The Causality Relationship Between Energy Consumption and Economic Growth: The Case of Turkey

Enerji, Piyasa ve Düzenleme (Cilt:1, Sayı:1, 2010, Sayfa 101-120) The Causality Relationship Between Energy Consumption and Economic Growth: The Case of Turkey Yusuf Akan *, E. Muhsin Doğan, Cem Işık Abstract This paper investigates relationship between economic growth and energy consumption for Turkey. Economic growth and energy consumption variables are conceptualized as an econometric model and an analysis is made to relate energy consumption to Turkey's economic growth by using the ADF test, the Cointegration approach, the Granger Causality test and Error Correction Model over the period of 1970 2007. We discovered bidirectional causality between energy consumption and economic growth. This means that there is a causality relationship between the two. Keywords: Energy consumption, Economic growth, Granger causality, Error Correction Model (ECM) Enerji Tüketimi ile Ekonomik Büyüme Arasında Nedensellik İlişkisi: Türkiye Örneği Özet Bu çalışma, Türkiye de ekonomik büyüme ile enerji tüketimi arasındaki ilişkiyi ele almaktadır. Ekonomik büyüme ve enerji tüketim değişkenleri, ekonometrik bir model olarak incelenmiştir. Bu doğrultuda 1970 2007 dönemi ekonomik büyüme ve enerji tüketim verileri ADF birim kök analizi, Eş-bütünleşme yaklaşımı, Granger nedensellik testi ve Hata Düzeltme Modeli yardımı ile analiz edilmiştir. Araştırma sonuçları, Türkiye de ekonomik büyüme ile enerji tüketimi arasında iki yönlü bir nedensellik ilişkisi olduğunu ortaya çıkarmıştır. * Prof. Dr., Department of Economics, Atatürk University, Erzurum Associate Prof. Dr., Department of Economics, Atatürk University, Erzurum Ph. D. Candidate, Institute of Social Science, Atatürk University, Erzurum; e-mail: isikc@atauni.edu.tr

102 Akan, Doğan, Işık Keywords: Enerji tüketimi, Ekonomik büyüme, Granger nedensellik, Hata Düzeltme Modeli (HDM)

The Causality Relationship between Energy Consumption and Economic Growth 103 1. Introduction Turkey has become a considerably sized and growing market in the world. Considering Turkey s economic conditions, the increase in population, a growing number of city dwellers and rapid economic development are causing more energy consumption. Turkish energy sector had certainly been perceived to set the benchmark since the 1990s. Paradoxically, those same energy standards, which were regarded in the 2000s as important ingredients for the Turkey success story are now increasingly perceived as outmoded and necessitating substantial reform. In contrast once more in accordance with economic growth rates, Turkish energy sector is again setting the standard against which the performance of other models has to be measured. The domestic share of total energy consumption is 37%, and between the years 2000 and 2010, the cost for needed energy will be approximately 55 billion US$. The government has been planning for 81% of this amount as an investment (Kılıc and Kaya, 2007). Part 1 of this study is to compare the energy consumption projections and economic growth in Turkey that was constructed more than 27 years ago with the observed data for the year of 2007. Part 2 of this paper presents a summary of Turkey s energy sources. The main energy sources are shown as a table. Part 3 shows the growth rates and energy consumption literature review. In Part 4, the relationship between energy consumption and economic growth is determined by the model. The actual GDP growth rates that are the principal determinants of energy consumption are important over the long-term. In addition, the last section of this part of the paper provides an overview of the changing global energy environment and the resulting modifications in the projected use of energy that was expected over the 20-year interval ending in 2007. Part 4 presents the energy consumption from 1970 2007 and compares them with the observed 2007 data (http://www.enerji.gov.tr/) for Turkey. The tables and figures in paper provide a detailed picture of the changing patterns of energy consumption over the 20-year interval. 2. Turkey s Energy Sources The main energy resources of Turkey are hard coal, lignite, asphalt, petroleum, natural gas, hydroelectric energy, and geothermal energy. Turkey s natural energy resources are quite miscellaneous, such as hard coal, lignite, asphalt, oil, natural gas, hydro, geothermal, wood, animal and plant wastes, solar and secondary energy resources, coke, and briquettes. These resources are produced and consumed in the country. Turkey does not

