Causality relationship between growth and energy use: a case study of Vietnam

Policies and Sustainable Economic Development | 129 Causality Relationship between Growth and Energy Use: A Case Study of Vietnam NGUYEN THI TAM HIEN The University of Danang, Campus in Kon Tum - nttamhien@kontum.udn.vn NGUYEN THI PHUONG THAO The University of Danang, Campus in Kon Tum - ntpthao@kontum.udn.vn VU THI THUONG The University of Danang, Campus in Kon Tum - vtthuong@kontum.udn.vn Abstract Recently, Vietnam is becoming an energy-dependent country In spite of the important contribution of energy to Vietnam economic growth via import and industrial production, the increase in energy consumption also raises the concerns of resource scarcity, the overwhelming dependence on energy, and sustainable growth This study investigates the causal relationship between economy growth and energy consumption in the case of Vietnam from 1986 to 2013 Through testing different types of Granger Causality based on the Vector Error Correction Model (VECM), the main finding is the unidirectional Granger causal connection from energy consumption to economic growth, which is different from previous research in Vietnam In addition, the paper indicates the negative effect of energy consumption on economic growth Keywords: energy consumption; Granger causality; Vector Error Correction Model (VECM) 130 | Policies and Sustainable Economic Development Introduction Over past two decades, the dramatical increase in the demand for energy to meet the rapid economic development in the Asian countries along with inefficient energy use has caused energy scarcity Besides, the high volatility of energy prices along with the rising greenhouse gas emissions in recent years also has become a challenge to the sustainable development of these countries The building of energy conservation policy intended to ensure energy security as well as promote sustainable growth has attracted many scholars’ concerns It is important in design this policy is that policy makers must understand clearly the causal relationship between energy consumption and economic growth In other words, policymakers have to answer the question whether economic growth boosts energy consumption or whether energy consumption causes economic growth However, up to now, there has a lack of consensus among economists due to the mixed findings from previous studies In term of energy economics literature, there has a massive body of academic research on the linkage between energy consumption and economic growth These studies have conducted in various countries with different periods and using different econometric methodologies (Ozturk, 2010) However, the directions of this causality are still mixed and controversial among empirical pieces of research Azlina (2012) classifies the causal relationship between energy consumption and economic development into four views The first view suggests that economic development is considered as the main driver for energy demand economy grows It means that economy growth to lead to the energy demand of the economy also increases On the contrary to the first view, the second view points out the important roles of energy in the economic development process In addition to capital, labor, and materials, energy is considered as an input to production The third view shows that there has a two-way causal relationship between energy consumption and economic development The fourth view argues that both energy consumption and economic development are neutral with respect to each other Over more past two decades, Vietnam has witnessed impressive economic growth in the Southeast Asia However, its consumption of energy also increased tremendously accompanied by high economic growth In particular, before the impact of 2008 World Economic Crisis, the economic growth rates in Vietnam on average reached over percent for the period from 1990 to 2007 At the same time, the energy consumption per capita in Vietnam also increased by 9.3 percent per year1 CIEM (2012) also show that in two last decades, the economic growth in Vietnam has relied heavily on draining a lot of its natural resources and energy intensity levels has been higher than other countries in the region In particular, to generate $ 1,000 of GDP, Vietnam must consume about 600 kg of oil equivalent, 1.5 times higher than in Thailand and more than times compared to the average level of the world (Hong Quang, 2015) Do and Sharma (2011) also give a forecast that the total Toan, P K., Bao, N M., & Dieu, N H (2011) Policies and Sustainable Economic Development | 131 energy consumption in Vietnam is projected to rise from 55.6 Mtoe in 2007 to 146 Mtoe in 2025 Vietnam is facing the risk of dependence on imported energy This impressive performance has placed an interesting question to economists and policy makers of whether energy consumption is the cause or effect of economic growth in Vietnam To the best of our knowledge, there have been some studies examining the energy-growth nexus, such as Chontanawat et al (2008), Phung (2011), Le (2011), and Nguyen (2012) However, this causal relationship between energy consumption and economic growth has not been reached to a consensus among economists2 