barrels and total energy consumption in quadrillion btu. All variables are in natural logarithms. Data are obtained from the International Monetary Fund, World Economic Outlook (WEO) data and Energy Information Administration (EIA) energy statistics. 112

Journal of Empirical Economics Taking inference from the empirical findings and theories, which has been derived from the theoretical exposition of the exogenous growth theories and then making energy central to the equation, we estimate the model specified below; GDP = αo + α1PT + α 2EC + α3CC + et ………………(A) Where; GDP = Gross domestic product PT = Petroleum Consumption EC = Electricity Consumption Cc = Coal Consumption et= Error term Prior to testing for cointegration, it is standard to check for stationarity of the series. Given that time series for macroeconomic variables such as GDP usually exhibit time trends i.e. their mean and variance depend on time and the covariance is not constant (Maddala, 2001; Harris and Sollis, 2003). In such cases, the series is non-stationary or I(1) (i.e. any sudden shock will not fade over time). Including a non-stationary variable in the model will result in spurious regression. Hence, before estimating the model, we need to test for unit root (i.e. test whether a series is non-stationary [I(1)], or stationary [I(0)]). To test for unit root, we employed augmented Dickey Fuller (Dickey and Fuller , 1981) and Phillips and Perron (1988). Once unit root tests establish that all variables are non-stationary at levels, to ensure obtaining nonspurious regression results, it is necessary to determine whether the variables are cointegrated and there exist a long run relationship among them. This can be done by applying Johansen’s test for cointegration (Maddala, 2001). The aim of this study is to re-investigate the relationship between energy consumption and economic growth. Once a cointergration relationship is estimated, an error correction model will be estimated to capture both short- and long-run effects of energy consumption on economic growth. The Cointegration Approach Cointegration can be defined simply as the long-term, or equilibrium, relationship between two series. This makes cointegration an ideal analysis technique to ascertain the existence of a long-term relationship between foreign direct investment and economic growth. The cointegration method by Johansen (1991; 1995) is used in this study. The Vector Autoregression (VAR) based cointegration test methodology developed by Johansen is described as follows; The procedure is based on a VAR of order p: yt= A1 yt-1+... + Apyt-p+ Bzt+ t

(1)

where yt is a vector of non-stationary I(1) variables (export and economic growth), ztis a vector of deterministic variables and t is a vector of innovations. The VAR may therefore be reformulated as: p−1

yt= П yt-1+ i=1 + Γiyt-p + Bzt+ t p Where П = i=1 Ai –I p andΓi = j=i+1 Aj

(2) (3) (4)

Estimates of Γi contain information on the short-run adjustments, while estimates of Π contain information on the long-run adjustments, in changes in yt. The number of linearly dependent cointegrating vectors that exist in the system is referred to as the cointegrating rank of the system. This cointegrating rank may range from 1 to n-1 (Greene 2000:791). There are three possible cases in which Πyt-1 ~ I (0) will hold. Firstly, if all the variables in ytare I (0), this means that the coefficient matrix Π has r=n linearly independent columns and is referred to as full rank. The rank of Π could alternatively be zero: this would imply that there are no cointegrating relationships. The most common case is that the matrix Π has a reduced rank and there are r … >λr> 0 and the associated eigenvectors β = (ν1, …νr). The co-integrating rank, r, can be formally tested with two statistics. The first is the maximum eigenvalue test given as: λ- max = -T ln (1- λr+1), .

(6)

Where the appropriate null is r = g cointegrating vectors against the alternative that r ≤ g+1. The second statistic is the trace test and is computed as: λ-trace = -T

n (1 − i=r+1 ln⁡

λi),

(7)

where the null being tested is r = g against the more general alternative r ≤ n. The distribution of these tests is a mixture of functional of Brownian motions that are calculated via numerical simulation by Johansen and Juselius (1990) and Osterwald - Lenum (1992). Cheung and Lai (1993) use Monte Carlo methods to investigate the small sample properties of Johansen’s λ-max and λ-trace statistics. In general, they find that both the λ-max and-λ trace statistics are sensitive to under parameterization of the lag length although they are not so to over parameterization. The Causality Analysis The most common way to test the causal relationship between two variables is the Granger-Causality proposed by Granger (1969). The test involves estimating the following simple vector autoregressions (VAR): Xt = Yt =

n i=1 αi Yt-i + m i=1 i Xt-i +

n j=1 βjXt-j + 1t m j=1 δjYt-j + 2t

(8) (9)