104 Akan, Doğan, Işık own large fossil fuel reserves. In the future, it seems that it will be very difficult to meet the anticipated demand for oil, natural gas, and even coal. On the other hand, Turkey has huge reserves of renewable energy sources (Kılıc and Kaya, 2007)(See apex 3.1 and 3.2). Turkey is connected to a long distance gas pipeline delivering gas from Azerbaijan, which continues to Europe by BTC (Baku-Tbilisi-Ceyhan) gas pipeline. The existing gas transmission network in Turkey has a total length of 1076 km and total BTC line is 1768 km (www.bp.com). 3. Literature Review We have used the following studies, which compare the empirical results of energy consumption and economic growth, to see the significance of consumption in explaining economic growth. In most recent studies, researchers have focused on the co-integrating relationship between energy consumption and economic growth for a few countries. Earlier studies about the relationships between energy consumption and economic growth are currently ambiguous due to there being different results for different countries in the same subject or region, different time periods within the same country and different methodologies in different regions (see Table 1). The literature on Granger causality has grown considerably, following his seminal work in 1969, slowly during the first years and more rapidly in recent years. Granger causality test suggests which variables in the models have significant impacts on the future values of each of the variables in the system. Among recent applied studies, a significant amount of work has been devoted to addressing the above mentioned question of causality between energy consumption and economic growth. This paper studies the time series properties of energy consumption and GDP and reexamines the causality relationship between the two series in the top 10 emerging markets excluding China due to lack of data and G-7 countries 1. Soytas and Sarı (2003) show that there is a bi-directional causality in Argentina, causality running from GDP to energy consumption in Italy and Korea, and from energy consumption to GDP in Turkey, France, Germany and Japan. In a recent study of the economic growth performance of Asian countries, Lee and Chang (2008) shows that consumption has a long-run economic growth effect. Using 16 Asian countries economic data, Lee and Chang (2008) confirm that there is long-run unidirectional causality running 1 G-7 countries are Argentina, Italy, Korea, Turkey, Germany, France, Japan

The Causality Relationship between Energy Consumption and Economic Growth 105 from energy consumption to economic growth. This means that reducing energy consumption does not adversely affect GDP in the short-run but would in the long-run; thus, these countries should adopt a more vigorous energy policy. Chan and Lee (1996) used co-integration and vector error correction model (VECM) techniques to analyze China's energy consumption behavior, suggesting that energy price, income and the share of heavy industry output in national income were significant factors affecting energy consumption. Wei (2002) examined the long-run relationship between total energy consumption and some main economic factors such as energy price, income and share of heavy industry in GDP and found that energy consumption and main variables are co-integrated. In the literature of energy economics, the causal relationship between energy consumption and income is a well-studied topic. In their pioneering studies on Granger causality (Granger, 1969) Kraft and Kraft (1978) found unidirectional causality running from GNP to energy consumption for the United States. They utilized the technique of Sims (1972) and used annual data for the 1947 1974 periods. However, Akarca and Long (1980) pointed out that the Kraft Kraft results are spurious by changing the time period by 2 years. In Oh and Lee (2004) especially in distinguishing short-run relationships from long-run dynamics, the empirical results suggest that multivariate VECM models can be useful in examining Granger causality in the presence of co-integration. They found the existence of a long-run unidirectional causal relationship from GDP to energy, but no short run causal relationship between energy and GDP. The source of causation in the long run points to the error correction term. Table 1: Reported Empirical Results for the Relation between Energy Consumption and Economic Growth Samples Authors Empirical method Period Countries Causal relationship One Kraft & Kraft (1978) Granger causality 1947 1974 USA Growth Energy Oh-Lee (2004) Granger causality 1981 2000 Korea Growth Energy Paul & Bhattacharya Granger causality 1950 1996 India Energy Growth Wolde & Rufael (2004) Granger causality 1952 1999 China Energy Growth

