Causality Relationship between Renewable and Non ...

Economics and Finance in Indonesia Vol. 59 (1), Page 1 – 18

Causality Relationship between Renewable and Non-Renewable Energy Consumption and GDP in Indonesia * Jauhary Arifin Normansyah Syahruddin

Abstract Recent contributions show that the world is facing serious problems with energy depletion as a result of the unbalanced availability between finite energy resources and population growth as well as industrial growth. The available amount of finite-based energy resources was predicted to last between 30-150 years (World Resource Institute 2007). Responding to that threat, an everexpanding research has been conducted on energy consumption and renewable energy resources, leading to a large literature on this research area. Research on the causal relationship between energy consumption and GDP has been a well established topic in the energy economics literature, yet the topic still remains debatable (Dhungel 2008). In the case of Indonesian economy, some studies have shown different results on the casual relationships between energy consumption and GDP and are mainly focusing on non-renewable energy. This paper tests the causality relationship between renewable and non-renewable energy consumption and GDP in Indonesia by applying the Toda-Yamamoto procedure as well as the Engle-Granger procedure. Two proxies of renewable energy consumption are used in this study. Granger causality is found to run only from renewable electricity consumption per capita to GDP per capita. The last part of this paper discusses the policy implications from our findings. Keywords: Renewable Energy; Non-Renewable Energy; Real GDP; Granger Causality; Indonesia JEL classification: C32; Q43. *

We thank Oetomo Tri Winarno from Institut Teknologi Bandung for lending us his electricity data compiled from the Statistics Book of Electricity and Energy of the Directorate General of Electricity and Energy Utilization. We also appreciate helpful comments from the editors of this journal. Arifin acknowledges financial support from the University of Verona, Italy (PhD scholarship, CooperINT 2008, and funding from Scuola di dottorato di Economia). Syahruddin acknowledges financial support from the University of Bergamo, Italy (PhD scholarship). Any opinions, findings, or conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of any institutions including the Ministry of Agriculture, Republic of Indonesia.

© 2011 LPEM

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Jauhary Arifin Normansyah Syahruddin

1. INTRODUCTION Recent contributions have shown that the world is facing serious problems with energy depletion as a result of the unbalanced availability between finite energy resources and population growth as well as industrial development. The available amount of finite-based energy resources was predicted to last between 30-150 years (World Resource Institute 2007). This situation also hindered the economic development in most developing countries in the world. Not only the availability became the immediate concern, but also the environmental degradation whereas oil and coal exploitation eventually led to forest destruction, biodiversity extinction and natural disasters (i.e. the Lapindo mud disaster in East Java). This type of energy use was also vulnerable to disruptions caused by major events in the world, such as war, monopolistic behaviors (e.g. by OPEC) and often very much depending on the political stability of the producing countries (Huntington 2009) 1 . Responding to these complexities, many countries have started to utilize energy which is produced from renewable resources. Different types of renewable energy sources, such as solar, wind, water, geothermal, and biomass have been used, mostly by developed countries, to meet the demand of energy, whereas non-renewable energy availability are expected to be scarce by 2050. This will later contribute to the achievement of the Millennium Development Goals (MDGs), stating that energy availability and energy access play a vital role in poverty eradication, providing aid to universal primary education, promoting gender equality, and improving health as well as reducing child mortality (United Nation 2005). With the growing interest in using renewable energy, it is natural to ask a fundamental question, namely whether this development causes economic growth, is caused by economic growth, whether both of the two are true, or none of them is true? Responding to this question leads us to a well-established topic in energy economics which focuses on the investigation of the causal relationship between energy consumption and economic growth, although the topic still remains debatable (Dhungel 2008). In this paper, we aim to investigate the causal relationship between renewable and non-renewable energy consumption and economic growth in Indonesia.

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Further, Huntington (2009) states that a principal difference over the last 10 years has been the spread of risks of oil supply interruption beyond the Persian Gulf region. A later study covering a wider area, including Russia, the states surrounding the Caspian Sea,, Nigeria, Angola, Venezuela, and Mexico, concluded that each of these countries could potentially experience political problems that would make its oil supplies vulnerable.

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Causality Relationship between Renewable and Non-Renewable Energy Consumption and GDP in Indonesia

This paper consists of six sections. In the introduction we discuss the background of our proposed research. In the next two sections, we describe some results from similar studies and provide an overview of energy utilization in Indonesia. The fourth section explains the data and methodology used in this study, and the fifth section discusses our empirical findings. Lastly, we derive conclusions and possible policy implications from our research.