Where it is assumed that the disturbances 1t and 2t are uncorrelated. Equation (8) represents that variable X is decided by lagged variable Y and X, so does equation (9) except that its dependent variable is Y instead of X. Granger-Causality means the lagged Y influence X significantly in equation (8) and the lagged X influence Y significantly in equation (9). In other words, researchers can jointly test if the estimated lagged coefficient ΣαiandΣj are different from zero with F-statistics. When the jointly test reject the two null hypotheses that ΣαiandΣj both are not different from zero, causal relationships between X and Y are confirmed. The Granger-Causality test is easy to carry out and be able to apply in many kinds of empirical studies. We used the Augmented Dickey-Fuller (ADF) and Philip-Perron (PP) tests for which the null hypothesis is non-stationarityon. While the Augmented Dickey-Fuller approach accounts for the autocorrelation of the first differences of a series in a parametric fashion by estimating additional nuisance parameters, the Phillips-Perron unit root test makes use of non-parametric statistical methods to take care of the serial correlation in the error terms without adding lagged difference terms (Gujarati and Porter, 2009).

114

Journal of Empirical Economics 3. Results and Interpretations Unit Root Test Appropriate tests have been developed by Dickey and Fuller (1981) and Phillips and Perron (1988) to test whether a time series has a unit root. Table 1 shows the results of Dickey and Fuller (ADF) and the Phillips and Perron (PP) tests with constant only. The hypothesis of unit root against the stationary alternative is not rejected at both the 1 and 5% levels for petroleum and gas variable in Nigeria, under the two tests. However, the first differences of these variables are stationary under the two tests. Hence, we conclude that these variables are integrated of order 1. Table 1: Results of (ADF) and (PP) unit root test, constant only Variable level ADF Test PP Gdp 0.9961 0.9796 Δgdp 0.0002*** 0.0002*** Tec 0.0919 0.0827 Δtec 0.0000*** 0.0000*** Coa 0.7906 0.3479 Δcoa 0.0000*** 0.0001*** Pet 0.0338** 0.0311** Δpet Ele 0.8954 0.7511 Δele 0.0000*** 0.0000*** Gas 0.0414** 0.414** Δgas Following from the results presented in tables 1, our variables of interest are integrated of order one, l(1), it therefore necessary to determine whether there is at least one linear combination of the variables that is l(0). Following the modeling approach described earlier, we determine the appropriate lag length and conducted the cointegration test. Table 2 reports the optimal lag length of one out of a maximum of 3 lag lengths as selected by the three criterion. Optimal lag length of one (i.e m=1) out of a maximum of 3 lag lengths was selected for Nigeria by Schwarz Information Criterion and Hannan-Quinn Information Criterion.

Lag 0 1 2 3

Table 2: Lag Length Selection AIC SC HQ -5.6514 -5.3712 -5.5618 -9.7425 -7.7809* -9.1150* -9.3047 -5.6616 8.1392 -10.3711* -5.0465 -8.6677

The Cointegration test performed for the long run relationship among series by using Johansen and Juselius cointegration test is presented in Table 3. The result show a cointegration rank of two in Trace test at 5% significance level for Nigeria. Max-Eigen statistics found no evidence of long run relationship for series in Nigeria.

115

B. O. Muse Table 3: Co integration Rank Test Assuming Linear Deterministic Trend 0.05 Null Test Probability Critical Hypothesis Statistics Value Value r=0 102.5923 95.7536 0.0156** Trace Statistics r=1 70.5815 69.8188 0.0434** r=2 41.0288 47.8561 0.1878 Max-Eigen Statistics Trace Max-Eigen a