Awareness of the importance of understanding the causal relationship between energy consumption and economic growth in policy implications leads us to continue this issue Performing Granger causality test and Vector Error Correction Model (VECM) using the energy consumption per capita and income per capita from the year 1986 to 2013, this paper aim to answer the following questions: (1) Does there exist causality between economic growth and energy consumption in shortterm and in long-term? (2) If yes, what is the sign and magnitude of such effects? The remainder of this paper is organized as follows: Section briefly reviews the literature on the causal relationships between energy consumption and economic growth Section outlines the data and the econometric methodology Then, the econometric results are discussed in section Conclusions follow in Section Literature review The causal linkage between energy consumption and growth has been a widely studied topic in the literature for a long time Since the pioneering work of Kraft and Kraft (1978), which concluded a unidirectional causality from income to energy consumption in the United States for the 1947-1974 period, many economists have joined the debate with either supporting or conflicting views during the next two decades For example, Abosedra and Baghestani (1989) confirms the Kraft and Kraft’s result by applying the standard test of Granger causality Cheng and Lai (1997) with Taiwanese data from 1955 to 1993 and Cheng (1999) with two time series of India also support the causality running from GDP to energy consumption without feedback Conversely, Akarca and Long (1980) detect the issue of temporal sample instability in Kraft and Kraft’s study Therefore, by replacing the time period, the relationship turned out to be no statistically significant The same neutrality property is proved by Yu and Hwang (1984), Yu and Jin (1992) with updated data to 1979 and 1990, respectively Using standard Granger technique, Yu and Choi (1985) also have the same conclusion for the case of US, UK, and Poland However, the causal relationship runs from GNP to total energy consumption for South Korea and vice versa for the Philippines Masih & Masih (1996) also found different results when checking the growth-energy consumption nexus for Asian countries The Johansen's Tang, C F., Tan, B W., & Ozturk, I (2016) 132 | Policies and Sustainable Economic Development multivariate cointegration tests and dynamic vector error correction model (VECM) show mutual effects between development and energy use in Korea, Taiwan, and Pakistan In sum, most studies in this period used bivariate models and Granger causality approach Hence, major reasons for these contradicted findings may be the usage of different tests and lag terms for time series, the diversification in data of various countries and various time scales In the recent years, the debate on the growth-energy nexus is even more extensive and diversified with various research directions and economic techniques Both country-specific and multi-country provide a broad context of the research issue with the aim of drawing a definite conclusion on the relationship and its direction between economic growth and energy consumption This ambitious purpose has still not been achieved due to a consensus on the subject matter so far Table 2.1 provides a summary of controversial arguments in the last 15 years From the review of recent literature, some noticeable points are summarised as follows: - The previous two decades experience a vigorous debate of a large number of researches worldwide Some studies focus on a specific nation while others assess a group with certain common properties such as developing countries, industrialized countries, G-7 group, oil-exporting countries, same region or same continent countries - Except for the study of Stern and Enflo (2013) which uses 150-year time series for Sweden, the examined period often ranges from to decades, assuring the reliability of findings However, the most recent year is 2011 that is relatively out of date In the context of increasingly urgent environmental issues, research with more updated data should be carried out to draw conclusions that are more suitable - In terms of methodology and techniques, most of researches implement main steps: First, test the stationarity of the series or their order of integration in all variables, using Augmented Dickey-Fuller (ADF) test and Phillips-Perron (PP) test Some authors apply more tests such as Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test (Ang, 2008), Zivot and Andrews test (Altinaya & Karagol, 2004) to verify their findings Second, examine the presence of a long-run relationship, utilizing the popular approaches of Johansen (1991) or Pedroni (1997, 2004) A few studies are distinguished by some newly developed cointegration tests, for example, test of Pesaran et al (2001) and a modified version of the Granger causality test due to Toda and Yamamoto (1995) in the studies of Wolde-Rafael (2006), Fatai et al (2004) Third, identify the direction by employing Granger causality tests (VAR model) or vector error correction model (VECM) Some other techniques are also applied depending on data’s