2. A BRIEF LITERATURE SURVEY Numerous researches have studied the causal relationship between energy consumption and economic growth (i.e. Ghosh 2002; Hondroyiannis et. al. 2002; Lee 2005; Francis et. al. 2007; Mozumder and Marathe 2007). The topic has also been recently surveyed by Ozturk (2010) and Payne (2010). Although the topic seems to be the trend in the literature, Payne (2010) points out that there is still no consensus concerning the causal relationship between energy consumption and GDP for a particular country. In the same paper, he summarizes four testable hypotheses of the causal relationship between energy consumption and economic growth that are generally used by researchers working on the topic. First, the “growth hypothesis” which proposes that energy consumption causes economic growth both directly and as a complement to other inputs in the production process. The “conservation hypothesis” suggests that energy conservation policies may not adversely affect real GDP growth. The “neutrality hypothesis” asserts that energy consumption does not have a significant causality on economic growth. The last hypothesis is the “feedback hypothesis” that implies interdependence between energy consumption and economic growth. Earlier studies on the causality relationship between total energy consumption and its contribution to economic growth in Indonesia have provided us with different results. Using GDP growth, commercial energy use per capita, and energy prices as the variables, Asafu-Adjaye (2000) shows that, in the short run, there is a uni-directional Granger causality running from energy consumption to income. The same view is also shared by Squalli (2007) who found a causality relationship running from electricity consumption to GDP in Indonesia. These findings corroborate the view that, for Indonesia, there is a short-run neutrality between energy consumption and income, similar to Fatai et. al. (2004), while Soytas and Sari (2003) pointed out that there is no cointegration between energy and income. In a further study, Sari and Soytas (2007) found that energy is an important input factor of production (in terms of explaining the forecast error variance of income growth) compared to labour and capital. Another result was provided by Masih and Masih (1996) which stated that causality runs from income to energy consumption. This later result is also supported by Yoo (2006) who found a uni-directional causality running from economic growth to electricity consumption.

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Despite the contributions to the topic, previous studies only focused on the non-renewable energy consumption causality towards national income or GDP. In a different study, Sadorsky (2009) presented the causality between renewable energy consumption and income in several emerging markets, including Indonesia. The result shows that in the short run, renewable energy consumption does not impact the current change in real income whereas in long run real per capita income does influence real per capita renewable energy consumption through the error correction term. This implies that the authorities have to be careful in constructing and implementing policies in renewable energy and do not only focus on short run gains but also on the long run perspective. Our work differs from previous studies in some respects: 1) we provide a perspective based on two types of energy sources in Indonesia, namely renewable and non-renewable energy sources; 2) we use two proxies of renewable energy consumption with a longer data coverage. Renewable energy sources refer to non-depletable energy sources, while non-renewable energy sources refer to depletable energy sources (Medlock III 2009). Gas, oil and coal are included in non-renewable energy sources, while biomass, wind, geothermal, solar, hydro and tidal waves are considered as renewable energy sources.

3. A BRIEF OVERVIEW OF INDONESIAN ENERGY UTILIZATION Indonesia was well known as an oil exporter in the 1980s until the late 1990s. Eventually, the oil reserves were not sufficient for meeting all demands of all the stakeholders. The production of oil was recorded at the highest point during 1980-1981, reaching 1.7 million barrels per day, while the production of natural gas reached its peak during 1995-1996 with a production of 3159.6 billion cubic feet. Figure 1 Production and Consumption of Oil and Gas in Indonesia

Source: Energy Information Agency (2011)

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Figure 1 shows that the production of oil in Indonesia has shown a declining trend, while consumption grew steadily over time. This development has transformed Indonesia from an oil exporter to a net oil importer. The figure also shows how oil became an irreplaceable commodity in various industrial sectors which needs serious attention from the government as the stock of oil is expected to plummet in the not too distance future. A different picture is provided by natural gas, where consumption is still far below the production level, thus enabling Indonesia to become a net exporter of natural gas. Figure 2 Share of energy utilization in Indonesia Comb. Renew. & waste 26.7%

Hydro 0.5% Gas 16.1%

Geothermal/solar/wind 7.2%

Coal/peat 18.7% Oil 30.9%

Source: International Energy Agency (2008)

To date, Indonesia’s energy utilization is still highly dependent on non-renewable energy resources, while the renewable energy resources account for less than 40% of the total energy consumption (Figure 2). The largest portion of the renewable energy belongs to the combination of renewable energy and waste with 26.7% followed by geothermal, solar, and wind energies with a 7.2% share. Hence, the utilization of renewable energy is still limited while the raw materials are abundant. This situation should be changed before the resources of nonrenewable energy become extinct. Exploitation of renewable energy can lead to a better industrial performance (i.e. the inexhaustable raw materials will stimulate the performance of energy industries and eventually becomes the answer to the energy shortage 2 ), social performance (i.e. utilization of renewable energy will absorb a

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See Matos and Hall (2007) and Nardin and Catanzaro (2007).

