The Relationship of Energy Consumption, CO2 Emission And Economic Growth: An Analysis For Thailand. Montchai Pinitjitsamut

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1 The Relationship of Energy Consumption, CO2 Emission And Economic Growth: An Analysis For Thailand Montchai Pinitjitsamut 0046 Kasetsart University, Thailand The Asian Conference on Sustainability, Energy and the Environment Abstract: This paper aims to present the empirical investigation on the causal relationship among economic growth, energy consumption and CO2 emission in Thailand during the period of The study framework based on Toda and Yamamoto (1995) approach. The findings from Granger causality found that an increase in GDP will bring about an increase in energy use, but not vice versa. Energy consumption causes the carbon emission in the long run and the inverse is also true. The increasing of energy use will make more CO2 emission. Carbon emission causes economic growth in the long run, but the inverse is not true. The response of LENR to LGDP is positive increasing trend after the quarter (one lag). But, the response to LCOE is nearly constant and last long over time. There is a strong relationship between LCOE to LENR, positive increasing trend on the response of LCOE to LGDP and initially positive impacts on response of LCOE to LGDP but decreasing over time. It implies that the effect of promoting low-carbon energy would be made some hinders on economic growth in short certain period and lesser impacts for long time in Thailand. Keywords Energy consumption, Economic growth, Carbon emission, Granger causality 48

2 1. Introduction Thailand experiences the high growth rates of economic during 1993 to early 1990 until the economic recession in During this period the economic growth rates of an average 8.04% per annum was gone with considerable highly increase of energy consumption and substantial CO2 emission at rates of 12.53% and 12.46%, respectively. Contraction in growth rates occurs in two periods. First was the period of with average annum growth of -5.94%, 1.61% and -3.11% for economic, energy consumption and CO2 emission, respectively. Second was the period of with average annum growth of 0.08%, 2.70% and 1.99% for economic, energy consumption and CO2 emission, respectively However, as shown in Table 1, the period before crisis during presents the high growth rate of real GDP, energy consumption and CO2 emission. The growth rate on energy consumption is declining over time while CO2 emission become structural adjusted with lower growth rate of 2.09%, 5.04% and 2.95% for the rest three periods (see Table 1). Moreover, notice that, CO2 emission become nearly steady growing rate over the periods. Figure 1 illustrates the increasing trends of real GDP and energy consumption that seem to be quite similar. Normally, the lower growth rates of total energy consumption over this period seem to reflect the lower economic activity and carbon emission. However, in the details perspectives, as shown in Figure 1, the trend of energy consumption and economic growth are not go along over time. But, in the long run, the increasing patterns of gross domestic product and total energy consumption for Thailand for the period are quite similar and closely related to each other, but CO2 emission is slower growth than the others. Table 1. Energy consumption, CO2 emission and economic growth in Thailand. (Average annual growth rates, %) Period Real GDP 9.11% 0.64% 5.11% 3.62% Energy Consumption 10.94% 4.35% 3.45% 1.74% CO2 Emission 12.20% 2.09% 5.04% 2.95% Notes: Author s calculation Table 2. Oil import and consumption ratio (%) Period Oil Import Consumption ratio Source: Energy Policy and Planning Office (2009) and author s calculation 49

3 GDP EnCon CO2 0 Q Q2 Q3 Q4 Q Q2 Q3 Q4 Q Q2 Q3 Q4 Q Q2 Q3 Q4 Q Q2 Q3 Q4 Figure 1. Quarterly growth trends of economic, energy consumption and CO2 emission in Thailand, Note: No quarterly data before Actually, Thailand s economic has a high level of dependence on oil imports. It can be expressed from the ratio of oil import to consumption in Thailand. If the ratio present a high value, it means that the energy consumption is highly depends on oil import. Table 2 presents an average ratio in summary over the periods which are categorized in 5 years interval. This information provides us the ratio which is increasing from 54.49% in the period of to 63.45% in the period of During the period of , the ratio is increasing over time. Because the oil price in world market becomes highly volatile and the economic contracted in that period. This situation discourages oil consumption in great amount. Therefore, it reflects the higher dependence of energy consumption for economic growth in Thailand with having a linkage to carbon emission. However, the concept regarding the relationship between energy consumption and economic growth are mentioned and analyzed in many studies. For example, Akarka and Long (1979) that presents on the concept of traditional neo-classical growth model and introduces energy as an intermediate input next to the basic factors of land, labor and capital contributing to economic growth. It is necessary to identify and understand the direction pattern of the causeeffect linkage among energy consumption, carbon emission and economic growth. This would make a great benefit for Thailand s energy policy implications. Moreover, since the presence of policies following the Kyoto protocol aiming at the reduction of energy consumption, it might possibly slow economic growth because of such causal relationships or the recent surge in the fuel prices. These issues have to be investigated the real relationship among variables. This paper attempts to explore and investigate the causal relationship between energy consumption and economic growth in quarterly for Thailand for the period The empirical investigation is conducted with the application of modified approach of time series proposed by Toda and Yamamoto (1995). The empirical focus has concentrated on the 50