106 Akan, Doğan, Işık Samples Authors Empirical method Period Countries Causal relationship Altinay & Karagöl (2005) Granger causality 1950 2000 Turkey Energy Growth Halicioglu(2007) Granger causality 1968 2005 Turkey Growth Energy Lise & Montford (2007) Granger causality 1970 2003 Turkey Growth Energy Soytaş & Sarı(2007) Granger causality 1968-2002 Turkey Energy Production Bowden & Payne (2008) Granger causality 1949 2006 USA Energy Growth Crossti Lee & Chang (2008) Granger causality 1971 2002 16 Asian Energy Growth Soytaş & Sarı(2003) Granger causality 1950 1992 G 7 Countries Energy Growth Energy Growth(Argentina) Energy (Italy-Korea) Growth(Turkey, Lee & Chang (2007) Panel VARs 1965 2002 1971 2002 22 developed and 18 developing countries Energy countries) Growth countries) Growth(developed Energy(developing Note: Consumption Growth denotes causality running from Energy consumption to economic growth. Growth Consumption denotes causality running from economic growth to Energy consumption. Consumption Growth denotes bidirectional causality between Energy consumption and Economic growth. Paul and Bhattacharya (2004) examined the different direction of causal relation between energy consumption and economic growth in India. Applying Engle Granger co-integration approach combined with the standard Granger causality test on Indian data for the period 1950 1996, it is found that bi-directional causality exists between energy consumption and economic growth. Further, they applied Johansen multivariate co-integration technique on the different set of variables. The same direction of causality exists between energy consumption and economic growth is used. In their study, empirical results using the standard Granger causality test reveal a 2 These countries are China, Hong Kong, Indonesia, Japan, Korea, Malaysia, the Philippines, Singapore and Thailand are member countries of APEC, but China, Hong Kong, Japan and Korea are not members of the ASEAN group.

The Causality Relationship between Energy Consumption and Economic Growth 107 unidirectional causal relation from energy consumption to economic growth. This process does not show any causal effect from economic growth to energy consumption. However, use of Engle Granger co-integration process exhibits a unidirectional long-run causality from GDP along with capital to energy consumption. No short-run relation is found. The results of the Engle Granger approach combined with the standard Granger causality process indicate that there is bi-directional causality between energy consumption and economic growth. The long-run causal relation runs from GDP to energy consumption and the short-run causal relation runs from energy consumption to GDP. Wolde and Rufael (2004) studied the causal relationship between various kinds of industrial energy consumption and real GDP in Shanghai for 1952 1999. The empirical evidence suggested that there was a unidirectional Granger causality running from coal, coke, electricity and total energy consumption to real GDP, except oil consumption. Altinay and Karagol (2005) found a strong long-run causality running from energy consumption to the real GDP in Turkey. The main conclusion of their study is that there is a relationship of causality between the variables. Sari and Soytas (2003) studied long-run causality running from energy consumption to income in the long-run but there exists also a bi-directional causality in the short-run. Lise and Montfort (2007) examined that causality runs from income to energy consumption in the long-run. Halicioglu (2007) found an evidence for the income and price elasticies of the residential energy demand both in the short-run and long-run for Turkey over the period 1968 2005. Bowden & Payne (2008) found a bidirectional Granger-causality causality between energy consumption and real GDP. Soytas and Sari (2007) investigated the causality between energy (electricity consumption) and production (Turkish manufacturing industry) at the industry level in an emerging market, Turkey. The uni-directional causality running from electricity consumption to value added. Lee and Chang (2007) investigated a relationship between energy consumption and real GDP in 22 developed and 18 developing countries. They found a uni-directional causality from real GDP to energy consumption in developing countries but, there is a bidirectional causality between energy consumption and real GDP in developed countries. 4. Methodology We obtain estimates of the relationship between economic growth and energy consumption on main macroeconomic variables. In this study