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considerable amount of different renewable energy sources and will absorb considerable amount of workforce 3 ) and even environmental performance as renewable energy can reduce the amount of CO 2 released to the atmosphere.

4. DATA AND METHODOLOGY In this work, we consider the approach by Bowden and Payne (2010) and Vaona (2010) that measure the causality between the GDP and energy consumption disaggregated by the type of energy. By this approach, we want to understand the phenomenon of the growth of GDP and energy consumption in a holistic view which is not limited to a certain type of energy.

4.1. Data Description We explore four variables in this study: the real GDP measured in 2000 price, non-renewable energy consumption, and two proxies of renewable energy consumption. The sample covers the period 1971-2008. The choice of the starting year was constrained by the availability of the data. The proxies for renewable energy consumption are non-fossil fuel energy consumption and electricity production generated from renewable energy power plants. GDP growth, fossil and non-fossil fuel energy consumption data were obtained from the World Development Indicators (WDI) of the World Bank. The non-fossil fuel energy consumption data was calculated by taking the difference between the total energy use and total fossil fuel energy consumption. The data on total electricity production from renewable energy power plants was calculated by taking the sum of the total electricity generated from hydro, geothermal, and wind power plants and collected from various publications of the Handbook of Energy & Economic Statistics of Indonesia of the Ministry of Energy and Mineral Resources and the Statistics Book of Electricity and Energy of the Directorate General of Electricity and Energy Utilization. All data were then divided with the total number of populations to represent the per capita terms and transformed to natural logarithms. Table 1 presents some summary statistics of the data in the original unit of measurement.

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A research by Thornley et al (2008) shows that the level of employment in the energy plant, that used biomass as the input of energy, is higher compare to the other power plant that used conventional non-renewable energy. This study also explained that higher level of employment is not only in the energy plant, but also along the supply chain of the biomass and equipment section of the supply chain.

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Table 1. Summary statistics of the variables (per capita, 1971-2008) GDP

NRE

RE_total

RE_electr.

Mean

619.07

315.0

251.1

39.1

Minimum

246.54

74.0

214.0

6.0

Median Maximum Std. Dev. CAGR Number of obs.

594.66

299.2

248.4

38.0

1,087.57

580.5

300.2

87.5

251.08

163.1

34.1

28.8

4.2%

5.7%

0.7%

7.4%

38

38

38

38

Notes: GDP is in US$, measured in 2000 price. NRE is the non-renewable energy consumption, measured in kg of oil equivalent. RE_total is the non-fossil fuel energy consumption, measured in kg of oil equivalent. RE_electr. is the electricity production generated from renewable energy power plants, measured in kilowatt hour.

4.2. Methodology The Granger (1969) test has been traditionally used in the literature to investigate the causality relationship between two variables. This test states that if the values of a variable x t significantly contribute to predict the values of another variable y t , then x t is said to Granger cause x t and vice versa. When performing the Granger causality test from x t to y t , the test is performed under the null hypothesis that x t does not Granger cause y t versus the alternative hypothesis that x t Granger causes y t . Moreover, the causality relationship is said to be bi-directional if x t causes y t and y t causes x t , uni-directional if one of the direction is true, and neutral if none of them are true. In the development of the testing methodology, Engle and Granger (1987) proposed a strategy that involves pre-testing the order of integration of both variables and the cointegration between them. Unfortunately, this strategy may suffer from pretest biases and therefore the results would be unreliable (Zapata and Rambaldi 1997). To overcome these issues, Toda and Yamamoto (1995) proposed a new procedure based on lag-augmented vector autoregressive (VAR) model with an integrated process. The main objective of this study is to investigate the Granger causality relationship between renewable and non-renewable energy consumption per capita and GDP per capita. Like in Tsani (2010) and Vaona (2010), in this study, we use a bivariate VAR model and apply the Granger causality test based on the Toda-Yamamoto procedure. As a check, we also run the Granger causality test between the variables by employing the Engle-Granger procedure of Engle and Granger (1987).