characteristics like Hsiao’s version of Granger causality tests (Aqeel & Butt 2001) Policies and Sustainable Economic Development | 133 - There are two types of models: (1) bivariate model in which two main variables are energy and growth (total or per capita) and electricity consumption seems to be the most popular measure energy use Other energy sources such as oil, gas are also mentioned in several studies (2) A multivariate model which usually adds energy price, labor, capital as exogenous factors - Although the results are mixed and contradicting, there are three main strands: (1) unidirectional causality which can be energy consumption – growth or growth – energy consumption directions; (2) bi-directional causality; (3) no relationship Since most of researches lead to policy suggestions accordingly to their findings, evidence in either direction is useful and important To be specific, if there is unidirectional causality running from energy consumption to economic growth, conducting and promoting energy preservation programs could harm the economy Similarly, with bi-directional causality, economic growth demands more energy at the same time energy levels also affect economic growth Energy-saving strategy, therefore, may slow down the momentum of development On the other hand, if the growth - energy consumption direction is found, policies aiming at reducing energy use may be implemented with little or no negative influence on economic growth The same suggestions will be made in the case of no causality Several studies on the growth-energy consumption relationship in Vietnam have only been carried out recently, including Phung (2011), Le (2011) and Nguyen (2012) Three authors used the bivariate model to study the relationship between energy consumption/ electricity consumption and economic growth in periods 1976 – 2010, 1975 – 2010, and 1986 – 2006 respectively Despite the different time scales, they came up with a unidirectional relation from growth to energy consumption These results could not explain the energy-led economy in Vietnam This makes difficult to understand the energy as a determinant of economic growth and giving implications to policymakers in energy policies or sustainable growth policies 134 | Policies and Sustainable Economic Development Table An overview and evaluation of existing literature Study Countries Period Methods Variables/models Results Paul & Bhattacharya (2004) India 1950–1996 Engle–Granger cointegration approach combined with the standard Granger causality test Multivariate model Bi-directional causality between energy consumption and economic growth Ghosh (2006) 1970-2002 Test stationary: ADF test Test co-integration: Johanson test VECM Bivariate model Unidirectional causality from GDP to petroleum consumption + In-sample forecasts till 2011– 2012 Soni, Singh & Banwet (2013) 1981-2011 Test stationary Test co-integration Granger causality & vector error correction mechanisms (VECM) Bivariate model Unidirectional causality: economic growth to electricity consumption) + Implications on policy No forecast 1971–1999 Unit root tests: ADF, PP, KPSS tests Johansen approach for the VARs constructed in levels Causality tests Multivariate model: add CO2 emissions Unidirectional causality: economic growth to energy consumption + KPSS test has higher power of rejecting the null Loganathan, Nanthakumar & Subramaniam (2010) 1971-2008 OLS Engel-Granger (OLS-EG) Dynamic OLS (DOLS) Autoregressive Distributed Lag (ARDL) Bounds testing approach & VECM Bivariate model Bi-directional causality between total energy consumption & GDP + Combination of different methods OLS-EG, DOLS, ARDL & VECM Azlina (2012) 1960-2009 Test stationary: ADF & PP tests Test co-integration VECM Multivariate model: add share of industry in GDP capital Unidirectional causality: income to energy consumption + Implications on policy + Consider both demand and supply side of energy 1950-2000 Test stationary: Zivot & Andrews test Granger non-causality test Bivariate model Unidirectional causality: electricity consumption to income Ang (2008) Altinaya & Karagol (2004) Malaysia Turkey Plus and minus point Policies and Sustainable Economic Development | 135 The standard Granger causality test Lise & Van Montfor (2005) 1970-2003 Test stationary: ADF test Test co-integration: Johanson test VECM Bivariate model (per capita energy and GDP) Unidirectional causality from GDP per capita to energy consumption per capita Jobert & Karanfil (2007) 1960-2003 Test stationary: ADF, PP tests Test co-integration: Johansen approach Multivariate model: add industrial value No relationship between real GDP and total energy + Tests on misspecification: Durbin-Watson, Godfrey, Ramsey’s reset, Lagrange multiplier, Breusch-Pagan Chow & Recursive Chow Stern & Enflo (2013) Sweden 1850-2000 Tests unit roots Granger causality tests using VAR model Test co-integration & VECM Multivariate models: add capital, labor The relationship has changed over time, but mostly energy causes growth + Long period (150 years) + Diversified techniques Glasure (2002) Korea 1961-1990 VECM approach Multivariate model: add government expenditure, money supply, oil prices Bi-directional causality between oil consumption and economic