108 Akan, Doğan, Işık effect of economic growth on energy consumption is investigated. In the study the data of energy consumption and economic growth rates are used for the period of 1970 2007. These data is compiled from Central Bank (CBRT) Electronic Data Delivery System and Energy Ministry for the 1970 2007 periods. Each of the variables is purified from seasonal variations. Figure1: Economic Growth Rate and Energy Consumption Variables from 1970 to 2007 12 8 4 0-4 -8-12 1970 1975 1980 1985 1990 1995 2000 2005 EC GR Figure 1 shows a wavy movement for Energy Consumption and Economic Growth Rate variables from 1970 to 2007. The Augmented Dickey Fuller (ADF) test was used to determine whether the variables used in regression equations are stationarity or not. In ADF, the results are shown in Table 2 obtained by unit root test in order to determine whether time series are stationarity or not. The procedures of conventional econometric technique may not be suitable when the variables of interest are non-stationary or exhibit a unit root (Eagle and Granger, 1987; Enders, 1995). In the presence of nonstationary variables, Granger and Newbold (1974) pointed out that an OLS regression might become a spurious regression, leading to biased and useless results. In the formation of econometric models, it is important to test stationarity of time-series data to set up an appropriate methodology (Eagle and Granger, 1987) (Kim et al., 2006).

The Causality Relationship between Energy Consumption and Economic Growth 109 The complications with model may develop as a consequence of the spurious regression phenomenon first described by Granger and Newbold (1974), caused by nonstationary trends in time series data. The mean, variance, and autocorrelation of the series are in general nonconstant through time, and the coefficient of determination (R2) may simply capture correlated trends and reflect nonstationary residuals. As ADF argues in this case, OLS estimates do not converge to constants and the standard t and F statistics do not even have the limiting distributions. In developing economies such as Turkey, as shown in Figure 1, economic time-series data are likely to be non-stationary. Thus, ADF tests were carried to examine the presence of a unit root for all study variables prior to testing a long-run equilibrium relationship between energy consumption and economic growth. Results of both ADF for stationarity are reported in Table 2. Judged by McKinnon's (1991) critical values, the null hypothesis of one unit root against the alternative of stationarity cannot be rejected in levels of variables, but is rejected in their first differences. It is required to examine the stationarity of the variables before specification and estimation of co integration and VAR. Stationarity means that the mean and the variance of a series are constant through time and the autocovariance of the series is not time varying (Enders, 1995). A test of stationarity is important to set up the specification and estimation of the correct model since a wrong choice of transformation of the data gives biased results and has consequences for wrong interpretation (Engle and Granger, 1987). Hence, the first step is to test the order of integration of the variables. Integration means that past shocks remaining undiluted affects the realizations of the series forever and a series has theoretically infinite variance and a time-dependent mean (Enders, 1995). ADF tests were used to test the non-stationarity of the variables. The results of testing the order of natural logarithm of EG and EC are given in Table 2. The tests strongly supported the null hypothesis of non-stationarity before differencing the variables and the first differenced series of EG and EC were stationary based on the unit root tests. Using the appropriate methodology, testing the stationarity of a time series leads to the implementation of the econometric model. Table 2 provides the different test's statistics with regard to the null hypothesis of one unit root against the stationary alternative. The most general model suggests that the null can not be rejected for each variable and therefore a unit root might exist. Looking at Table 3, the data generation process