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Toda-Yamamoto procedure Toda and Yamamoto (1995) has proposed a procedure to perform a Granger causality test between two variables without performing a preliminary cointegration test, as opposed to the Engle-Granger procedure. This way, the Toda-Yamamoto has advantages that it can avoid some possible pretest biases which may occur when using the standard procedure (Toda and Yamamoto 1995) and shows an excellent performance in terms of size stability (Yamada and Toda 1998). To explain the Toda-Yamamoto procedure, let us first consider a bivariate VAR model of two variables X t and Y t , Yt   1 

Xt   2 

m

n

m

n

i 1

j  m 1

i 1

j m 1

m

n

m

n

i 1

j  m 1

i 1

j  m 1

  1, i Yt  i    1, i Yt  i    1, i X t  i    1, i X t  i   1, t

  2, i X t  i    2, j X t  j    2, i Yt  i    2, j Yt  j   2, t

(1) (2)

where α, β, and γ are parameters to be estimated, and ε are the residual terms. The variable with subscript t denotes the value of the variables at time t. The number of lags m and n are to be determined by following the subsequent analysis. Later in this paper, the variable Y t is the log of GDP and the variable X t is the log of each three types of energy consumption. Hence, we will have to analyze three bivariate VAR models and six directions of the Granger causality. The procedure for Granger causality test of the Toda-Yamamoto is described as follows. We first check the order of integration of each variable. The order of integration of a time series can be analyzed by performing an Augmented Dickey-Fuller (ADF) test (Dickey and Fuller 1979, 1981) at the level, first differenced, or second order differenced data. If a series is non-stationary and must be differenced d times before it becomes stationary, then the series is said to be integrated of order d, or denoted by I(d). For now, let us assume that the highest order of integration between the two variables is d max . The next step of the procedure is to choose the optimal number of lags m of equation (1) and (2) based on the Schwarz Information Criteria. In this step, we do not consider the parameters with the subscript j on the equation 1 and 2. At this stage, we also check for the autocorrelation of the residuals using the Portmanteau test. If autocorrelation is suspected, we increase the number of lags until autocorrelation is not present in the residuals. We then estimate the VAR model of equation (1) and (2) using the number of lags n equals to m+d max . The modified Wald test is then performed to test the joint significance of the first m estimated coefficients

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of γ 1 in equation (1) and the first m estimated coefficients of γ 2 in equation (2). If the first m lags of X variables in equation (1) (Y variables in equation (2)) are jointly equal to zero, then we can conclude that X does not Granger cause Y (Y does not Granger cause X). Cointegration and Granger causality Engle and Granger (1987) has proposed a procedure to test the Granger causality by incorporating the concept of cointegration. The concept of cointegration can be related to the concept of long-run equilibrium, where two variables are said to be cointegrated if they share a common trend. The test of cointegration relationship between two or more variables can be performed by testing the order of integration of each variables and then finding a cointegrating vector that stationarizes the linear combination betwen the variables. The implication of cointegration on testing the Granger causality has been pointed out by Granger (1986) who argued that if two variables, X t and Y t , are I(1) and cointegrated, then there must be Granger causality in at least one direction either uni-directional or bi-directional. To establish the direction of the Granger causality when the series are cointegrated, we can use the vector error-correction model. But if the variables of X t and Y t are I(1) but not cointegrated, as is the case in this paper, Toda and Phillips (1993) argued that the Granger causality using the VAR model of the first-differenced data is likely to have higher power in finite samples. The VAR model of the firstdifferenced data can be written as the following: p

p

i 1

i 1

p

p

i 1

i 1

Yt  1   1, i Yt  i   1, i X t  i  1, t X t   2 

  2, i Xt  i   2, i Yt  i   2, t

(3) (4)

where φ, θ, and ϕ are the parameters to be estimated, η is the residual term, and Δ denotes the first differenced of the variable. The number of lags p is chosen using the Schwarz Information Criteria. To test the Granger causality, we need to estimate the equation (3) and (4) and use the Wald test to test the joint significance of the p estimated coefficients of ϕ 1 in equation (3) and the p estimated coefficients of ϕ 2 in equation (4). If the p lags of ΔX variables in equation (3) (ΔY variables in equation (4)) are jointly equal to zero, then we can conclude that X does not Granger cause Y (Y does not Granger cause X).

