Relationship between biomass energy and economic growth in transition countries: panel ARDL approach

Transcription

1 GCB Bioenergy (2014) 6, , doi: /gcbb Relationship between biomass energy and economic growth in transition countries: panel ARDL approach MELIKE E. BILDIRICI Department of Economics, Yildiz Technical University, Barbaros Bulvarı, Besßiktasß, 34349, Istanbul Abstract In this study, we have investigated the co-integration and causality relationship between the biomass energy consumption and economic growth in the transition countries by the Auto Regressive Distributed Lag (ARDL) method and Pedroni co-integration analysis for Analysed countries are gathered under two groups. For s 1 and 2, Pedroni panel co-integration test and ARDL bound test results show that biomass energy consumption and economic growth are co-integrated. Fully modified ordinary least squares results suggested that biomass energy consumption has positive effect on the economic growth. Keywords: biomass energy, economic growth, granger causality, panel ARDL, pedroni co-integration Received 18 March 2013 and accepted 18 April 2013 Introduction An effect of economic development has been increased consumption of fossil or nonrenewable energy to the point of dominance and decreased consumption of traditional biomass energy. Biomass energy consumption increased with effects of pollution caused by fossil energy and volatility of oil price after the oil crisis of the 1970s. Biomass energy that is substitute for oil is used to meet a variety of energy needs, including generating electricity, heating homes, fuelling vehicles and providing processed heat for industries. Biomass energy can be grouped into three categories: woody or solid, nonwoody and animal wastes. First, woody or solid biomass is produced in forests and agro-industrial plantations, bush trees, urban trees and farm trees; secondly, nonwoody biomass is produced in crop residues such as straw, leaves and plant stems, by processing residues like saw dust, bagasse, nutshell and husks and domestic wastes (food rubbish and sewage); lastly, animal waste is constituted by waste from animal husbandry (Bildirici & Ozaksoy, 2013; G okcßol et al., 2009). Biomass that is consumed directly or indirectly contains enormous energy that is used to produce heat, electricity, transportation fuel or chemicals. Humans have directly or indirectly consumed biomass energy for thousands of years, since humans started to burn wood to cook food or to keep warm. Direct consumption that is, the traditional consumption of biomass energy involves the process of combustion for cooking, heating and Correspondence: Melike E. Bildirici, tel , fax , melikebildirici@gmail.com industrial processes. Indirect consumption and/or modern consumption are (is) the more advanced process(es) of converting biomass into secondary energy Chang et al., 2003; Hall et al., Today, the primary energy source for many of the 1.6 million people in the underdeveloped countries, who are not served by electricity consumption, is traditional biomass energy that is a predominant form of energy consumed by people in the underdeveloped countries (Komor, 2003). Furthermore, more than 90% of the total rural energy supplies in developing countries are made up of biomass energy (Balat & Balat, 2009). One of the main reasons that traditional energy of rural and poor urban households comes from biomass is that fossil energy requires a well-developed infrastructure, which is absent in many rural households. Traditional biomass energy is an accessible energy source for rural as well as poor urban households. Thus, access to clean and affordable energy is essential for rural and urban poor areas for supply of heat, light and power as well as for other benefits such as the generation of income and health improvement (Gumartini, 2009). Besides these important effects, biomass energy is clean and renewable, while fossil energy causes pollution. Although there are positive externalities of energy consumption on economic growth (Bildirici et al., 2012), because of pollution caused by fossil energy, the type of energy is an important problem, humanity is facing the dual pressure of economic growth and environmental protection. In addition, energy problems are the core problems related to national security, sustainable economy and social development in the World (Zhang et al., 2011). Pezzey (1989, 1992) presented the wiring diagram and accompanying mathematical analysis showing various linkages between natural resource 2014 John Wiley & Sons Ltd 717

