Biomass energy and economic growth nexus in G7 countries: Evidence from dynamic panel data

Renewable and Sustainable Energy Reviews 49 (2015) 132–138

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Biomass energy and economic growth nexus in G7 countries: Evidence from dynamic panel data Faik Bilgili a, Ilhan Ozturk b,n a b

Department of Economics, Faculty of Economics and Administrative Sciences, Erciyes University, Turkey Faculty of Economics and Administrative Sciences, Cag University, 33800 Mersin, Turkey

art ic l e i nf o

a b s t r a c t

Article history: Received 8 February 2014 Received in revised form 9 March 2015 Accepted 23 April 2015

The purpose of this paper is to reveal the long run dynamics of biomass energy consumption and GDP growth through homogeneous and heterogeneous variance structures for G7 countries. It covers annual data from 1980 to 2009. Panel unit root analyses, panel cointegration analyses, conventional OLS and dynamic OLS analyses are run throughout homogeneous and heterogeneous variance structures of the panel data to examine the relationship. The findings show that the long run elasticities of panel real GDP data in terms of panel capital stock, panel human capital index and panel biomass consumption are significant and positive. The results confirmed the growth hypothesis in which biomass energy consumption have positive effects on economic growth of G7 countries. As a policy implication, energy policies which improve the biomass energy infrastructure and biomass supply are the appropriate options for G7 countries since biomass energy consumption increases the economic growth. & 2015 Elsevier Ltd. All rights reserved.

Keywords: Biomass energy consumption GDP growth Homogeneous and heterogeneous variance structures Panel data G7

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Econometric methodology and data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Econometric methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Estimation results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Conclusion and policy implications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Energy plays a vital role for all countries in the world. Fluctuations in energy prices, energy dependency, environmental problems, climate change, energy security and limited fossil energy sources have forced countries to replace fossil energy sources with renewable and sustainable energy sources. Thus, renewable energy, especially biomass has been accepted as a new energy source for sustainable development in the world. Biomass is basically a stored source of

n

Corresponding author. Tel./fax: þ 90 324 6514828. E-mail addresses: [email protected] (F. Bilgili), [email protected] (I. Ozturk). http://dx.doi.org/10.1016/j.rser.2015.04.098 1364-0321/& 2015 Elsevier Ltd. All rights reserved.

132 133 134 134 134 135 138 138

solar energy initially collected by plants during the process of photosynthesis whereby carbon dioxide is captured and converted to plant materials mainly in the form of cellulose, hemi-cellulose and lignin. Biomass includes crop residues, forest and wood process residues, animal wastes including human sewage, municipal solid waste food processing wastes, purpose grown energy crops and short rotation forests. Growing interest for biomass energy is driven by the following facts among others: (i) modern biomass energy is an alternative for reducing foreign oil dependency because it is renewable, abundant and can be produced everywhere. (ii) Biomass energy contributes to poverty reduction in under developed and developing countries and increase rural employment (biomass production is labor intensive). (iii) Biomass energy can be converted to useful thermal energy,

F. Bilgili, I. Ozturk / Renewable and Sustainable Energy Reviews 49 (2015) 132–138

electricity and fuels for power by means of transferring. (iv) Biomass energy reduces carbon dioxide emissions (CO2). (v) It promotes energy security by replacing renewable energy with fuels The reason for choosing G7 countries (USA, UK, France, Germany, Italy, Canada and Japan) as sample is that, G7 economies are the one who consumes 36.6 percent of World's total energy production, and causes 33.7 percent of World's total CO2 emissions, in average terms, over the period 2000–2008 (World Development Indicators (WDI), 2012). Thus, to know the direction of causality between biomass energy consumption and economic growth is important to determine appropriate energy policies. Eventually, the aim of this study is to estimate the relationship between biomass energy consumption and economic growth in G7 countries for 1980–2009 period. The panel unit root analyses, panel cointegration analyses, conventional OLS and dynamic OLS (DOLS) analyses are run throughout homogeneous and heterogeneous variance structures of the panel data to investigate the relations between the variables. This study can be defined as complementary to the previous papers in the context of energy economics. However, it differs from the existing literature of energy economics in some aspects. First, it is the first study in the literature that analyzes the causal relationship between biomass energy consumption and economic growth for the analyzed countries. Second, it considers both homogeneous and heterogeneous variance structures for the panel estimations and by following dynamic OLS methodology as well as conventional OLS methodology for panel data. Third, it employed multivariate model rather than bivariate by adding human capital and capital stock into the model. The rest of the paper is organized as follows: In the second section of the study, literature review is presented. Econometric methodology and data are given in the third section. The fourth section consists of the empirical results, while the last section includes conclusions and policy implications.