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5. EMPIRICAL RESULTS In order to investigate the Granger causality relationship using the TodaYamamoto procedure, we first identified the order of integration of each variable by applying the ADF test both at the level and first-differenced data. The results of the ADF unit root tests are presented in Table 2. The results exhibit that all variables at level data are non-stationary at the 95% levels of significance. The results of the ADF test on the first differenced data show that all variables are significantly stationary. Hence, we concluded that all variables are integrated of order one, I(1). Based on these results, we are able to determine the highest order of integration is equal to one (d max = 1), for all pairs between GDP and the three types of energy consumption. Table 2. Results of ADF tests of the variables ADF Intercept ln(GDP)

ln(NRE)

ln(RE_total)

ln(RE_electr.)

Trend + Intercept

Level

-1.39 (2)

-1.94 (1)

first difference

-3.73 (1) **

-3.88 (1) **

Level

-2.72 (1) *

-1.49 (1)

first difference

-4.53 (0) ***

-5.38 (0) ***

Level

-0.21 (1)

-2.34 (1)

first difference

-5.75 (0) ***

-5.77 (0) ***

Level

-0.88 (1)

-1.68 (1)

first difference

-5.57 (0) ***

-5.53 (0) ***

Notes: ***, **, * denote the levels of significance at 1%, 5%, and 10% respectively. The number in parentheses denotes the number of lag for the ADF test.

We then checked the optimal number of lags m of equation (1) and (2) for each pair between energy consumption and GDP using the Schwarz Information Criteria. The optimal number of lags is three for the VAR model between ln(NRE) and ln(GDP), one between ln(RE_total) and ln(GDP), and two between ln(RE_electr) and ln(GDP). We then augmented the optimal number of lags by one, as the highest order of integration of the variables is one, and estimated the VAR model of equation (1) and (2). The modified Wald test is then performed on the first m estimated coefficients ignoring the last d max lagged coefficients.

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The results of the Granger causality test following the TodaYamamoto procedure are reported in Table 3. The first and second column are the variables of energy consumption distinguished by the type of energy (E) and the corresponding VAR models used. The third and fourth column respectively report the modified Wald test with null hypothesis of “GDP does not Granger cause E” and “E does not Granger cause GDP”. From the results in Table 3, a significant evidence of Granger causality is found to run only from renewable electricity consumption to GDP. The null hypothesis of “renewable electricity consumption does not Granger cause GDP” is rejected at the one percent level of significance. Or, in another word, we significantly accept the hypothesis that renewable electricity consumption Granger causes GDP. The causality test statistics of the opposite direction (GDP to renewable electricity consumption) shows insignificant value even at the 10 percent levels of significance. Hence, we cannot reject the null hypothesis of “GDP does not Granger cause the renewable electricity consumption”, or, in another word, GDP does not Granger cause renewable electricity consumption. Table 3. Granger causality test (Toda-Yamamoto Procedure) Type of Energy (E)

Model Specification

GDP causes E

E causes GDP

Conclusion

ln(NRE)

VAR (4)

0.52

1.04

Neutral

ln(RE_total)

VAR (2)

2.19

0.75

Neutral

ln(RE_electr.)

VAR (3)

1.21

12.00 ***

RE_electr.  GDP

Notes: ***, **, * denote the levels of significance at 1%, 5%, and 10% respectively.

The test statistic of Granger causality on other pairs (GDP to nonrenewable energy consumption, non-renewable energy consumption to GDP, GDP to non-fossil fuel energy consumption, non-fossil fuel energy consumption to GDP) are all insignificant even at the 10 percent levels of significance. Hence, we do not find significant evidence of Granger causality between non-renewable energy consumption and GDP and between non-fossil fuel energy consumption and GDP. As a complement, we performed the Granger causality test based on the Engle-Granger procedure. As previous analysis found that all variables are I(1), we could proceed to test the cointegration relationship between each energy consumption variables and the GDP at the level data using the Johansen’s (Johansen 1988, Johansen and Juselius 1990) cointegration test. The results of the Johansen test based on Trace statistic and Maximum Eigenvalue statistics are presented in Table 4 with the five percent critical value reported at the bottom of the table. As can be seen

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in Table 4, all test statistic of none cointegrating equation of all variables are less than the corresponding critical value. Hence, we do not find significant evidence of cointegration relationship between the GDP and any of the three types of energy consumption.

Table 4. Results of Johansen test for cointegration relationships between GDP and each variable (intercept, no trend) Variable ln(NRE)

ln(RE_total)

ln(RE_electr.)

Hypothesized No. of CE(s)

Trace

Max. Eigen.