2 718 M. E. BILDIRICI flows, economic activity and natural resource depletion (Toman, 2003). Other studies by Tahvonen & Salo (2001) and Schmalensee et al. (1998) have found evidence of an inverse-u relationship for carbon emissions and energy consumption. The Tahvonen & Salo (2001) model determined transitions between non-renewable and renewable energy at different development stages of an economy, and they showed that, in the historical context, the emphasis on energy production may evolve from renewables to non-renewables and back to renewables. Along the equilibrium path, fossil energy resource consumption may increase. An inverse-u relationship between fossil energy consumption and economic growth may follow without the implementation of environmental policy (Tahvonen & Salo, 2001). As nonrenewal energy resources cause environmental pollution through over depletion, households and governments are paying more and more attention to environmental pollution and renewable energy consumption. The consumption of biomass energy, one of renewable energies, has the potential to reduce CO 2 emissions. Modern biomass energy is an alternative to reduce foreign oil dependency because it is renewable, abundant and can be produced everywhere (Schmalensee et al., 1998). The policy makers of many developed countries encourage the consumption of biomass energy as an alternative energy source (Best & Christensen, 2003; Gumartini, 2009) because policy makers and theorists accept that there is value addition in terms of the potential for increasing employment opportunities, enhancing energy security, boosting economic growth and developing strong export industries, as well as the environmental benefit (Domac et al., 2005). The aim of this study was to estimate the relationships between biomass energy consumption and economic growth by using the Auto Regressive Distributed Lag (PARDL) method and Pedroni (1999) co-integration analysis of transition countries: Albania, Bulgaria, Belarus, Estonia, Georgia, Latvia, Lithuania, Moldova, Poland and Romania. In analysed countries, modern biomass energy consumption is slowly expanding; on the other hand, heat production from agricultural waste among developing countries is common. Biomass energy in countries analysed is the main rural combustible energy being used especially for space, for water-heating and for cooking. Analysed countries have a considerable energy potential of biomass that is constituted by solid and nonwoody waste. Countries are divided into two subgroups such as former SSCB countries and other transition countries, because it is aimed to see whether there is difference between biomass consumptions between the two subgroups. All former SSCB countries are not analysed in this study because their economic structures are different from one another. Some transition countries that are oil producers were not included in this study. Some countries of the former Soviet Union play major roles in the world energy market as producers of oil as well as energy distribution centres: Russia, Azerbaijan, Kazakhstan, Turkmenistan, Uzbekistan and Romania are the main oil producers and exporters. While Russia, Kazakhstan, Azerbaijan and Turkmenistan are net exports of oil, the remaining countries of the former Soviet Union are net importers (Apergis & Payne, 2009; Bildirici & Kayıkcßı, 2012a,b). Biomass energy consumption is very low in these countries. Armenia, Azerbaijan, Kazakhstan, Kyrgyz Republic, Russian Federation, Ukraine and Uzbekistan are countries with the lowest level of biomass energy consumption. For comparison, the biomass energy consumptions of these countries and countries analysed for the year 2009 are given in Table 1. Ours is the first study in the literature that uses cointegration techniques to analyse the relationship between biomass energy consumption and economic growth for the transition countries. In addition, this study is distinguished from the previous works because it employs not only the Pedroni co-integration and Granger causality methods, but also the ARDL Table 1 Biomass energy consumption Analysed transition countries Albania Bulgaria Poland Romania Analysed former SSCB Belarus Estonia George Latvia Lithuania Other former SSCB Armenia Azerbaijan Kazakhstan Kyrgyz Republic Russian Federation Ukraine Uzbekistan

3 RELATIONSHIP BETWEEN BIOMASS ENERGY 719 method, to clarify the direction of relationships with elasticities of biomass energy consumption. Furthermore, in addition to presenting the direction of causality, our study estimates the magnitude of the relationship with two different estimators: fully modified ordinary least squares (FMOLS) and pooled mean group (PMG). In the next section, biomass energy consumption and economic growth studies in the literature is presented. Econometric theory and methodology are identified in the third section. The fourth section consists of the empirical results, while the last section includes conclusions and policy implications. Literature review Kraft & Kraft (1978), Akarca & Long (1980), Yu & Hwang (1984), Yu & Choi (1985), Erol & Yu (1987), Yu et al. (1988) are the first papers that investigated the causal relationship