110 Akan, Doğan, Işık examination suggests that the use of co-integration techniques will be suitable to proceed with the long-run analysis. (Balaguer and Jorda, 2002). Table 2: Augmented Dickey Fuller (ADF) Unit Root Analysis Results Variable Constant Constant / time trend LEC (level) -2.0991-2.3151 LGR (level) -3.0380-3.1952 McKinnon Critical Values a=%1 b=%5 c=%10-3.830-3.049-2.655-4.535-3.675-3.276 LEC (first difference) -5.56172-5.68987 LGR (first difference) -6.5451-6.460475 a=%1-3.62102-4.22681 McKinnon Critical Values b=%5-2.94342-3.53660 c=%10-2.61026-3.20032 Note: LEG represents the annual economic growth rate in natural logarithms; LEG expresses economic growth in real terms and in natural logarithms. denotes the first difference of variable. The optimal lags selected for the truncation lag for the ADF test based on the Akaike information criterion (AIC, Judge, Griffiths, Hill, Lutkepohl, & Lee, 1985). The symbol * indicates that the null hypothesis can be rejected at the 1% level. Tests for unit roots have been carried out on EVIEWS 3.1. This paper uses Johansen s co-integration methodology which is suitable for estimation purposes when the variables are nonstationary, and particularly when they are I (1) variables. It suggests likelihood ratio tests which enable to test for the order of co-integration and for restrictions on the variables of the co-integrating vector. A comprehensive description of estimating co integrating vectors and testing hypothesis can be found in Johansen (1988, 1995) and Johansen and Juselius (1990, 1992). This approach estimates long-run or co-integration relationships between nonstationary variables using a maximum likelihood procedure which tests for the number of co integrating relationships and estimates the parameters of those co- integrating relationships. Johansen (1988) proposes two likelihood ratio tests for the co-integration rank, a maximum eigenvalue test and a trace test (Balaguer and Jorda, 2002).

The Causality Relationship between Energy Consumption and Economic Growth 111 The results of co-integration tests are reported in Table 3. The two test statistics, maximum eigenvalue (λmax) and trace, are presented, where λmax tests for at most r co integrating vectors against the alternative of exactly r + 1 co integrating relationships, while Trace tests for at most r co integrating vectors against the alternative of at least r + 1 vectors (Balaguer and Jorda, 2002). In order to find number of co integrated vectors, Johansen (1991), Johansen and Juselius procedure had been followed. Computed trace statistic and maximum self-value statistic results are compared to table critic values. Both statistic critic values have been given by Johansen and Juselius (1990). If the tests indicate that cointigrate vector/ vectors are important, it is accepted that there is a long term relation between series. Trace and maximum self value statistics are also used for determining the number of cointegrate vector. (The results are shown on Table 3) The co-integration relationship especially presents the affect of the relationship between the economic growth rate and energy consumption in the Turkish economy between the years 1970-2007. Consequently, two cointegration equalities can be defined between the series and this means there is a long-term relationship. Table 3: Johansen Co-integration Test Results Likelihood 5 Percent 1 Percent Hypothesized Eigenvalue Ratio Critical Value Critical Value No. of CE(s) 0.736275 48.23142 12.53 16.31 None ** 0.006890 0.248895 3.84 6.51 At most 1 Normalized cointegration equation : GR=-0.1255578+0.15783EC *(**) denotes rejection of the hypothesis at 5%(1%) significance level L.R. test indicates 1 co-integrating eqn(s) at 0.05 significance level Table 3 shows that there are one associate vectors between economic growth rate and energy consumption. Because the obtained trace values are higher than that of the 5% critical values, it has been supported by a double sided causality relation co-integration test.

112 Akan, Doğan, Işık Engle and Granger (1987) and Granger (1988) noted that if two timeseries variables are co integrated, then at least one -directional Grangercausation exists. The existence of a stable long-run relationship (cointegrating relationship) between economic growth and energy consumption implies that the two variables are causally related at least in one direction. As a final step, to answer the question regarding the direction of causation, the Granger causality tests were performed. Since two series of economic growth and energy consumption are cointegrated of order (1,1), a VAR model can be constructed in terms of the levels of the data (Engle and Granger, 1987). The causality tests between economic growth and energy consumption involve estimating the following bivariate regressions: Where µ is the deterministic component, e t is white noise and Energy and Growth represents the Energy expansion (LEC) and economic growth (LGDP), respectively. In a cointegrated system, the null hypothesis that Energy does NOT Granger-cause Growth cannot be rejected if Similarly, the null hypothesis that Growth does NOT Granger-cause Energy cannot be rejected If