None

14.22

10.86

At most 1

3.36

3.36

None

10.70

9.69

At most 1

1.01

1.01

None

14.69

12.84

At most 1

1.86

1.86

Notes: CE(s) is the abbreviation for cointegrating equation(s), Max. Eigen. denotes maximum eigenvalue statistic and Trace is trace statistic of the Johansen cointegration test. The 5% critical value of the trace statistic of none and at most 1 cointegrating equation are 15.41 and 3.76 respectively. The 5% critical value of the maximum eigenvalue statistic of none and at most 1 cointegrating equation are 14.07 and 3.76 respectively. The results indicate no cointegration at the 5% levels of significance.

As cointegration is not found between the GDP and any of the three types of energy consumption, we proceed to perform the Granger causality test based on the first differenced data of equation (3) and (4). The optimal number of lags p is chosen using the Schwarz Information Criteria. The optimal number of lags is: one for the VAR model between ∆ln(NRE) and ∆ln(GDP), one between ∆ln(RE_total) and ∆ln(GDP), and two between ∆ln(RE_electr) and ∆ln(GDP). We then tested the Granger causality using the Wald test and present the results in Table 5. The results of Granger causality test based on Engle-Granger procedure are consistent to our previous results using the Toda-Yamamoto procedure. The significant evidence of Granger causality is found to run only from renewable electricity consumption to GDP. The null hypothesis of “renewable electricity consumption does not Granger cause GDP” is rejected at the one percent level of significance. Or, in another word, we

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significantly accept the hypothesis that the renewable electricity consumption Granger causes the GDP, which confirms our previous result using the Toda-Yamamoto procedure. This result gives evidence that the renewable electricity consumption supports the “growth hypothesis” and suggests that the consumption of renewable electricity affects economic growth directly or indirectly complementing labor and capital in the production process in the Indonesian economy. Although the study of causality relationship between renewable electricity consumption and GDP in Indonesian economy is relatively new in the literature, our result is consistent with a closely similar study by Squalli (2007) but contrary to Yoo (2006).

Table 5. Granger causality test (Engle-Granger Procedure) Type of Energy (∆E)

Model Specification

∆GDP cause ∆E

∆E cause ∆GDP

Conclusion

∆ ln(NRE)

VAR (1)

0.16

0.31

Neutral

∆ln(RE_total)

VAR (1)

0.79

0.23

Neutral

∆ln(RE_electr.)

VAR (2)

0.23

5.36 ***

RE_electr.  GDP

Notes: ***, **, * denote the levels of significance at 1%, 5%, and 10%.

Moreover, the results of the Granger causality test between nonrenewable energy consumption and GDP and between non-fossil fuel energy consumption and GDP presented in Table 5, which are also consistent with the previous results using the Toda-Yamamoto procedure, give evidence that these types of energy consumption support the “neutrality hypothesis”. This suggests that the consumption of nonrenewable energy and non-fossil fuel energy does not significantly cause the economic growth in Indonesia, which is consistent with a related study of Soytas and Sari (2003).

6. CONCLUSIONS AND POLICY IMPLICATIONS In this paper, we examine the causality between renewable and nonrenewable energy consumption per capita and GDP per capita in Indonesia. We consider two proxies of renewable energy consumption: total renewable energy consumption and total electricity generated from renewable power plants. The Toda-Yamamoto procedure and Engle-Granger methodology are used to test the Granger causality relationship.

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Using these procedures, we found that the renewable electricity consumption is the only variable that significantly supports the “growth hypothesis” in Indonesian economy. On the other hand, we did not find any significant causality relationship between the consumption of nonrenewable energy and the GDP growth. This indicates that the higher consumption of renewable energy will increase the level of GDP and reduce the dependency on non-renewable energy sources. A wider implication on this research is related to the environmental impact in the context of reducing deforestation as well as lowering CO2 emissions. This can be extended to the REDD+ programme in Indonesia by returning the function of the forest as the vital instrument in stabilizing the climate change (United Nations, 2011). This finding is related to the conclusion provide by Hermawan and Hadi (2006) stating that renewable energy sources in Indonesia are plentiful but remains underdeveloped. This can stimulated by the production of renewable energy along with its supporting implementation and legal framework. It would also be interesting to analyze the Granger causality based on disaggregated energy consumption based on various industrial sectors and their contribution towards GDP growth. This will yield a more specific results on the sector’s prioritization in energy allocation and can be integrated into the national blue print that have been outlined by the Ministry of Energy and Mineral Resources.

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