between energy consumption and economic growth. Kraft & Kraft (1978) found the relationship between energy consumption and GNP for the period as one way from GNP to energy consumption by using Sims causality analysis. Akarca & Long (1980) continued with the analysis by eliminating the data for the years 1973 and They found neutrality between GNP and energy consumption as being different from the results of the Kraft & Kraft (1978). Yu and Choi (1985) found causal relationship between energy consumption and gross national product in the Philippines, unidirectional causal relationship between gross national product and energy consumption for South Korea. However, they found no causal relationship between gross national product and energy consumption for the USA, UK and Poland. Erol & Yu (1987) found a bidirectional causal relation between energy consumption and GDP for Japan, between energy consumption and gross national product for Canada, between gross national product and energy consumption for Germany and Italy and no causal relationship in respect of England and France. Yu et al. (1988) found no relationship between energy and GNP, and between energy and employment, using Granger method in the United States. In pursuit of these pioneering papers, many works analysed the relationship between energy consumption and economic growth. They determined different results about the direction of Granger Causality between energy consumption and economic growth. The differences in causality results constructed four hypotheses: neutrality hypothesis, conservation hypothesis, growth hypothesis and feedback hypothesis. The neutrality hypothesis postulates that causality between GDP and biomass energy consumption does not exist. Biomass energy consumption is a relatively small component of overall output and thus will have little or no impact on economic growth. In the absence of this causal relationship, the implication is that a country does not depend on biomass energy for economic growth and development. Second, the conservation hypothesis determines the unidirectional causality between GDP and biomass energy consumption. Energy conservation policies, such as efficiency improvement measures and demand management policies designed to reduce biomass energy consumption and waste, may not have an adverse impact on economic growth. These hypotheses are supported in cases when an increase in economic growth causes an increase in biomass energy consumption. Third, the growth hypothesis suggests the unidirectional causality between biomass energy consumption and GDP. The growth hypothesis postulates that energy consumption has played a vital role in economic growth both directly and indirectly. As an increase in biomass energy consumption has a positive impact on economic growth, energy conservation-oriented policies that reduce biomass energy consumption can have an impact on economic growth. If unidirectional causality runs between biomass energy consumption and GDP, then reducing biomass energy consumption could lead to a decrease in economic growth. This implies that a negative shock to energy consumption leads to higher energy prices or energy conservation policies and has a negative impact on GDP. Fourth, the feedback hypothesis accepts the existence of bidirectional causality between GDP and biomass energy consumption. The feedback hypothesis determines the interdependent relationship between biomass energy consumption and economic growth, whereby each serves as a complement to the other. Chien & Hu (2007) examined the effects of renewable energy on the technical efficiency of 45 countries in 2001 and They found that the technical efficiency in OECD countries was higher than in non-oecd countries, but non-oecd countries have a higher share of renewable energy. Chien & Hu (2008) used the structural equation modelling approach to show the effects of renewable energy on the GDP of 116 countries. They determined that renewable energy has a positive and significant effect on capital formation and that there are relationships between renewable energy and GDP through the increase in capital formation. Menyah & Wolde-Rufael (2010) analysed the causal relationship between CO 2 emissions, renewable and nuclear energy consumption and realgdp for the United States for the period They found a unidirectional causality between nuclear energy consumption and CO 2 emissions without feedback, but no causality between renewable energy and CO 2 emissions. Apergis & Payne (2011)