4 impacts on each other among three variables: economic growth, energy consumption and carbon emission. 2. Methodology The VAR and ECM is an empirical technique which is widely use to conduct the Granger causality that can examine the causal linkage and direction among variables. The VAR estimation can be conducted with first degree difference, namely I(1), of the variables. If the variables are known to be I(1) with no cointegration. It can be carried out the ECM approach. However, it needs to organize some pre-testing to identify the suitability because the ECM approach contains the bias. Moreover, another problem of this method is the basis assumption which is treated the time series as a stationary data. Therefore, this method has to be used in restriction scope and carefully to perform the estimation. Tsani (2010) expresses that the limitations of the methodologies derive from the fact that an investigation of any cointegration relationships requires integration of order one (I (1)) of the series and any inference regarding relationship among variables is conditional on this assumption. If the series is not integrated of order I(1) or is integrated in different orders no test for long-run relationship is employed. On the other hand employment of unit root and cointegration tests may suffer from low power against the alternative therefore they can be misplaced and may suffer from pre-testing bias 1. This paper applies the approach which is described in Toda and Yamamoto (1995). The major approach in this method is a modified Granger causality test to adjust the limitation of previous studies. In this study, it is an attempt to overcome the limitations of previous studies examining causal relationships between energy consumption, CO2 emission and economic growth and, then, to apply for the case of Thailand. Furthermore, this approach applies a revised Wald test on the parameters of the VAR(k), where k is the lag length of the VAR system. The correction is performed on order of the VAR system by adding the maximal integration order (d max ) into the model. Then, the VAR(k+d max ) is estimated with the coefficients of the last lagged d-max vector being ignored. Toda and Yamamoto (1995) affirms that the Wald statistic converges in distribution to an Chi-square random variable with degrees of freedom equal to the number of the excluded lagged variables regardless of whether the process is stationary, possible around a linear trend or whether it is cointegrated. Also, by using the approach, Zhang et al. (2009) explains that this approach does not require pre-testing in order to determine the cointegrating properties of the system hence overcoming potential unit root and cointegration test bias. For this paper, the original VAR system model can be expressed, as follow. V t k k dmax 0 ivt i jvt j t i 1 j k 1 (1) where, α is a vector of constant, βt is coefficient matrix, and εt is white noise residuals. Where V i is a vector of estimated variables consist of economic growth (LGDP), final energy consumption (LENR) and CO2 emission quantity (LCOE). All variables are in natural 1 Zhang et al. (2009) 51

5 logarithm. These three variables are arranged in three pairs in VAR system to create investigating models. For example, to investigate the relationship of LGDP and LENR, the pair of equation can be derived and formed a model as follow, k k dmax k k dmax 0 1 ilgdpt i 2 jlgdpt j 1 ilenrt i 2 jlenrt j 1 t i 1 j k 1 i 1 j k 1 LGDP (2) t LENR t k k dmax k k dmax 0 1 ilenrt i 2 jlenrt j 1 ilgdpt i 2 jlgdpt j 2t i 1 j k 1 i 1 j k 1 (3) As shown in Eq. (2), Granger causality analysis will be performed on LGDP t to LENR t for γ 1i 0. On the other hand, in Eq. (3), Granger causality have to conducted from LENR t for all i and δ 1i 0. After that, the others two pairs of equation, LENR_LCOE and LCOE-LGDP, are estimated in the same way as the example. Toda and Yamamoto (1995) procedure is used to examine the relationship among income, energy and environment aspects in Thailand. The steps are as follows 2 : (i) Find the maximal order of integration d of variables by conducting unit root tests. (ii) Identify the optimum lag length k of a VAR by several criteria such as AIC, SIC, HQ. (iii) Estimate the lag-augmented VAR (k+d max ) model: (iv) Check robustness of augment VAR (k+ d max ) by diagnostic tests. (v) A Wald test is conducted on the first k parameters instead of on all parameters in the augment VAR (k+d max ) model and the statistics follows an asymptotic Chi-square distribution with k degrees of freedom Data and unit root tests This study uses quarterly data on real GDP (Y) (constant price of 1988 and local Thai-Baht), final energy consumption (E) converting to oil equivalent; barrel per day and CO2 emission from energy consumption (kilo-tons). All data covering the period of 1993 to 2010 and are used in natural logarithm. In order to investigate the existence of the Granger causality between energy consumption, CO2 emission and real GDP the first step should be the identification of the order of integration of the series under consideration (d max ). For this purpose a set of different unit root tests is employed. In order to derive the order of integration the Dickey and Fuller (1981) test (ADF) and the Phillips and Perron (1988) test (PPerron) are employed. The unit root tests are necessary to be carried before conducting the estimation with Toda and Yamamoto (1995) method. The purpose is to obtain the maximum of integration order (d max ) in each variable. This study conducts three different unit root tests: augmented Dickey Fuller (ADF), Phillips Perron (PP), and Kwiatkowski Phillips Schmidt Shin (KPSS). All tests are 2 Zhang et al. (2009) 3 For more details on Wald statistics, see Toda and Yamamoto, 1995; Zapata and Rambaldi,