The Causality Relationship between Energy Consumption and Economic Growth 113 Both hypotheses were tested by a standard F-test. The optimal lag l was selected with the smallest values of Akaike Information Criteria (AIC) and Schwartz Bayesian Criteria (SBC). Both criteria indicated lag 6 and lag 3 as the optimal lag for the quarterly and annual data, respectively. Then, the diagnostic checks with various lags were performed to ensure that results of the causality test are not sensitive to the different lags (Pindyck and Rubinfeld, 1991; Shan & Sun, 1998). (Kim et al., 2006). Table 4: Granger Causality Tests Dependent variable Short run causality Source of causation (independent variable) Long run causality LGR LEC ECT ECT/ LGR ECT/ LEC LGR -------- 9.93728* 14, 2561** --------- 1, 43607* LEC 4.58098 --------- 0,001 0,501999 ---------- The appropriate lag lengths are chosen using Akaike s Information Criteria (AIC). *Denotes for 5% significance level **Denotes for 1% significance level Table 4 provides results of the Granger causality test with annual data, respectively. The null hypothesis regarding no causation of economic development (LGDP) to energy consumption (LEC) is rejected at the 5% significance level for yearly data; the null concerning no causation of energy consumption (LEC) to economic growth (LGDP) is also rejected at the 5% significance level. Thus, both the energy consumption-led economic growth and the economic-led energy consumption of this study are supported. The coexistence of the energy consumption-led economic growth and the economic-led energy consumption indicates a reciprocal relationship between the two variables. Error-correction Model This article employs an error correction (ECM) model of the following forms:

114 Akan, Doğan, Işık ECT: The error-correction term 5. Results and Discussion This study provides the results of the estimation. First the results of the unit roots of the individual variables, second the co-integration test results and lastly the granger causality test results. Table 2 shows the results of the unit roots. The degree of integration of each variable has been determined that all the series are nonstationary by using P-P test. However, they are all stationary in the first difference. On the other hand, Table 3 shows the results of the Johansen cointegration test results. The implication is that standard Granger test is appropriate. The next section shows results of the Granger causality test results. The hypothesis that there is causality from EG to EC is accepted because the calculated F value is higher than the critical F value. The hypothesis that there is causality from EC to EG is accepted, on the other hand, because the calculated F value is higher than the critical F value. In the same way, when considering the effect of a standard error shock on the rate of energy consumption and growth, the shock has a positive effect on development as well as a standard error in energy consumption. (See apex 2) The results from impulse response functions also confirm the identified directions of causality for Turkey. The results of the impulse response functions are presented in Appendix. 6. Concluding remarks This article examines the short- and long-run causal relationship between energy consumption and economic growth of Turkey s economy. Based on cointegration and error correction modeling the empirical results show that there exists bidirectional causality running from energy consumption to economic growth and from economic growth to energy

The Causality Relationship between Energy Consumption and Economic Growth 115 consumption for Turkey. Impulse response functions also confirm the direction of causality in Turkey. In other words, this study presents an empirical investigation for Turkey s energy sector and the main purpose of this study is to test the affect of the relationship between the economic growth rate and energy consumption in the Turkish economy between the years 1970-2007. If economic growth rate increases then total energy consumption increases. The fact that economic growth tends to be very closely correlated with energy consumption, at least for short periods does not mean that energy consumption is the cause of the economic growth. Indeed, standard growth models assume exactly the opposite: that economic growth (due to the accumulation of capital, and labor, plus technical progress) is responsible for increasing energy and natural resource consumption. Based on the results, decisions on the energy related matters can be adjusted or altered such as the overall energy budget, approval of private or governmental energy projects. In future studies, energy researchers may want to compare multiple countries using the above variables as intervening factors between economic development and energy activity and draw a concrete conclusion as to energy-led economic growth theory. In summary, this study provides some insights into the relationship between energy consumption and economic growth. Future research on the relationship between energy and economic growth may shed additional insight on the relative impact of energy consumption on the economy as well as assist in the development of a more prudent and effective energy for Turkey. Moreover, Turkey can invest in research and development to innovate technology that makes alternative energy sources more feasible, thus mitigating pressure in environment. They can, furthermore, increase energy utilization and establish a price mechanism which may encourage the use of renewable and environmental friendly energy sources. References Altinay, G. and Karagol, E. (2005) Electricity Consumption and Economic Growth: Evidence from Turkey, Energy Economics 27, 849 856. Akarca, A.T. and Long, T.V (1980) On the Relationship between Energy and GNP: A Reexamination, J. Energy Development 5 (1980), 326 331.