r=0 r≤1 No of Vectors No of Vectors

32.0107 29.5527 2 0

40.0775 33.8768

0.3024 0.1506

Denotes rejection of the null hypothesis at 0.05 level

In sum, the tests suggest that a long-run stable relationship exists among the variables of interest. Based on the existence of cointegration relationship for model at 5% significance level), we therefore estimate the long-run relationships using the Ordinary Least Squares (OLS) model and established the short-run dynamics of the model within an error correction model. Table 4: OLS Long Run Coefficient Estimates Coefficients (Dependent Probability Regressors variable = gdp) Value Constant 4.394658 0.0101** Tec -0.415240 0.0498** Coa -0.081241 0.0012*** Pet 0.179319 0.5390 Ele 0.825643 0.000*** R-squared 0.9524 Adjusted R-squared 0.9456 Durbin-Watson stat 1.600037 Prob(F-Statistic) 0.0000 Table 4 reported the regression analysis using OLS with the HAC or Newey-West standard error that take into account the problem of autocorrelation. For Nigeria, all the variables are statistically significant except petroleum consumption. The result reveals that a 1 percent rise in electricity consumption will lead to about 0.83 percent increase in economic growth, which suggests that electricity is an important factor in economic growth. However the result reveals that total energy consumption and coal consumption have negative relationship with GDP, a proxy for economic growth. For example a 1 percent rise in total energy consumption will bring about a 0.42 percent reduction in economic growth. Table 5: Parsimonious Error Correction of the Growth Model (Dependent Variable: Δgdp) Regressors Constant Δtec Δcoa Δpet Δele Ecm(-1)

Coefficient 0.034149 -0.094107 -0.000037 0.203632 0.205434 -0.134086

P - Value 0.0251** 0.4374 0.9975 0.3851 0.0462** 0.4484

* Δ first difference operator 116

Journal of Empirical Economics The short run results are shown in Table-5 and we used error correction method to obtain short run behavior of independent variables on development variable. The estimate of lagged ECM term identifies the speed of adjustment from short run towards long run equilibrium path. It is pointed out by Bannerjee and Newman (1998) that error correction term with negative sign and statistically significant at high level of significance further corroborates established long run relationship between the variables. Our empirical exercise considers that the estimated value of coefficient of ECMt-1 for Nigeria and South Africa are 0.134086 and -0.063978 but they are not statistically significant even at 10 percent significance level. This shows that any changes in short run towards long run is corrected by about13.41 percent per year in Nigeria and 6.40 percent per year in South Africa. The study further shows that causality run from electricity to economic growth for both countries. On the final analysis we carried out a causality test using pairwise granger causality test to provide further evidence on direction of causality among our variables of interest. Table 6: Pair wise Granger Causality Test Null hypothesis F-Statistic Prob. Value Electricity does not granger cause coal 2.30935 0.1394 Coal does not granger cause electricity 1.03335 0.3178 GDP does not granger cause coal 2.09250 0.1587 Coal does not granger cause GDP 0.01200 0.9135 Total energy does not granger cause coal 3.10609 0.0885* Coal does not granger cause total energy 0.54064 0.4681 Petroleum does not granger cause coal 1.40810 0.2450 Coal does not granger cause petroleum 0.64154 0.4297 GDP does not granger cause electricity 10.8870 0.0026*** Electricity does not granger cause GDP 0.00538 0.9420 Total energy does not granger cause elecricity 1.20188 0.2820 Electricity does not granger cause total energy 0.36675 0.5495 Petroleum does not granger cause electricity 1.07274 0.3089 Electricity does not granger cause petroleum 0.23980 0.6280 Total energy does not granger cause GDP 0.55197 0.4635 GDP does not granger cause total energy 0.67612 0.4176 Petroleum does not granger cause GDP 0.56268 0.4592 GDP does not granger cause petroleum 0.37733 0.5438 Petroleum does not granger cause total energy 2.05933 0.1620 Total energy does not granger cause petroleum 0.30977 0.5821 The Granger-causality test for Nigeria is reported in Table 6. The results show an existence of a unidirectional causality that runs from total energy consumption to coal consumption and from GDP to electricity consumption. The decision on the direction of causality was made from the probability values of the tests. However, the granger causality result for others reveals that there is no causality. In summary, for Nigeria, a rise in economic growth will lead to more demand for electricity 4. Summary and Conclusion This study examined the causal relationship between economic growth and energy consumption in Nigeria during period 1980-2012 and employed the co-integration test, OLS analysis, error correction model and pairwise granger causality test techniques. The cointegration test result revealed that there exists a long run relationship among our variable of interest for Nigeria. For Nigeria, the study suggests that electricity is 117