4 720 M. E. BILDIRICI studied the relationship between renewable energy consumption and GDP in six Central American countries for the period, They indicated bidirectional causality between renewable energy consumption and GDP in both the short-run and the long-run. Apergis et al. (2010) analysed, using a panel error correction model, the causal relationship between CO 2 emissions, nuclear energy consumption, renewable energy consumption and GDP in a group of developed and developing countries from 1984 to The literature on the causal relationship between biomass energy and economic growth is very sparse when compared with the number of papers on other forms of energy. In recent years, the causal relationship between biomass energy consumption and economic growth was investigated for America by Bildirici (2012, 2013). Bildirici (2012) analysed, using the ARDL method, the relationship between biomass energy consumption and economic growth in some developing countries such as Argentina, Bolivia, Brazil, Chile, Colombia, Guatemala and Jamaica. According to her results, there is unidirectional causality between GDP and biomass energy consumption for Colombia and there is unidirectional causality between biomass energy consumption and GDP for Bolivia, Brazil and Chile. There is bidirectional causality for Guatemala. According to the long-run causality result, there is bidirectional causality for all countries. Bildirici (2013) performed the short-run and long-run causality analysis between biomass energy consumption and economic growth in the selected 10 developing and emerging countries by using the ARDL testing approach of co-integration and vector errorcorrection models. It covers annual data from 1980 to The co-integration test results show that there is co-integration between biomasss energy consumption and economic growth in nine of the ten countries (Argentina, Bolivia, Cuba, Costa Rica, El Salvador, Jamaica, Nicaragua, Panama, Paraguay and Peru). There is evidence of unidirectional Granger causality between biomass energy consumption and GDP in Argentina, Bolivia, Cuba, Costa Rica, Jamaica, Nicaragua, Panama and Peru, and there is bidirectional causality between biomass energy consumption and GDP for El Salvador. The long-run causality results indicate that there is bidirectional causality for Argentina, Bolivia and Nicaragua, and unidirectional causality for Cuba, Costa Rica, El Salvador Jamaica and Panama. A strong causality result shows bidirectional causality for Argentina, Bolivia, Costa Rica, Nicaragua, Panama and Peru. Materials and methods In this study, the relationship between biomass energy consumption and economic growth for the period was analysed by Pedroni (1999) panel co-integration and ARDL (See Bildirici & Kayıkcßı, 2012a,b; and Bildirici & Kayıkcßı, 2013) methods, and then estimated by FMOLS (Pedroni, 2000) and PMG (Pesaran et al., 1999) methods in some transition countries: Albania, Bulgaria, Poland and Romania in 1 and Belarus, Estonia, Georgia, Latvia, Lithuania and Moldova in 2. Twenty-one-year data for each country were taken from the World Bank and the International Energy Agency s statistics. As some of these countries emerged after the collapse of the Soviet Union, it is not possible to obtain data before Y represents the natural logarithm of per capita GDP; BEC represents the natural logarithm of biomass energy consumption. The sample size is rather small but enough to apply these methodologies. Methods unit root tests. The Levin et al. (2002) test and the Im et al. (2003) test are being used intensively in panel studies. While the Levin, Lin and Chu (LLC) test depends on pooled data, the Im, Pesaran and Shin (IPS) test is obtained as an average of ADF statistics. The LLC panel unit root test is based on the following equation: y it ¼ q i y i;t 1 þ z 0 it c þ u it i ¼ 1;...; N; t ¼ 1; T ð1þ where z it is the deterministic component and u it is stationary process. The LLC test assumes that residuals are independently and identically distributed with mean zero and variance r 2 u and q i = q for all values of i. The null hypothesis can be constructed as H 0 : q = 1, which means that all series in the panel have a unit root, whereas the alternative H 1 : q < 1 means that all series are stationary. While LLC allows for heterogeneity in the intercept terms, IPS allows for heterogeneity both in intercept and slope terms for the cross-section units. The IPS unit root test can be specified as: y it ¼ q i y i;t 1 þ X pi j¼1 u ijdy i;t j þ z 0 it c þ e it Null hypothesis states that all series in the panel have unit root H 0 : q i = 1 and alternatively part of the series is stationary: H 1 : q i < 1. It proposes an alternative test procedure that depends on the averages of individual unit root test statistics. The IPS test is equivalent to test unit root for all cross-section units. If N?, and T?, IPS t-statistic is ð2þ pffiffiffiffi N t 1 N t IPS ¼ E½t itjq i ¼ 1Š q ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi ) Nð0; 1Þ ð3þ 1 N var½t itjq i ¼ 1Š There may emerge cross-section dependence problems as a consequence of unaccounted residual interdependence, regional and macroeconomic linkages, unobserved common factors and externalities. Although IPS do not consider this, new generation panel unit root tests account for the cross-section problem by accepting the prevalence of macroeconomic linkages. For this purpose, Pesaran s (2007) Cross Sectionally Augmented IPS test was used (CIPS) (See Bildirici & Kayıkcßı, 2013). Pesaran takes the cross-sectionally Augmented Dickey-Fuller