growth + Deal with the omitted variables ( control effects of the oil price, money supply, government spending and the oil price shocks 1970-1999 Unit root test: PP test Cointegration test: VAR and the corresponding VECM Causality test Multivariate model: labour and capital added Bidirectional causation between energy and GDP + The Newey and West (1987) method applied to choose optimal lag lengths Oh & Lee (2004) Mozumder & Marathe (2007) Bangladesh 1971-1999 Test stationary: ADF test Test co-integration: Johanson test VECM Bivariate model Unidirectional causality: per capita GDP to per capita electricity use + Policy implications Aqeel & Butt (2001) Pakistan 1955-1996 The co-integration technique and Hsiao’s version of Granger causality tests Bivariate model Unidirectional causality: economic growth to energy consumption + Hsiao’s Granger Causality test: more robust over arbitrary lag length selection and other systematic methods determining lag length 136 | Policies and Sustainable Economic Development Yang (2000) Taiwan 1954–1997 Unit root tests Cointegration panel tests VECM Bivariate model Bi-directional causality between total energy consumption and GDP Lolos & Papapetrou (2002) Greece 1960-1996 Unit root tests Cointegration panel tests VECM Multivariate model: add CPI Bi-directional causality between energy consumption (total and industry) and GDP Shiu & Lam (2004) China 1971-2000 Unit root tests Cointegration panel tests VECM Bivariate model Unidirectional causality: electricity consumption to real GDP Fatai, Oxley & Scrimgeour (2004) NZ 1960-1999 Toda and Yamamoto (1995) approach to check robustness of results Bivariate model Unidirectional causality: GDP to energy consumption Jumbe (2004) Malawi 1970-1999 Standard GC methodology VECM Bivariate model Bi-directional causality between electricity consumption (kWh) and GDP (+) comparison with Au, Indo, India, Philippine and Thai Unidirectional causality: nonagricultural-GDP to kWh Morimo & Hope (2001) Sri Lanka 1960-1998 Econometric model developed by Yang (2000) Bivariate model Unidirectional causality: electricity supply to real GDP Soytas & Sari (2003) G-7 countries and 16 emerging markets 1950-1994 Unit root test: DF , ADF ; PP test Cointegration panel tests VECM Bivariate model: Bi-directional causality in Argentina Unidirectional causality from GDP to energy consumption in Italy, Korea, vice versa in Turkey, France, Germany, Japan + Suggest a number of ways to reduce CO2 Narayan & Smyth (2008) G7countries 1972–2002 Panel unit root tests Panel cointegration tests Multivariate model: add capital stock, real Unidirectional causality: energy to growth (a 1% increase in energy + Panel cointegration test proposed by Westerlund (2006) allowing for multiple Policies and Sustainable Economic Development | 137 Granger causality and long-run structural estimation gross fixed capital formation per capita use increases real GDP by 0.12– 0.39%) structural breaks + Use suitable information criteria to select the optimum lag Yoo (2006) Asian countries 1971–2002 Stationary and co-integration tests Granger-causality method VECM Bivariate model Bi-directional causality between electricity consumption per capita and GDP per capita in Malaysia and Singapore Unidirectional causality: energy to growth in Thailand & Indonesia Mehrara (2007) 11 exporting oil countries 1971–2002 Panel unit-root tests Panel cointegration analysis Bivariate model Unidirectional causality: economic growth to energy consumption Lee (2005) 18 developing countries 1975-2001 Panel stationary tests with heterogeneous country effect Modified ordinary least square techniques (FMOLS) Multivariate model: add capital Unidirectional causality: energy consumption to GDP Joyeux & Ripple (2007) East Indian Ocean countries 1971-2001 Panel unit root tests Co-integration panel tests of Pedroni (1997, 2004) Biviarate model No co-integration: income and electricity consumption (household level) + Different research direction Other composite indexes (HDI) Wolde - Rafael (2006) 17 African countries 1971–2001 Newly developed cointegration test proposed by Pesaran et al (2001) Modified version of the Granger causality test of Toda and Yamamoto (1995) Bivariate model Different relationships and direction between countries + Suitable methods for studies that have small sample sizes Asafu-Adjaye (2000) India, Indonesia, Philippines Thailand 1973–1995 Unit root tests Cointegration panel tests VECM Multivariate model: add CPI Unidirectional causality: energy to income for India and Indonesia Bi-directional causality: energy to income for Thailand and the Philippines Source: Authors’ analysis and synthesis 138 | Policies and Sustainable Economic Development Data and econometric methodology 3.1 Data To carry out the analysis of Granger causal relationship between energy consumption and economic growth, this study uses the energy consumption in kg of oil equivalent per capita (denoted E) and GDP per capita in current $US (denoted Y) These secondary data is collected from World Development Indicators (WDI, 2016) Both data have the time horizons from 1986 to 2013 The logarithm form is applied to both variables to reduce heteroscedasticity 3.2 Econometric methodology To analyze the