116 Akan, Doğan, Işık Balaguer, L. and Cantavella-Jorda, M. (2002) Tourism as a Long-run Economic Growth Factor: The Spanish Case, Applied Economics, 34, 877 884. Bowden, N. and Payne, J. E., (2008) The Causal Relationship Between US Energy Consumption and Real Output: A Disaggregated Analysis, Journal of Policy Modeling, 31 (2), 180-188. Chan, H.L. and Lee, S.K. (1996) Forecasting the Demand for Energy in China, Energy Journal 17 (1), 19 30. Enders, W. (1995) Applied Econometric Time Series, Wiley, New York. Engle, R.F. and Granger, C.W.J. (1987) Co-integration and Error Correction: Representation, Estimation and Testing. Econometrica 50, 987 1007. Granger C.W.J. (1988) Granger, Causality, Co-Integration and Control, Journal of Economic Dynamics and Control 12, 551 559. Granger, C.W.J. (1969) Investigating Causal Relations by Econometric Models and Cross- Spectral Methods, Econometrica 37, 424-438. Granger, C.W.J and Newbold, P. (1974) Spurious Regressions in Econometrics, Journal of Econometrics, 2, 111 120. Halicioglu, F. (2007) Residential Electricity Demand Dynamics in Turkey, Energy Economics 29 (2), 199-210. Johansen, S. (1988) Statistical Analysis of Co-Integrating Vectors, Journal of Economic Dynamics and Control, 12, 231-54. Johansen, S. (1995) Likelihood-based Inference in Co-integrated Vector Autoregressive Models, Oxford University Pres Incorporated, New York. Johansen, S. and Juselius, K. (1990) Maximum Likelihood Estimation and Inference On Co-Integration-With Applications to the Demand For Money, Oxford Bulletin of Economics and Statistics, 52, 169-210. Johansen, S. (1991) Estimation and Hypothesis Testing of Co-Integration Vectors in Gaussian Vector Autoregressive Models, Econometrica 59, 1551 1580. Judge, G.G., Griffiths, W.E., Hill,R.C., Lutkepohl, L. and Lee T.C. (1985) The Theory and Practice of Econometrics (2nd ed.), Wiley, New York. Kilic, F. C. vand Kaya D. (2007) Energy Production, Consumption, Policies, and Recent Developments İn Turkey, Renewable and Sustainable Energy Reviews 11, 1312 1320 Kim, H. J., Chen, M. H. and Jang, S. C. (2006). Tourism Expansion and Economic Development: The Case of Taiwan, Tourism Management, 27, 925 933. Kraft, J. and Kraft, A. (1978) On the Relationship Between Energy and GNP, Journal of Energy and Development 3, 401 403. Lee, C.C. and Chang C. P. (2007) Energy Consumption and GDP Revisited: A Panel Analysis of Developed and Developing Countries, Energy Economics 29 (6), 1206-1223.