5 RELATIONSHIP BETWEEN BIOMASS ENERGY 721 (ADF) regression and estimates cross-section units in panel by OLS: Dy it ¼ a i þ q i y i;t 1 þ c i y t 1 þ Xk d ij Dy t j þ Xk b ij Dy i;t j þ e it ð4þ j¼0 CIPS statistic that is based on the average of individual CADF statistics: CIPS ¼ 1 N X N t i ðn; TÞ where t i (N, T) is the t-statistic of the estimate of p i. cointegration test. The Pedroni (1995) test is the most popular among panel co-integration tests. Pedroni also takes into account heterogeneity by using specific parameters, which are allowed to vary across individual members of the sample. The co-integration test, which allows for cross-section interdependence with different individual effects, is specified as follows: DY it ¼ a i þ d it þ DY i;t p þ e ð6þ Pedroni (1995) has proposed seven different statistics to test panel data co-integration. The first four are based on pooling, which is called the within dimension and the last three are based on the between dimension. Both kinds of tests focus on the null hypothesis of no co-integration. The calculated test statistics must be smaller than the tabulated critical value to reject the null hypothesis of the absence of co-integration. Pedroni (1995) heterogeneous panel and heterogeneous group mean panel co-integration statistics are calculated. statistics and group statistics depend on the null hypothesis, H 0 : ^q i ¼ 1 for all i, where ^q i, is the estimated autoregressive coefficient of the residuals in the Ith unit. Although Pedroni s methodology allows us to test the presence of co-integration, it could not provide an estimation of a long-run relationship. Pedroni suggests a FMOLS estimator and describes how it can be employed to obtain the panel data estimates. In a heterogeneous panel with nonstationary variables, this method yields better estimates than Ordinary Least Squares (OLS). Its distribution is standard and is asymptotically unbiased. Moreover, FMOLS results produce consistent standard errors and t-statistics in the presence of endogenous regressors (Purna & Pravakar, 2007; Bildirici, 2004). We start with the OLS regression in Pedroni(1995) Y it ¼ a i þ b i BEC it þ l it ð7þ and these are co-integrated with slopes b i. i = 1,..., N for each country in the panel and t = 1,..., T refers to time period.let e it ¼ ^u it ; DBEC it be a stationary vector including the estimated residuals and differences in BEC. Also, let "!! X # T X T 0 X it ¼ lim E T 1 e it e it ð8þ T!1 t¼1 t¼1 be the long-run covariance for this vector process, which can be decomposed into X i ¼ X 0 i þ C i þ C i 0 j¼1 ð5þ ð9þ where X 0 i is the contemporaneous covariance and Γ i is a weighted sum of auto covariances. FMOLS estimators are given as:! ^b GFM ¼ XN X T 1! N 1 ðbec it BEC i Þ 2 X T ðbec it BEC i Þy it T^c i 1 t¼1 t¼1 ð10þ where y it ¼ðy it y i Þ ^X 21i ^X 22i DBEC it and ^c i ¼ ^C 21i þ ^X 0 21i ^X 21i ^X 22i ð^c 22i þ ^X 0 22iÞ ð11þ PN The between-dimensions estimator is ^b GFM ¼ N 1 b FM;i where b FM;i is a conventional FMOLS estimator applied to the ith country of the panel. The Pedroni (1995) test depends on the common factor restriction hypothesis. Also, it does not take cross-country dependence into account. These hypotheses raise some problems and they accept that the long-run parameters for the variables in their levels are equal to the short-run parameters for the variables in their first differences. These insufficiencies in the restrictions decrease the power of the residual-based cointegration tests. For this reason, we apply the panel ARDL test in addition to the Pedroni (1995) test. ARDL test. Pesaran et al. (1997), Peseran et al. (2004) suggested the ARDL approach for the co-integration analysis in the single equation models. The ARDL approach to co-integration involves two steps for estimating a long-run relationship. The first step is to investigate the existence of a long-run relationship among all variables. If there is a long-run relationship (co-integration) between variables, the second step is to estimate the long-run coefficients according to the ARDL model s results. According to them, cross-equation restrictions to the long-run parameters must be implemented by maximumlikelihood estimation for using this approach in panel data. The Hausman (1978) test is used for the validity of restrictions. Then, estimations are provided by the PMG Estimator. PMG is defined as the average of unrestricted single country coefficients and it is a good alternative to the other estimators for the panel like Dynamic OLS and FMOLS. As