causal relationship between the energy consumption and economy growth, we apply the process as follows 3.2.1 Stationary testing This study investigates the stationary of logY and logE using augmented Dickey and Fuller approach – ADF (Dickey and Fuller, 1979) and Phillips – Perron test – PP (Phillips and Perron, 1988) The purpose of the stationary test is to avoid the spurious regression and then determine the order of integration of each variable The fitted regression model both logE and logY is expressed as: 𝑚 ∆𝑋𝑡 = 𝑎0 + 𝑎1 𝑡 + 𝑎3 𝑋𝑡−1 + ∑ 𝑏𝑖 ∆𝑋𝑡−𝑖 + 𝜀𝑡 𝑖=1 where: ∆ is the first difference of the log of variables t is the time or trend variables (if available) 𝜀𝑡 is the pure white noise error term 𝑚 is the maximum length of the lagged depentent variable Under the null hypothesis of stationary testing, the time series contain a unit root or - 𝐻0 : 𝑎3 = 0.The alternative hypothesis is 𝐻0 : 𝑎3 < For next cointegration test, Granger causality test, and, VECM, logY and logE are expected to be non-stationary at level and stationary at the first difference 3.2.2 Testing for cointegration The regression among nonstationary variables can lead to the spurious result that is not meaningful to decision making However, in the context that the time series in the study are under cointegration, the spurious regression does not occur In this interesting event, the cointegrated variables show the short- term deviation from their association that must converge to equilibrium in long-term Granger (1986) and Engle and Granger (1987) introduced the Engle-Granger test for cointegration between two variables, basing on the residual of a linear combination of variables Policies and Sustainable Economic Development | 139 Relying on this procedure, we conduct the cointegration test between logY and logE Loosely speaking, logY and logE are cointegrated if they are 𝐼~(1), and the residual of the linear combination of variables is an I(0) process Therefore, in nature, the Engle-Granger test or augmented Engle – Granger tests are ADF test The Engle – Granger test process is as follows First, we estimate following equations using ordinary least square – OLS method, and find the corresponding residuals 𝑙𝑜𝑔𝑌𝑡 = 𝑎1 + 𝑎2 𝑙𝑜𝑔𝐸𝑡 + 𝜀1𝑡 𝑙𝑜𝑔𝐸𝑡 = 𝑏1 + 𝑏2 𝑙𝑜𝑔𝑌𝑡 + 𝜀2𝑡 where 𝜀1𝑡 , 𝜀2𝑡 are the uncorrelated error terms with zero mean and constant variance The residuals are calculated as: 𝜀1𝑡 = 𝑙𝑜𝑔𝑌𝑡 − 𝑎1 − 𝑎2 𝑙𝑜𝑔𝐸𝑡 𝜀2𝑡 = 𝑙𝑜𝑔𝐸𝑡 − 𝑏1 − 𝑏2 𝑙𝑜𝑔𝑌𝑡 Then, we use ADF test mentioned above to test if the residuals are stationary The null hypothesis states that two variables are not cointegrated or the residuals obtained is not I(0) process The alternative hypothesis states the contrast 3.2.3 Granger causality test and VECM The test for causality is first introduced by Granger (1969) The core idea of the causality relation is “the correlation of current value of one variable and the past value of others” (Brooks, 2008) To address this issue, a simple linear regression between the variable and the lagged values of both itself and other variables is conducted In this study, the regression models are exhibited as follows: 𝑛 𝑛 𝑙𝑜𝑔𝑌𝑡 = ∑ 𝛼1𝑖 𝑙𝑜𝑔𝑌𝑡−𝑖 + ∑ 𝛼2𝑗 𝑙𝑜𝑔𝐸𝑡−𝑗 + 𝑒1𝑡 𝑖=1 𝑗=1 𝑛 𝑛 𝑙𝑜𝑔𝐸𝑡 = ∑ 𝛽1𝑖 𝑙𝑜𝑔𝐸𝑡−𝑖 + ∑ 𝛽2𝑗 𝑙𝑜𝑔𝑌𝑡−𝑗 + 𝑒2𝑡 𝑖=1 𝑗=1 where 𝑒1𝑡 , 𝑒2𝑡 are the uncorrelated error terms with zero mean and constant variance In the presence of causality, the coefficients of past logE in the first equation and/or the coefficients of past logY in the second equation are jointly equal to zero In particular, to investigate if logE causes logY, the joint hypothesis is that the logE does not Granger cause logY or α21 = α22 = ⋯ = α2j = Similarly, to invest if logY causes logE, the null hypothesis is that the logY does not Granger cause logE or β21 = β22 = ⋯ = β2j = 140 | Policies and Sustainable Economic Development There are four possible outcomes for the test above They are logE Granger causes logY, logY Granger cause loge; there is feedback between two variables and two variables are independent Nevertheless, the Granger Causality test just indicates the direction, but nothing of the sign and the magnitudes of the relationship between two variables Running VECM is necessary to find those missing answers The VECMs for logY and logE in this study are: 𝑛 𝑚 ∆logYt = φ0 + ∑ 𝜑1𝑖 ∆logYt−i + ∑ 𝜑2𝑗 ∆logYt−j + δECT1t−1 + ϑ1t 𝑖=1 𝑗=1 𝑛 𝑚 = φ0 + ∑ 𝜑1𝑖 ∆logYt−i + ∑ 𝜑2𝑗 ∆logYt−j + δ(logYt−1 − a1 − a2 logEt−1 ) 𝑖=1 𝑗=1 + ϑ1t 𝑛 𝑚 ∆logEt = θ0 + ∑ 𝜃1𝑖 ∆logEt−i + ∑ 𝜃2𝑗 ∆logEt−j + γECT2t−1 + ϑ1t 𝑖=1 𝑗=1 𝑛 𝑚 = θ0 + ∑ 𝜃1𝑖 ∆logEt−i + ∑ 𝜃2𝑗 ∆logYt−j + γ(logYt−1 − a1 − a2 logEt−1 ) 𝑖=1 𝑗=1 + ϑ2t The ECT1t−1 and ECT2t−1 respectively show the size of deviation of logY and logE from their corresponding long-term equilibrium 𝛿 and 𝛾 indicate how much disequilibrium of logY and logE in previous year is corrected next period The cointegrating vector [1 –a1 –a2] represent the coefficients of long-term relationship between logY and logE, in which a2 measures the energy consumption elasticity We will test three type of Granger Causality, including weak Granger causality, long-run Granger causality, and strong Granger causality In particular, the null hypotheses for each type are expressed as: Weak Granger causality 𝐻0 : ∑𝑚 𝑗=1 𝜑2𝑗 = : No Granger causality relationship 