6 defined on the basis null hypothesis that I(1) has a unit root, and the results present mostly the rejecting null hypothesis at 1% statistical significant, except LCOE on intercept and trend. Therefore, the results of unit root tests reported in Table 3 which indicated that the integration orders of all variables do not appear to be larger than 1, so d max is defined as 1. Table 3. Results of unit root test Variable ADF test PP test KPSS test In In + Tr In In + Tr In In + Tr Levels LGDP (5) (5) a c LENR (0) (0) a b LCOE (6) (6) c a First differences LGDP a (4) b (4) a a LENR a (0) a (0) a a LCOE a (5) b (5) a a Second differences LGDP a (3) a (3) a a LENR a (3) a (3) a a b LCOE a (2) a (2) a a Note: 1. In means intercept, and In+Tr means intercept and trend. 2. Superscript a, b and c denote as a significant level of 1%, 5% and 10%, respectively. 3. For ADF test the lags are based on the Schwarz information criteria (SIC) with maximal lag = 11. The critical values for the ADF test t statistics are from MacKinnon (1991). 4. PP test with automatic lag selection based non Newey-West (1994), lags=3 and the critical values for the KPSS test are from Kwiatkowski et al. (1992). 4. Empirical Findings Table 4 presents the correlation among real GDP, energy consumption and economic growth in Thailand. The finding suggests that developments in economic growth, energy consumption and CO2 emission patterns are strongly correlated. All correlation coefficients are positive and indicate the same direction adjustment among these three variables. By comparing on coefficients value, the energy consumption has greater impact to CO2 emission than the economic growth. However, both economic growth and energy consumption greatly activate to CO2 emission in Thailand. Moreover, the economic growth and energy consumption are also highly correlated each other, even though lesser than theirs relationship to CO2 emission. Regarding the information presented, the correlation is just illustrated the natural pattern of causal relationship among variables. Despite the lack of causal direction, the correlation provides as preliminary information and leads as a guideline for identifying causal direction. Table 4. Real GDP, Energy consumption and CO2 emission Correlation matrix LGDP LENR LCOE LGDP 1 53

7 LENR LCOE Causality tests In Toda and Yamamoto (1995) approach, firstly, it needs to determine the optimal lag length. The several tests are conducted for identifying the optimum lag length such as Akaike Information criterion (AIC), Schwarz Information Criterion (SIC) and Hanna-Quinn information criterion (HQ) and the results are shown in Appendix. For the first pair of equation, LGDP-LENR, the optimal lag length is pointed out by AIC and FPE equal to 7, LR and HQ suggests a lag length of 6, while the optimum lag length of VAR is 3 via SIC. Then, the diagnosis tests on augmented model, VAR (k+d max ), are conducted to identify the stability condition. The diagnosis test is performed through the inverse roots of the characteristics autoregressive polynomial. The estimated VAR is stable (stationary) if all roots have modulus less than one and lie inside the unit circle. If the VAR is not stable, certain results (such as impulse response standard errors) are not valid. The diagnostic test results indicate the augmented VAR (4) (k+d max =4) is not stable. But the results of VAR (7) (k+d max =7) and VAR (8) (k+d max =8) are stable. However, as suggested by Stock (1994) in the case of conflicting results between the different tests, AIC is preferred above other tests. Therefore, in the first pair of equations, the VAR (8) (k+d max =8) is chosen to analyze in the next process. Similarly, the second pair of equations is carried out in the same way. The LENR-LCOE optimal lag length is pointed out by AIC, LR and FPE equal to 7, while the optimum lag length of VAR is 3 via SIC and HQ. The diagnostic test results indicate the augmented VAR (4) (k+d max =4) is stable. Unfortunately, the results of VAR (8) (k+d max =8) is not stable. Therefore, the VAR (4) (k+d max =4) is chosen to analyze in the next process. For the third pair of equations, the LCOE-LGDP optimal lag length is pointed out by AIC and FPE equal to 7, while the optimum lag length of VAR is 5 via SIC, HQ and LR. The diagnostic test results indicate both augmented VAR (6) (k+d max =6) and VAR (8) (k+d max =8) are stable. Therefore, the VAR (8) (k+d max =8) is chosen to analyze in the next process. Table 5. Granger causality test From To F statistics Lag p-value Causality GDP ENR GDP --> ENR * ENR GDP 1, No COE ENR COE --> ENR *** ENR COE ENR --> COE *** GDP COE No COE GDP COE --> GDP ** Note: 1. ***, ** and * denotes as a significant level of 1%, 5% and 10%, respectively. 54