PMG presents important evaluations about whether long-run homogeneity is implemented in the relationship between biomass energy consumption and GDP, we preferred this estimator. The ARDL method had been used by Binder & Offermanns (2007) for the purchasing power parity analysis in Europe, by Bildirici & Kayıkcßı (2012a,b) for analysing the relationship between electricity consumption and economic growth and Bildirici & Kayıkcßı (2013) for analysing the relationship between oil production and economic growth in major oil exporting Eurasian countries: Azerbaijan, Kazakhstan, Russian Federation and Turkmenistan for the period, The ARDL model is a variety of the ARDL (p, q) model in the Pesaran et al. ARDL-UECM model for the standard log-linear functional specification of long-run relationship between variables:

6 722 M. E. BILDIRICI DY it ¼ U i þ Xp c ij DY i;t j þ Xq l ij DBEC i;t j þd 1ij Y i;t 1 where k¼1 þ d 2ij BEC i;t 1 þ e it d 1i ¼ ð1 Pq k ij Þ l ij ¼ Pq j¼1 x im m¼jþ1 k¼0 d 2i ¼ Pq x ij j¼0 c ij ¼ Pp ð12þ k im m¼jþ1 where i=1, N are cross-section units, t=1, T are time periods, φ i is group-specific intercept and x ij and k ij are k 9 1 vectors for explanatory variables. The model becomes equivalent to the Pesaran et al. (1997), Peseran et al. (2004) model. The null hypothesis of no co-integration among the variables in equation (12) is H 0 : d 1i = d 2i = 0 against the alternative hypothesis H 1 : d 1i 6¼ d 2i 6¼ 0. The null hypothesis of no co-integration is H 0 : x ij = 0 against the alternative hypothesis H 0 : x ij 6¼ 0. One set of critical values assume that all variables in the ARDL model are I (0), while the other is calculated on the assumption that the variables are I (1). The distribution of the test statistics under the null hypothesis is nonstandard, in which critical values depend on the order of integration of variables involved. If the computed test statistic exceeds the upper critical bounds value, then the null hypothesis is rejected. If the F-statistic is lower than the lower bounds value, then the null hypothesis of no co-integration cannot reject. Relying on the importance of homogeneity assumption between countries in this study, we implied long-run slope coefficients for the specific countries as a restriction to the model. This long-run relationship, which is also used in Binder & Offermanns (2007), is: Y it ¼ d ð 2i =d 1i Þ 0 BEC it u i =d 1i ¼ g 0 i BEC it þ u i ð13þ This relationship assumes O i ηc i. These restrictions are tested with Hausman (1978) test: h ¼ T^q 0^vð^qÞ 1^q ð14þ where ^q ¼ ^g u ^g r is the difference between unrestricted MG and restricted PMG. Hausman (1978) has determined that estimation of the variance of ^q (Vð^qÞ ¼vð^g u Þ vð^g r Þ) is consistent. Restricted estimation is consistent under the null hypothesis of homogeneity; Covð^g r ; ^qþ ¼0 In the second step, if co-integration is established, the conditional ARDL long-run model for Yt can be estimated as follows: Y it ¼ u i þ Xp k¼1 k ij Y i;t j þ Xq k¼0 x ij BEC i;t j þ e it ð15þ This involves selecting the orders of the ARDL (p, q) model using AIC or SBC. In the third step, we obtain the short-run dynamic parameters by estimating an error correction model associated with the long-run estimates. correction term. u is a parameter indicating the speed of adjustment to the equilibrium level after a shock. It shows how quickly variables converge to equilibrium and it must have a statistically significant coefficient with a negative sign. Granger causalities. In the last stage, Engle & Granger s (1987) causality test was used. At this step, moving from the error correction model, the PMG estimator is used instead of three-stage least squares (3SLS) and general method of moments (GMM) because PMG is an intermediate estimator, which includes pooling and averaging. Furthermore, it has advantages over OLS and especially DOLS as it allows for the differences in the countries for the short-run dynamics. Longterm coefficients are constrained to be the same. If the variables are co-integrated, PMG can be used for the Granger causality test like the other estimators. The Vector Error Correction model that was used to analyse the short-run relationships between the variables was constructed as follows: DY it ¼ a 0 þ Xm DBEC it ¼ a 0 þ Xp b ik DY j;t i þ Xn h ik DBEC j;t i þ Xq u ik DBEC j;t i þ u 1 ECT t 1 þ e 1t ð17þ # ik DY j;t i þ u 2 ECT t 1 þ e 2t ð18þ where residual e t is independently and normally distributed with zero mean and constant variance and ECT t 1 is the error correction term resulting from the long-run equilibrium relationship and b, φ, h, ϑ and ϕ are parameters to be estimated. ϕ is a parameter indicating the speed of adjustment to the equilibrium level after a shock. It shows how quickly variables converge to equilibrium and it must have a statistically significant coefficient with a negative