𝐻0 : ∑𝑚 𝑗=1 𝜃2𝑗 = 0: No Granger causality relationship Long-run Granger causality 𝐻0 : 𝛿 = : no Granger causal connection 𝐻0 : 𝛾 = : no Granger causal connection Strong Granger causality 𝐻0 : 𝛿 = ∑𝑚 𝑗=1 𝜑2𝑗 = 0: no Granger causal connection Policies and Sustainable Economic Development | 141 𝐻0 : 𝛾 = ∑𝑚 𝑗=1 𝜃2𝑗 = 0: no Granger causal connection Results and analysis 4.1 Unit root test The Augmented Dickey-Fuller tests are performed for both variables, logY and logE at the level and first difference and without trend and intercept The results are displayed in Table As can be seen from this table, logY and logE are individually non-stationary at level because the corresponding p-values are greater than any conventional level of significance However, they are stationary at the first difference at a significance level of 1%, or they follow the I(1) process Table The Augmented Dickey-Fuller and Phillips – Perron Unit root test 1st difference At level ADF PP ADF PP logY -0.263 -0.436 -3.831*** -3.852*** logE 1.018 0.759 -3.363** -3.383** Note: Table presents the test statistics of unit root test using Augmented Dickey Fuller approach and Phillips-Perron approach The asterisk indicate the level of significance, including *** (1%), ** (5%), * (10%) 4.2 Testing for cointegration Naturally, the selection of lag length is as a pre-estimation of VAR to obtain the information criteria by which compare to other criteria to determine the best-fitted model As can be seen from Table 3, all criteria confirm that four lags are the most suitable for the intended VECM model Table The selection of lag length Lag LR FPE AIC HQIC SBIC 004 127 153 225 151.8 9.8e-06 -5.865 -5.786 -5.570 8.9465 9.5e-06 -5.904 -5.774 -5.413 2.277 000012 -5.666 -5.483 -4.978 28.088* 5.6e-06* -6.502* -6.268* - 5.618* Note: Table reports the results of four information criteria to select the lag lengths for VECM as well as the likelihood ratio test (LR) Those four information criteria include final prediction error (FPE), Akaike’s information criterion (AIC), Schwarz’s Bayesian information criterion (SBIC) and the Hannan and Quinn information criterion (HQIC) The asterisk indicates the optimal lag at 10% 142 | Policies and Sustainable Economic Development Following the lag order selection is the test of cointegration which is presented in Table Since there are two variables in considered model, at most two cointegrated equations exist As a result, the trace statistic of r = (in which r is the number of the cointegrating equation) is of statistical significance This shows the rejection of the null hypothesis that there is one or no cointegrated relation This result determines only one equation of cointegration Table Test of cointegration Trace test Optimal lags length Null hypothesis Alternative hypothesis Trace statistics 𝑟=0 𝑟≥1 27.8565 𝑟=1 𝑟≥2 0.2255* Note: Table reports the number of the cointegrating equation R is the number of the cointegrating equation The asterisk * indicates the number of r chosen by Johansen’s multiple trace test process at 10% 4.3 The Granger causality test and VECM Table presents the result of Granger causality test between energy consumption and economic growth The statistically significant Wald F – test indicates the aid of logE on the prediction of logY This means the energy consumption is a strongly exogenous regressor in determining the economic growth However, the similar F-test refuses the reversal effect from income per capital to energy consumption per capita Table Granger causality test – residual-based approach Equation F-test p-value LogY on logE 9.63 0.0005 LogE on logY 0.88 0.4692 While demonstrating the unidirectional Granger causality relation from energy consumption to economic growth, it is still important to run the VECM whose results will give more information on the good or bad effect of energy consumption on the economy in both long-term and short-term Table displays the results of VECM Meanwhile, Table presents several Granger Causality test based on VECM results It is clear from Table that energy consumption has negative effects on economic growth in shortterm A 1% growth in energy consumption can cause a 1.1020% decrease in the economic growth next year The presence of such negative effect also maintains in long-term The cointegration equation is: 𝑙𝑜𝑔𝑌𝑡 − 2.2956×𝑙𝑜𝑔𝐸𝑡 + 7.3861 The energy consumption elasticity is of – 2.2956 and very significant, indicating the greater magnitude of negative effect and the long-run causality This evidence may support the energy conservation policies as well as other environmental protection policies Policies and Sustainable Economic Development | 143 The statistically significant coefficient of error correction term (ECT) points out the imbalance of economic growth in relation to energy consumption This coefficient equaling to – 0.4536 shows that on average, the economic growth will converge to its equilibrium point about 45,4% next year Such response is relatively considerable As can be seen from Table 7, the small p-values indicate the validity of three type of Granger causality relationship, including weak Granger Causality, Granger causality relation in long-term and strong Granger causality In contrast, the great p-values