8 Results in Table 5 show that there is a unidirectional Granger causality running from real GDP to energy consumption at the 10% significant level in Thailand. That is, an increase in GDP will bring about an increase in energy use, but not vice versa. Therefore, Thai government can foster conservative energy policy in the long run without retarding economic growth. This result is the same as Zhang et al (2009), but differs from the study of Yuan et al (2008). The second finding in Granger causality is that energy consumption causes the carbon emission in the long run and the inverse is also true in Thailand. This is a reinforcing loop between energy using and carbon emission in Thailand. The more CO2 emission that makes unfavorable condition of environment will initiate the higher energy consumption. Also, the increasing of energy use will make more CO2 emission. Thai government may have a policy to encourage the using of low-carbon energy that means to impede high-carbon energy consumption and discourage CO2 emission. Another important finding in Granger causality is that economic growth not directly causes the carbon emission in the long run. However, the inverse is true in Thailand. It can be explained that the general economic activities in Thailand are still inefficient in term of carbon generating and emission. Also, people may have an absence of environment awareness and concern Impulse response Figure 2 shows the impulse response of the variables which represent a time of adjustment for each variable. Each curve presents the shock occurring on one variable impacts to another variables and illustrate how long the effect lasts in short run. The shock in one of three variables (LGDP, LENR and LCOE) mostly has positive initial impacts on the others, except response of LENR to LGDP. The response of LENR to LGDP is initially minimal negative but in positive increasing trend after the quarter (one lag). But, the response to LCOE is nearly constant and last long over time. Furthermore, the response of LCOE to LENR is on decreasing trend over time. It also presents that the impact on LENR shock having positively last long for less than one year. This reflects a very closely relationship between LENR and LCOE. Another important finding is the positive increasing trend on the response of LCOE to LGDP. Even though the LCOE has statistical insignificant cause to LGDP, the response curve illustrates the linkage between carbon emission from consumption activities impact to economic growth. Lastly, the response curve of LGDP to LENR which represents an adjustment of economic growth to a shock of energy consumption is died off over horizontal after 10 lags or 30 months. Notice that, the response starts with positive value at the first time, increasing for 6 months and declining for another 6 months. It reflects the length of one year on positive impact of energy consumption shock on economic growth. After that the negative response occurs and last long for about 18 months. Another important finding is that a response of GDP (LGDP) on shock of carbon emission (LCOE) has initially positive impacts on economic growth but decreasing over time at the end. All of these results of impulse response analysis support that of Granger causality tests. It is interesting to note that impacts of carbon emission shocks on economic growth are decreased over the horizons, although initial impacts are greatly positive. It implies that the 55

9 effect of promoting low-carbon energy would be made some hinders on economic growth in short certain period and lesser impacts for long time in Thailand. Response of LENR to LG DP R e s p o n s e o f L E N R t o L C O E R e s p o n s e o f L C O E t o L E N R R e sponse o f LCOE to LG DP Resp onse o f LG D P to LENR.06 R e s p o n s e o f L G D P t o L C O E Figure 2. Impulse response function for economic growth, energy consumption and CO2 emission in Thailand. The Wald statistic is conducted for lag exclusion tests in each lag in the VAR. For each lag, the Wald statistic for the joint significance of all endogenous variables at that lag is reported for each equation separately and jointly. For LGDP-LENR equation, the findings summarizes that the only lag no. be excluded from the model are lag 3 and lag 6 with significant level of 10%. For LCOE-LENR equation, the findings summarizes that there is only lag no. 2 being excluded from the model with significant level of 10%. Finally, For LCOE-LGDP equation, the findings summarized that the only lag no.2, 3, 7 be excluded from the model with significant level of 10%. 56