The Causality Relationship between Energy Consumption and Economic Growth 117 Lee, C.C., and Chang C. P. (2008) Energy Consumption and Economic Growth in Asian Economies: A More Comprehensive Analysis Using Panel Data, Resource and Energy Economics 30, 50-65. Lise., W. and Montfort, K.V., (2007). Energy Consumption and GDP in Turkey: Is There a co-integration Relationship? Energy Economics 29, 1166-1178. MacKinnon, J. G. (1991) Critical Values for Co-Integration Tests. In Engle, R.F. and Granger C.W.J. (Eds.), Long-Run Economic Relationships: Readings in Co-Integration (267 276). Oxford: Oxford University Press. Oh, W. and Lee, K. (2004) Energy Consumption and Economic Growth in Korea: Testing the Causality Relation, Journal of Policy Modeling 26, 973 981. Paul, S. and Bhattacharya, R.N. (2004) Causality between Energy Consumption and Economic Growth in India: A Note on Conflicting Results, Energy Economics 26, 977 983 Pindyck, R.S. and Rubinfeld, D.L. (1991) Econometric Models And Economic Forecasts, McGraw-Hill, New York. Shan, J. and Sun, F. (1998) On the Export-Led Growth Hypothesis: The Economic Evidence from China, Applied Economics 30, 1055 1065. Sims, C.A. (1972) Money, Income and Causality, Amer Economic Review 62, 540 552. Soytas, U. and Sari, R. (2003) Energy Consumption and GDP: Causality Relationship in G- 7 Countries and Emerging Markets, Energy Economics 25, 33 37. Soytas U. and Sari, R. (2007). The Relationship between Energy and Production: Evidence from Turkish Manufacturing Industry. Energy Economics, 29 (6), 1151-1165. Wei, W. (2002). Study on the Determinants of Energy Demand in China, Journal of Systems Engineering and Electronics 13 (3), 17 23. Wolde-Rufael, Y. (2004) Disaggregated Industrial Energy Consumption and GDP: The Case of Shanghai, 1952 1999, Energy Economics 26 (1), 69 75. http://www.bp.com/ http://www.enerji.gov.tr/

118 Akan, Doğan, Işık Appendix 1 to deal with trend and fluctuation movement of both variables together 8 4 0-4 -8 Hodrick-Prescott Filter (lambda=100) 12 8 4 0-4 -8-12 1970 1975 1980 1985 1990 1995 2000 2005 EC Trend Cycle Appendix 2: The effects of a standard error shock on energy consumption and economic growth rate Hodrick-Prescott Filter (lambda=100) 15 10 5 0 10 5 0-5 -10-5 -10-15 1970 1975 1980 1985 1990 1995 2000 2005 GR Trend Cycle

The Causality Relationship between Energy Consumption and Economic Growth 119 Empirical Distribution CDF Survivor 1.0 1.0 0.8 0.8 Probability 0.6 0.4 Probability 0.6 0.4 0.2 0.2 0.0 0.0-6 -4-2 0 2 4 6 8 10 12 EC -6-4 -2 0 2 4 6 8 10 12 EC 12 Quantile 1.0 CDF 8 0.8 EC 4 0 Probability 0.6 0.4-4 0.2-8 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Probability 0.0-10 -8-6 -4-2 0 2 4 6 8 10 GR 1.0 Survivor 12 Quantile 0.8 8 Probability 0.6 0.4 GR 4 0-4 0.2-8 0.0-10 -8-6 -4-2 0 2 4 6 8 10 GR -12 0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0 Probability

120 Akan, Doğan, Işık Appendix 3.1: Turkey s Renewable Energy Potential Energy type Usage Purpose Natural Technical Economical Capacity Electric (billion kwh) 977,000 6.105 305 Solar energy Thermal (mtoe) 80,000 500 25 Hydro power Electric (billion kwh) 430 215 124,5 Wind Direct Electric (billion kwh) 400 110 50 energy (land) Wind Direct energy (off-shore) Electric (billion kwh) --- 180 --- Wind energy wave Geothermal energy (billion kwh) 150 18 --- Electric (109 kwh) --- --- --- Thermal (mtoe) 31.500 7.500 2.843 Source: F. C- A. Kilic-, D. Kaya / Renewable and Sustainable Energy Reviews 11 (2007) 1312 1320 Appendix 3.2: Fossil Energy Sources in Turkey Sources Apparent Probable Possible Total Hard coal (million tons) 428 449 249 1126 Lignite (million tons) 7339 626 110 875 Asphaltite (million tons) 45 29 8 82 Bituminous schist (million 555 1086 269 1641 tons) Oil (million tons) 36 ---- ---- 36 Natural gas (billion m3) 8,8 ---- ---- 8,8 Source: F. C- A. Kilic-, D. Kaya / Renewable and Sustainable Energy Reviews 11 (2007) 1312 1320