of test relied on the equation of logE shows the acceptance of null hypotheses or there is not enough evidence to Granger causality running from economic growth (logY) to energy consumption (logE) This results contrast to Phung (2011), Le (2011) and Nguyen (2012) who indicated the opposite unidirectional connection from economic growth to energy consumption Table VECM ∆𝑙𝑜𝑔𝑌𝑡−1 ∆𝑙𝑜𝑔𝑌𝑡−2 ∆𝑙𝑜𝑔𝑌𝑡−3 ∆𝑙𝑜𝑔𝐸𝑡−1 ∆𝑙𝑜𝑔𝐸𝑡−2 ∆𝑙𝑜𝑔𝐸𝑡−3 ECT ∆𝑙𝑜𝑔𝑌 36233*** (6.21) -.0072 (-0.15) 2258*** (4.59) -1.1020*** (-2.69) -.4631 (-1.30) 1909 (0.50) -.4536*** (-4.89) ∆𝑙𝑜𝑔𝐸 -.0291 (-0.77) 0253 (0.82) -.0229 (-0.72) 2152 (0.81) 0136 (0.06) -.1015 (-0.41) 0564 (0.94) Notes: Table displays the results of VECM The figure in parentheses () is the t-statistics The asterisk indicate the level of significance, including *** (1%), ** (5%), * (10%) Table Granger Causality results based on VECM Dependent variables ∆𝑙𝑜𝑔𝑌 ∆𝑙𝑜𝑔𝐸 Type Weak Granger Causality Long-run Granger Causality Strong Granger Causality Weak Granger Causality Long-run Granger Causality Strong Granger Causality Chi- squared Statistics 9.62 (0.0221) 23.87 (0.0000) 28.88 (0.0000) 1.69 (0.6398) 0.89 (0.3463) 3.74 (0.4429) ∆𝑙𝑜𝑔𝑌 𝐸𝐶𝑇 Excluded Reject/accept Null Hypothesis ∆𝑙𝑜𝑔𝐸 Reject 𝐸𝐶𝑇 Reject ∆𝑙𝑜𝑔𝐸, 𝐸𝐶𝑇 Reject Accept Accept ∆𝑙𝑜𝑔𝑌, 𝐸𝐶𝑇 Accept Notes: Table displays the Chi-squared statistics of three types of Granger Causality based on VECM The figure in parentheses () is the p-value 144 | Policies and Sustainable Economic Development Conclusion This paper studies the causality relationship between the energy consumption and economic growth in case of Vietnam from the year 1986 to 2013 This study finds the unidirectional Granger causality from energy consumption to economic growth Performing VECM shows that consuming more energy can reduce the Vietnam rate of growth in both short – run and long – run More importantly, this result may suggest the government to review the state of energy consumption, the dependence of the economy on energy as well as reassess the trade-off between the contribution of energy consumed industries to the whole economy and erosion of sustainable growth Therefore, the government should consider the policies of conserve energy, the use of renewable resources, and various green growth policies References Abosedra, S S., & Baghestani, H (1989) New evidence on the causal relationship between United States energy consumption and gross national product Journal of Energy and Development, 14(2), 285-292 Akarca, A T., & Long, T V (1980) On the relationship between energy and GNP: A reexamination Journal of Energy and Development, 5, 326-331 Altinay, G., & Karagol, E (2005) Electricity consumption and economic growth: Evidence from Turkey Energy Economics, 27(6), 849-856 Ang, J B (2008) Economic development, pollutant emissions and energy consumption in Malaysia Journal of Policy Modelling, 30(2), 271-278 Aqeel, A., & Butt, M S (2001) The relationship between energy and economic growth in Pakistan Asia-Pacific Development Journal, 8(2), 101-110 Asafu-Adjaye, J (2000) The relationship between energy consumption, energy prices and economic growth: Time series evidence from Asian developing countries Energy Economics, 22(6), 615-625 Azlina, A A (2012) Energy consumption and economic development in Malaysia: A multivariate cointegration analysis Procedia - Social and Behavioral Sciences, 65, 674 - 681 Brooks, C (2008) Introductory to econometrics New York: Cambridge University Press Cheng, B S (1999) Causality between energy consumption and economic growth in India: an application of cointegration and error-correction modeling Indian Economic Review, 34(1), 39-49 Cheng, B S., & Lai, T W (1997) An investigation of co-integration and causality between energy consumption and economic activity in Taiwan Energy Economics, 19(4), 435-444 CIEM (2012) Green GDP index: Research and development framework methodology Retrieved from: http://www.ciem.org.vn/Portals/0/CIEM/TinTuc/2012/08/Bao_ cao_chi_so_GDP_xanh.pdf Dickey, D A., & Fuller, W A (1979) Distribution of the estimators for autoregressive time series with a unit root Journal of the American Statistical Association, 74(366), 427-431 Do, T M., & Sharma, D (2011) Vietnam's energy sector: A review of current energy policies and strategies Energy Policy, 39(10), 5770-5777 Policies and Sustainable Economic Development | 145 Fatai, K., Oxley, L., & Scrimgeour, F G (2004) Modelling the causal relationship between energy consumption and GDP in New Zealand, Australia, India, Indonesia, the Philippines, and Thailand Mathematics and Computers in Simulation, 64(3-4), 431-445 Ghosh, S (2002) Electricity consumption and economic growth in India Energy Policy, 30(2), 125-129 Glasure, Y U (2002) Energy and national income in Korea: Further evidence on the role of omitted variables Energy Economics, 24(4), 355-365 Granger, C W J (1969) Investigating causal relations by econometric models and cross-spectral method Econometrica, 37(3), 424-438 Granger, C W J (1986) Developments in the study of cointegrated economic variables Oxford Bulletin of Economics and Statistics, 48(3), 213-228 Granger, C W J., & Engle, R F (1987) Co-integration and error correction: Representation, estimation and testing Econometrica, 