2. Literature review The relation between energy consumption and economic growth in the context of causality has been investigated in many studies during last two decades. An extensive literature survey of these studies can be seen in the paper of Ozturk [1]. The empirical outcomes of the studies which investigate the relationship between these variables are sometimes inconsistent with each other. According to Ozturk [1], using different data sets, alternative econometric methodologies and different countries' characteristics are the main reasons of this conflicting result. Also the papers obtained different results about the direction of Granger causality between energy consumption and economic growth. The differences in causality results constructed four hypotheses: (i) The growth hypothesis refers to a situation in which energy consumption plays a vital role in the economic growth process directly and/or as a complement to capital and labor. The growth hypothesis is supported, if uni-directional causality is found from energy consumption to economic growth. (ii) The conservation hypothesis means that economic growth is the dynamic which causes the consumption of energy sources. The validity of the conservation hypothesis is proved if there is unidirectional causality from economic growth to energy consumption. (iii) The feedback hypothesis is supported if there exists bi-directional causality between energy consumption and economic growth. (iv) The neutrality hypothesis indicates that energy consumption does not affect economic growth. In recent years, the causal relationship between renewable energy consumption and economic growth was investigated in some countries. The recent studies are as follows: Apergis and Payne [2] analyzed the relationship between renewable energy consumption

133

and economic growth for a panel of 20 OECD countries for 1985–2005 period. The Granger-causality results indicate bidirectional causality between renewable energy consumption and economic growth in both the short and long-runs. Apergis and Payne [3] studied the relationship between renewable energy consumption and GDP for a panel of six Central American countries over the period of 1980–2006. They found bidirectional causality between renewable energy consumption and GDP in both the short-run and long-run for the countries studied. Bildirici [4] examined the relationship between biomass energy consumption and economic growth in some emerging and developing countries (Argentina, Bolivia, Brazil, Chile, Colombia, Guatemala, and Jamaica) using ARDL method. Unidirectional causality from GDP to biomass energy consumption for Colombia, unidirectional causality from biomass energy consumption to GDP for Bolivia, Brazil, and Chile, and bidirectional causality for Guatemala is found. However, there is bidirectional causality for all countries in the long run. Tugcu et al. [5] investigate the long-run and causal relationships between renewable and non-renewable energy consumption and economic growth by using classical and augmented production functions, and making a comparison between renewable and non-renewable energy sources in order to determine which type of energy consumption is more important for economic growth in G7 countries for 1980–2009 period. The long-run estimates showed that either renewable or nonrenewable energy consumption matters for economic growth and augmented production function is more effective on explaining the considered relationship. On the other hand, although bidirectional causality is found for all countries in case of classical production function, mixed results are found for each country when the production function is augmented. Bildirici [6] studied the short-run and long-run causality analyses between biomass energy consumption and economic growth in the selected 10 developing and emerging countries for the 1980–2009 period by using the ARDL bounds testing approach of cointegration and vector error-correction models. The cointegration test results show that there is cointegration between the biomass energy consumption and the economic growth in nine of the ten countries (Argentina, Bolivia, Cuba, Costa Rica, El Salvador, Jamaica, Nicaragua, Panama, Paraguay, and Peru), whereas no cointegration is found in Paraguay. Bildirici and Ozaksoy [7] investigated the causality analysis between biomass energy consumption and economic growth was investigated in the selected 10 countries by using bounds testing approach and vector error-correction models for1960–2010 period. The causality results suggest that there is unidirectional causality from economic growth to biomass energy consumption for Austria and Turkey, unidirectional causality from biomass energy consumption to economic growth for Hungary and Poland, and bidirectional causality is found for Spain, Sweden, and France. Payne [8] examines the causal relationship between biomass energy consumption and real gross domestic product (GDP) within a multivariate framework for US by using annual data from 1949 to 2007. The results show unidirectional causality from biomass energy consumption to real GDP and supportive the growth hypothesis. Apergis and Danuletiu [9] investigate the relationship between renewable energy and economic growth for 80 countries under the Canning and Pedroni long-run causality test, which indicates that there is long-run positive causality running from renewable energy to real GDP for the total sample as well as across regions. The empirical findings provide strong evidence that the interdependence between renewable energy consumption and economic growth indicates that renewable energy is important for economic growth and likewise economic growth encourages the use of more renewable energy source. The presence of causality provides an avenue to continue the use of government policies that enhance the development of the renewable energy sector. Sebri and Ben-Salha [10] investigate the causal relationship between economic growth and renewable energy consumption in the BRICS countries over the