B. O. Muse an important factor in economic growth. The result is thus an indication that energy consumption enhances economic growth enormously. Furthermore, the result from causality test shows that there is a bi-directional casual relationship between total energy consumption and economic growth in long run for Nigeria. Hence, an economy with inefficient energy consumption will witness a retarded economic growth. Since electricity is an important factor in in the level of economic growth in Nigeria, it becomes imperatives for policy maker to invest more in power sector in order to enhance economic growth in Nigeria. The findings of this study supports the conclusions of Adeniran (2007), Eggoh et al (2007), Youssof et al (2005), Helmi (2009), Rexford(2011), and Constantini (2005), who concluded that there is a bidirectional relationship that runs from energy consumption to economic Growth. Moreover, this results was different from the findings of Kumar(2012) and Shariff(2011), who suggested that energy consumption does not granger cause economic growth. References Abaidoo, R.(2010). Economic growth and energy consumption in an emerging economy: Augmented Granger Causality Approach. Research in Business and Economic Journal. Adeniran, O. (2009). Does energy consumption cause economic growth: Empirical Evidence from Nigeria. University of Dundee Journal of Economics, 2(12),1-33. Ahmed, N. (2012). Energy consumption and economic growth: Evidence From Pakistan. Australian Journal of Business and Management Research, 2(6), 9-14. Akarca, A.T., Long II. (1980). The relationship between energy and GNP: A Re-examination. Journal of Energy and Development, Spring, 326-331. Alkhathlan, K. ,Alam, M., &Javid, M. (2012). Carbon dioxide emissions, energy consumption and economic growth in Saudi Arabia: A Multivariate Cointegration Analysis. British Journal of Economics, Management & Trade 2(4), 327-339. Altonay, G., Karagol, E., 2004. Structural break, unit root, and the causality between energy consumption and GDP in Turkey. Energy Economics 26, 985–994. Amiri, A., &Zibaei, M. (2012).Granger causality between energy use and economic growth in France using geo statistical models. Department of Agricultural Economics, College of Agriculture. Ansgear, B., Dreger, C., and Haan, F. (2010). Energy consumption and economic growth: New Insights into the Cointegration Relationship. Ruhr Economic Papers, 190. Aqeel, A. (2001). The relationship between energy consumption and economic growth In Pakistan. AsiaPacific Development Journal, 8(2), 3-6. Asafu-Adjaye, J., (2000.)The relationship between energy consumption, Asian developing countries. Journal of Energy Economics 22, 615–625. Asghar, Z. (2008). Energy-GDP relationship: A Causal Analysis for the Five Countries of South Asia. Applied Econometrics and International Development journal, 8(1). Aslan, A. et al. (2013). Energy consumption and economic growth: Evidence From Micro Data. ASBBS Annual Conference, 20(1). Bampatsou, C. &Hadjiconstantinou, G.(2010), International Jounal of Economic Sciences and Applied Research, 2(2), 65-86. Bildirici, M.,(2013). The analysis of relationship between economic growth and electricity consumption in Africa by ARDL Method. Energy Economics Letters, 1(1), 1-14. Binh,P. (2011). Energy consumption and economic growth in Vietnam: Threshold Cointegration and Causality Analysis. International Journal of Energy Economics and Policy 1(1),1-17. 118