55(2), 251-276 Hong Quang (2015) Ensuring energy security in Asia and Vietnam Retrieved from: http://tapchitaichinh.vn/nghiencuu trao-doi/trao-doi-binh-luan/bao-dam-an-ninh-nang-luong-o-chau-a-va-viet-nam-59375.html Jobert, T., & Karanfil, F (2007) Sectoral energy consumption by source and economic growth in Turkey Energy Policy, 35(11), 5447-5456 Johansen, S (1991) Estimation and hypothesis testing of cointegrating vectors in Gaussian vector autoregressive models Econometrics, 59(6), 1551-1580 Joyeux, R., & Ripple, R D (2007) Household energy consumption versus income and relative standard of living: A panel approach Energy Policy, 35(1), 50-60 Jumbe, C B L (2004) Cointegration and causality between electricity consumption and GDP: Empirical evidence from Malawi Energy Economics, 26(1), 61-68 Kraft, J., & Kraft, A (1978) On the relationship between energy and GNP Journal of Energy and Development, 3, 401403 Lee, C C (2005) Energy consumption and GDP in developing countries: A cointegrated panel analysis Energy Economics, 27(3), 415-427 Le, Q C (2011) Electricity consumption and economic growth in Vietnam: A cointegration and causality analysis Journal of Economic Development, 13(3), 24-36 Lise, W., & Monfort, K V (2007) Energy consumption and GDP in Turkey: Is there a co-integration relationship? Energy Economics, 29(6), 1166-1178 Loganathan, N., & Subramaniam, T (2010) Dynamic cointegration link between energy consumption and economic performance: Empirical evidence from Malaysia International Journal of Trade, Economics and Finance, 1(3), 261267 Masih, A M M., & Masih, R (1996) Energy consumption, real income, and temporal causality: Results from a multicountry study based on cointegration and error-correction modelling techniques Energy Economics, 18(3), 165-183 Mehrara, M (2007) Energy consumption and economic growth: The case of oil exporting countries Energy Policy, 35(5), 2939-2945 Morimoto, K., & Hope, C (2004) Impact of electricity supply on economic growth in Sri Lanka Energy Economics, 26(1), 77-85 Mozumder, P., & Marather, A (2007) Causality relationship between electricity consumption and GDP in Bangladesh Energy Policy, 35(1), 395-402 146 | Policies and Sustainable Economic Development Narayan, P K., & Smyth, R (2008) Energy consumption and real GDP in G7 countries: New evidence from panel cointegration with structural breaks Energy Economics, 30(5), 2331-2341 Nguyen, D L (2012) Energy consumption and economic development: Granger causality analysis for Vietnam Vietnam Development and Policies Research Centre(DEPOCEN), Working paper, 14 Oh, W., & Lee, K (2004) Causal relationship between energy consumption and GDP revisited: The case of Korea 19701999 Energy Economics, 26(1), 51-59 Ozturk, I (2010) A literature survey on energy-growth nexus Energy policy, 38(1), 340-349 Paul, S., & Bhattacharya, R N (2004) Causality between energy consumption and economic growth in India: A note on conflicting results Energy Economics, 26(6), 977-983 Pedroni, P (1999) Critical values for cointegration tests in heterogeneous panels with multiple regressors Oxford Bulletin of Economics and Statistics, 61(S1), 653-670 Pesaran, M H., Shin, Y., & Smith, R J (2001) Bounds testing approach to the analysis of level relationships Journal of Applied Econometrics, 16(3), 289-326 Phung, T B (2011) Energy consumption and economic growth in Vietnam: cointegration and causality analysis International Journal of Energy Economics and Policy, 1(1), 1-17 Threshold Shiu, A., & Lam, P L (2004) Electricity consumption and economic growth in China Energy Policy, 32(1), 47-54 Soni, V., Singh, S P., & Banwet, D K (2013) A cointegration and causality analysis for assessing sustainability and security in Indian energy sector Global Journal of Management and Business Research Economics and Commerce, 13(4), 31-42 Soytas, U., & Sari, R (2003) Energy consumption and GDP: Causality relationship in G-7 countries and emerging markets Energy Economics, 25(1), 33-37 Stern, D I., & Enflo, K (2013) Causality between energy and output in the long-run Energy Economics, 39, 135-146 Tang, C F., Tan, B W., & Ozturk, I (2016) Energy consumption and economic growth in Vietnam Renewable and Sustainable Energy Reviews, 54, 1506-1514 Toda, H Y., & Yamamoto, T (1995) Statistical inference in vector autoregressions with possibly integrated processes Journal of Econometrics, 66(1-2), 225-250 Wolde-Rufael, Y (2006) Electricity consumption and economic growth: A time series experience for 17 African countries Energy Policy, 34(10), 1106-1114 Yang, H.-Y (2000) A note on the causal relationship between energy and GDP in Taiwan Energy Economics, 22(3), 309-317 Yoo, S.-H (2006) The causal relationship between electricity consumption and economic growth in the ASEAN countries Energy Policy, 34(18), 3573-3582 Yu, E S H., & Choi, J Y (1985) The causal relationship between energy and GNP: An international comparison Journal of Energy Finance & Development, 10(2), 249-272 Yu, E S H., & Hwang, B.-K (1984) The relationship between energy and GNP: Further results Energy Economics, 6(3), 186-190 Yu, E S H., & Jin, J C (1992) Cointegration tests of energy consumption, income, and employment Resources and Energy, 14(3), 259-266