sign. The error correction model is estimated by the mean group and pool mean group estimators. While mean group allows for heterogeneity in the short-run and long-run, the pool mean group allows for heterogeneity only for the short-run. These are better than the classical fixed-effect estimators and are robust to the presence of unit root and to endogeneity. Granger causality can be examined in three ways (Bildirici & Kayıkcßı, 2012a,b; See for use in time series Lee & Chang, 2008). In equations (17) and (18), first, short-run or weak Granger causalities are tested by H 0 : ϕ i = 0 and H 0 : ϑ i = 0 for all i and k. Second, long-run Granger causalities are tested from the ECTs in those equations. Long-run causalities are tested by H 0 : ϕ 1 = 0 and H 0 : ϕ 2 = 0. Strong Granger causalities are tested by H 0 : φ i = ϕ 1 = 0 and H 0 : ϑ i = ϕ 2 = 0 for all i and k. The advantage of using an error correction term to test for causality is that it allows for the testing of short-run causality through the lagged differenced explanatory variables and for the testing of longrun causality through the lagged ECT term. DY it ¼ a i þ Xp k¼1 c ij DY i;t j þ Xq k¼0 l ij DBEC i;t j þ u ij ECM t i þ e it ð16þ where residual, e t is independently and normally distributed with zero mean and constant variance and ECM t 1 is the error Results The statistics of biomass energy consumption and economic growth for s 1 and 2 are given in Table 2.

7 RELATIONSHIP BETWEEN BIOMASS ENERGY 723 Table 2 Basic statistics for s 1 and 2 Average Median Minimum Maximum SD 1 Y BEC Y BEC Unit root test results We tested individual heterogeneity and cross-country homogeneity by a standard test before estimating the panel model. According to the specification test, individual heterogeneity and cross-country homogeneity were determined. We used the IPS test from the first-generation tests and the Pesaran test for accounting the crosssectional dependency from the second-generation tests. The IPS test assumes the cross-sectional independency between panel units. However, the Pesaran test accepts more general forms of cross-sectional dependency (not only limited to common effects). The Pesaran test is based on the average of pair-wise correlation coefficients of the OLS residuals obtained from individual standard ADF regressions. The null hypothesis is cross-sectional independence and it is asymptotically distributed as a two-tailed standard normal distribution. The IPS and Pesaran unit root test results show that the null hypothesis of unit root cannot be rejected for the variables in levels. We further applied the unit root test in the first differences in the variables and the results reject the null hypothesis, implying that the levels are nonstationary, and the first differences are stationary. The results of Levin, Lin and Chu, IPS, ADF - Fisher Chi-squared and Pesaran tests are in Table 3. Pedroni and panel ARDL tests For s 1 and 2, Tables 4 and 7 report both the within- and between-dimension panel co-integration Table 4 Pedroni panel co-integration test and FMOLS estimation results Pedroni co-integration test results Within-dimension statistics v-statistic q-statistic PP-statistic ADF-statistic FMOLS result test (2.011) (2.456) (3.003) (2.442) Between-dimension statistics q-statistic PP-statistic ADF-statistic test (2.75) (2.2015) (3.056) c (2.199) y (2.871) R-squared 0.69 F-statistic test statistics for each panel data set. These statistics are based on averages of the individual autoregressive coefficients associated with the unit root tests of the residuals for each country in the panel. For s 1 and 2, all seven panel co-integration tests reject the null hypothesis of no co-integration (Tables 4 and 7). In Table 5, for 1, Johansen and Fisher co-integration test results with trace statistic and maximum eigenvalue statistic support the co-integration relationships of economic growth and biomass energy consumption found in the Pedroni test. For 2 in Table 6, we determined Johansen and Fisher co-integration test results as trace statistic and maximum eigenvalue statistics. In Tables 4 and 7, FMOLS results indicate the existence of a relationship between biomass energy consumption and GDP. Income elasticities of biomass energy are determined as for 1 and for 2. According to this result, biomass energy is necessity good for both 1 and 2. Table 3 The results of panel unit root tests Levin, Lin & Chu ADF Fisher chi-square Im-Pesaran-Shin Pesaran CD Level First Difference Level First difference Level First difference Y BEC Y BEC