10 5. Conclusion and policy implications This paper investigates the temporal linkages among GDP, energy consumption, and carbon emissions Thailand during the period in a multivariate model. By using the Toda and Yamamoto (1995) model and quarterly data, the empirical findings on Granger causality illustrates that the changes in economic growth will directly impact to energy consumption, but not in the contrary. Energy consumption positively causes the carbon emission in the long run and the inverse is also true in Thailand. The findings present the reinforcing mechanism on energy consumption and carbon emission which initiate unfavorable environment condition that, in turn, make greater energy consumption. Carbon emission has a positive linkage to economic growth in the long run. However, the economic growth does not encourage to CO2 emission in Thailand. It can be explained that economic activities in Thailand are mostly inefficient use of energy and make large amount of carbon emission, particularly in the long run. The findings of impulse response functions present that the shock on increasing amount on energy consumption impacts to GDP by decreasing in minimal at the beginning one quarter and positively increase after that. The shock on energy consumption to carbon emission is nearly constant and last long over time. However, in the opposite, the response of carbon emission to energy consumption is on decreasing trend over time. It reflects a close relationship between energy consumption and carbon emission. There is a positive linkage between carbon emissions from consumption activities impact to economic growth. The adjustment of economic growth to a shock of energy consumption is died off over horizontal after 30 months and on relationship in the long run. The impacts of carbon emission shocks on economic growth are decreased in the long run. It implies that the effect of promoting low-carbon energy would be made some hinders on economic growth in short certain period and lesser impacts for long time in Thailand. Also, the increasing of energy use will make more CO2 emission. Thai government may have a policy to encourage the using of low-carbon energy that means to impede high-carbon energy consumption and discourage CO2 emission. The dominant source of carbon emission is energy consumption activities not economic activities. Policies diversifying energy source may be applied to reduce the reliance on fuel that normally in consumption activities such as oil. Thailand should promote to increase the utilization of clean energy fuel such as wind, solar and bio energy. 6. Reference - Ang, J.B., (2008). Economic development, pollutant emissions and energy consumption in Malaysia. Journal of Policy Modeling 30, Asafu-Adjaye, J., (2000). The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries. Energy Economics 22, Huang, B.N., Hwang, M.J. and Yang, C.W., (2008). Causal relationship between energy consumption and GDP growth revisited: a dynamic panel data approach. Ecological Economics 67, Lee, C.C., (2005). Energy consumption and GDP in developing countries: a cointegrated panel analysis. Energy Economics 27,

11 - Perron, P., (1989). The great crash, the oil price shock and the unit root hypothesis. Econometrica 57, Phillips, P.C.B. (1995). Fully modified least squares and vector autoregression. Econometrica 63, Tsani, Stela Z. (2010). Energy consumption and economic growth: A causality analysis for Greece, Energy Economics 32, Toda, H.Y., and Phillips, P.C.B., (1993). Vector autoregressions and causality. Econometrica 61, Toda, H.Y. and Yamamoto, T., (1995). Statistical inference in vector autoregressions with possibly integrated processes. Journal of Econometrics 66, Yuan, J.H., Kang, J.G., Zhao, C.H., Hu and Z.G., (2008). Energy consumption and economic growth: evidence from China at both aggregated and disaggregated levels. Energy Economics 30, Zapata, H.O. and Rambaldi, A.N., (1997). Monte Carlo evidence on cointegration and causation. Oxford Bulletin of Economics and Statistics 59, Zhang, X.P. and Cheng, X.M. (2009) Energy consumption, carbon emission and economic growth in China. Ecological Economics 68, Appendix Table A1. VAR lag order selection criteria: LGDP and LENR Lag LogL LR FPE AIC SC HQ NA E E E * E E * * 1.42E e-06* * E E Note: * represents lag order selected by the criterion with choosing the maximum lag length of 9. Table A2. VAR lag order selection criteria: LCOE and LENR Lag LogL LR FPE AIC SC HQ NA 5.01E E E * E * E E E * 1.76e-06* * E E Note: * represents lag order selected by the criterion with choosing the maximum lag length of 9. Table A3. VAR lag order selection criteria: LGDP and LCOE 58

12 Lag LogL LR FPE AIC SC HQ NA 7.01E E E E E * * 4.13E * E e-07* * E E Note: * represents lag order selected by the criterion with choosing the maximum lag length of 9. 59

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