Journal of Empirical Economics Chiou-Wei, S., Chen, C., Zhu, Z. (2008). Economic growth and energy consumption revisited Evidence from linear and nonlinear Granger causality. Energy Economics, 30, 3063-3076. Christina,B., & George, H.(n.d). The use of the DEA method for simultaneous analysis of the interrelationships among economic growth, environmental pollution and energy consumption. International Journal of Economic Sciences and Applied Research 2(2), 65-86. Constantini, V. & Martini, C. (2010) The causality between energy consumption and economic growth: A Multi-sectoral Analysis Using Non-stationary Cointegrated Panel Data. Energy Economics, 32, 591603. Dantama,U., Abdullahi, Y., &Inuwa, N. (n.d). Energy consumption - economic growth nexus in Nigeria: An Empirical Analysis Based on ARDL Bound Test Approach. European Scientific Journal,8(12). Douglason G.O. (2008).Causality between energy consumption and economic growth in Nigeria. Pakistan Journal of Social Sciences 5(8), 827-835. Eggoh, J. C., Bangake,C., and Rault,C. (2011). Energy consumption and economic growth revisited in African countries. CESifo Working Paper,3590.. Gbadebo,O., and Okonkwo C. (2009). Does energy consumption contribute to economic performance? Empirical Evidence from Nigeria. Journal of Economics and Business, (12)2. Glasure, Y. U. (2002). Energy and national income in Korea: Further evidence on the role of omitted variables. Energy Economics 24, 355-365. Hamdi,H. and Sbia,R. (n.d). Dynamic relationship between electricity consumption and economic growth in a small open economy: the case of Bahrain. Research Directorate at the Ministry of Finance. Hondroyiannis, G., Lolos, S., Papapetrou, E., (2002.) Energy consumption and economic growth: assessing the evidence from Greece. Johansen, S. (1988). Statistical analysis of cointegration vectors, Journal of Economics Dynamics and Control, 12, 231-254. Kraft, J., Kraft, A .(1978). The relationship between energy and GNP. Journal of Energy and Development, 3, 401–403. Mahmoudinia,D., et al. (2013). Oil products consumption, electricity consumption –economic growth Nexus in the Economy of Iran: A Bounds Testing Co-integration Approach. International Journal of Academic Research in Business and Social Sciences, 3(1). Menyah,K.,Rufael,Y. (2010). Energy consumption, pollutant emissions and economic growth in South Africa. Energy Economics Journal 32, 1374–1382. Mohammed, H. A.,Youssef,A.B., M’henni H., and Rault, C.(2012). Energy consumption, economic growth and CO2 emissions in Middle East and North African Countries.IZA Discussion Paper, 6412. Moroney, J.(1992), “Energy and the macroeconomy: Capital Spending After an Energy Cost Shock,” Advances in the Economics of Energy and Resources, Greenwich, CN: J.A.I. Press, 7, 101-124, Noor,S., and Siddiqi,M.(2010). Energy Consumption and Economic Growth in South Asian Countries: A Co-integrated Panel Analysis. International Journal of Human and Social Sciences, 5(14). Odhiambo,N.M,(2011). Economic growth and carbon emissions in South Africa: An Empirical Investigation. International Business & Economics Research Journal, 10(7). Oh, W. and K. Lee. (2004). Causal relationship between energy consumption and GDP revisited: the case of Korea 1970-1999. Energy Economics 26, 51-59. Olusanya,S.O.(2012).Longrun relationship between energy consumption and economic growth: Evidence From Nigeria. IOSR Journal of Humanities and Social Science (JHSS). 3(3), 40-51. 119

B. O. Muse Omisakin, O.A.(2008). Energy consumption and economic growth in Nigeria:A Bounds Testing Cointegration Approach. Journal of Economic Theory 2(4),18-123. Orhewere,B., &Machame,H.(2011). Energy consumption and economic growth in Nigeria. JORIND 1(9), 1596-8308. Okafor,H.(2012). Testing the relationship between energy consumption and economic growth: Evidence From Nigeria and South Africa. Journal of Economics and Sustainable Development, 3(11), RoulaInglesi-Lotz, R.(2012). On the causality and determinants of energy and electricity demand in South Africa: A Review. University of Pretoria. Seetanah, B.(2012). Energy use, economic growth and trade: Evidence from Mauritius. ICRI Journal. Soytas, U. and R. Sari. 2003. Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Economics 25: 33–37. Soytas, U., Sari, R. (2003). Energy consumption and GDP: Causality Relationship in G-7 Countries and Emerging Markets. Energy Economics 25, 33-37. Shahidan,S., Hussain,E. and Mohammed. I.(2012). Relationship between Energy Consumption and Economic Growth: Empirical Evidence for Malaysia. Business Systems Review,2(1) 2280-3866. Stern, D. I. (1993). Energy growth in the USA: A multivariate approach. Energy Economics 15, 137-150. Omay, T.(2012), Energy consumption and economic growth: Evidence from Nonlinear Panel Cointegration and Causality Tests. Journal of Energy and Development, 10(3) Tiwari,A.K.,(2011). Energy Consumption,CO² Emissions And Economic Growth: Evidence From India. Journal of International Business and Economy, 12(1), 85-122. Umar et al. (2009), Energy consumption and economic growth in Nigeria: ARDL Bound Analysis, European Scientific Journal, 7(11). Yu, E.S. and Choi, J. (1985). The causal relationship between energy and GNP: An International Comparison, Journal of Energy and Development, 10(2), 249-72. Yusuf, D. &Yahya, Z.(2011). Energy consumption- economic growth nexus in Nigeria: An Empirical Assessment Based on Bound Test Approach. European Scientific Journal, 8(12).

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