8 724 M. E. BILDIRICI Table 5 Johansen and fisher co-integration test results Table 8 ARDL test and PMG estimation results H 0 Fisher stat. * (test statistic) Fisher stat. * (from max-eigen test) 1 2 F and Wald test result F and Wald test result r = r *Probabilities are computed using asymptotic Chi-squared distribution. Table 6 Fisher and Johansen co-integration test for 2 Fisher Stat. * (from trace test) r = r ARDL and PMG estimates Fisher Stat. * (from max-eigen test) *Probabilities are computed using asymptotic Chi-squared distribution. Table 7 Pedroni panel co-integration test for 2 Pedroni co-integration test results Within-dimension test statistics v-statistic q-statistic PP-statistic ADF-statistic FMOLS result (2.901) (2.446) (2.0690) (2.2601) Between-dimension test statistics q-statistic PP-statistic ADF-statistic (2.4108) (0.0886) (0.1866) c (1.9959) y (2.751) R-squared F-statistic F Y W Y F BEC W BEC F Y W Y F BEC W BEC PMG result Equations for the long-run relationship between biomass energy consumption and GDP were calculated for each country. From these results, MG was estimated from the unrestricted country-by-country estimation through ignoring small sample bias. Coefficients of MG are the averages of group-specific parameters and PMG coefficients are restricted to be the same across groups. Table 8 reveals the sufficient arguments for valid long-run relationships between the variables. According to these estimates, the effects of GDP on biomass energy consumption are positive, which means that results support the conservation hypothesis for these countries. The F-statistic was recalculated using Pesaran et al. (1999; 2004) and Narayan (2005) paper. Results are not much different from the bounds calculated by Pesaran. As can be seen from the Table, the estimated F-statistic is greater than the upper bound critical values at the 1% level in s 1 and 2. The results of the ARDL bounds tests shown in s 1 and 2 suggest the rejection of the null hypothesis of no long-run relationship at the 1% level of significance when BEC is treated as the dependent variable and GDP is the independent variable. PMG results indicate the existence of a strong relationship between biomass energy consumption and GDP. As the variables are used in their natural logarithm, coefficients can be treated as elasticities. Income elasticity of biomass energy demand being for 1 and for 2 shows that biomass energy is necessity good. PMG and FMOLS results determined that income elasticities of demand is necessity good. Causality results PMG result y (2.2245) (1.9904) c 1.45 (1.1175) (2.4119) R Testing for existence of a level relationship among the variables in the ARDL model 95% Lower bound 95% Upper bound 90% Lower bound 90% Upper bound *F-statistic was recalculated through using Pesaran method. Results are not much different from the bounds calculated by Narayan (2005). P(h), P-value of the Haussmann test was found as 0.39, At the last stage of the study, we implemented the Granger causality analysis. The results of estimation determine the existence or absence of a long-run relationship between biomass energy consumption and GDP. However, these methods do not indicate the direction of causality. For this reason, the Granger causality test was used to examine the causal relationships.

9 RELATIONSHIP BETWEEN BIOMASS ENERGY 725 Table 9 Results of granger causality * ECT (t-stat.) There is bidirectional causality between biomass energy consumption and economic growth both in the long-run and in the strong causality. This supports the significant coefficients in FMOLS and PMG models. Moreover, coefficients of Error Correction Terms (ECT) in Table 9 provide information about the required time for adjustment after any shock to the equilibrium. ECT coefficient determined that speed of adjustment is low, so after a shock, the system turns back to its long-run equilibrium level in more than 8 years for 2. According to weak causality, there is a unidirectional causality from Y to BEC and there is a bidirectional Granger causality between biomass energy consumption and GDP both long and strong forms in 1. For 2, in results of weak, long and strong causality, there is bidirectional causality. Discussion ΔBEC?ΔY ΔY?ΔBEC (2.273) (3.651) ECT?ΔBEC ECT?ΔY ΔBEC, ECT?ΔY ΔY, ECT?ΔBEC *In this Table, the symbol? shows the direction of causality. In this study, we investigated the co-integration and causality relationship of the biomass energy consumption and economic growth in some transition countries by the ARDL method and Pedroni co-integration analysis. According to this result, an increase in biomass energy consumption directly affects economic growth and that economic growth also stimulates further biomass energy consumption in that country. Economic policies aimed at improving the biomass energy infrastructure and increasing the biomass energy supply are the appropriate options for these countries as biomass energy consumption increases the income level. Policymakers in the analysed countries should consider improving the biomass energy infrastructure and increasing the biomass energy to achieve higher economic growth. 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