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F. Bilgili, I. Ozturk / Renewable and Sustainable Energy Reviews 49 (2015) 132–138

period 1971–2010 within a multivariate framework. The ARDL bounds testing approach to cointegration and vector error correction model (VECM) are used to examine the long-run and causal relationships between economic growth, renewable energy consumption, trade openness and carbon dioxide emissions. Empirical evidence shows bidirectional Granger causality exists between economic growth and renewable energy consumption, suggesting the feedback hypothesis, which can explain the role of renewable energy in stimulating economic growth in BRICS countries. Ozturk and Bilgili [11] examined the long run dynamics of economic growth and biomass consumption nexus by applying dynamic panel analyses for 51 Sub-Sahara African countries for 1980–2009 period. The results show that economic growth is affected by biomass consumption, openness and population significantly and positively in African countries. In conclusion, the paper finds significant effect of biomass consumption on GDP in 51 African countries.

3. Econometric methodology and data 3.1. Econometric methodology Econometric methodology employs (i) panel homogeneous and heterogeneous unit root analyses, (ii) panel homogeneous and heterogeneous cointegration analyses and (iii) panel homogeneous and heterogeneous dynamic ordinary least squares (DOLS) analyses. Considering Eq. (1) below, one may state that panel data
 yit has unit root if j ∂i j Z 1. yit ¼ ai þ ∂i yit  1 þ eit

ð1Þ

Or equivalently, following Eq. (2), which
is an  augmented form of Eq. (1), one may argue that panel data yit has one unit root if ϑi ¼ ð∂i  1Þ ¼ 0:

Δyit ¼ ai þ ni t þ ϑi yit  1 þ

m X

π k Δyit  k þ eit

ð2Þ

k¼1

The homogeneous unit root null hypothesis (or common slope or within dimension or common AR null hypothesis) is that H0 : ϑi ¼ ϑ ¼ 0 for all i. On the other hand the heterogeneous unit root null hypothesis (or hypothesis permitting distinct slope values or between dimension or individual AR null hypothesis) is that H0 : ϑi ¼ 0 for all i. If null hypothesis is not rejected, one may statistically conclude that panel data is not stationary. Therefore, homogeneous stationarity tests consider that ϑi are identical across panel members [12,13] while heterogeneous stationarity tests allow individual effects across panel data [14]. Panel variable might be found stationary [I(0)] or it might be found stationary at its first difference [I(1)]. Hence [I(1)] indicates that panel variable is difference stationary. Variables of [I(1)] might be employed in a long run equation provided that they all cointegrated. In case they are not cointegrated, the parameter estimation would yield spurious results. This work considers potential existence of panel cointegration between panel GDP growth variable and panel biomass consumption variable. To this end, one first needs to define fundamental GDP (y) function as follows: yit ¼ f ðK it ; Lit Þ

ð3Þ

where yit , K it ; Lit denote reel GDP, capital stock and labor, respectively. Eq. (3) yields also short and/or long run equilibrium between yit and K it and Lit . In the short run, the capital is assumed to be fix, hence, yit is solely function of labor. Total output therefore increases first at an increasing rate and later continues to increase at a decreasing rate and later reaches its maximum point and later experiences absolute decrease. In the long run, however, production (output) is function of both capital and labor. Once basic national

production function (yit ) is defined properly, then one may extend this function to observe the potential long run relation between reel GDP and biomass consumption if available as follows: yit ¼ f ðK it ; Lit ; Biomassit Þ

ð4Þ

where Biomassit denotes the ith country's biomass consumption at time t. Following Eq. (4), the long run equilibrium between yit and explanatory variables of K it ; Lit and Biomassit is given by yit ¼ δi þ θi K it þ ρi Lit þ σ i Biomassit þ nit

ð5Þ

where nit denotes residuals from Eq. (5). Following (5), one may run cointegration tests through Eq. (6). nit ¼ φi nit  1 þ ωit

ð6Þ

The null hypothesis of no cointegration is H0 : φi ¼ 1 for all i. The alternative homogeneous hypothesis is HA : φi ¼ φ o 1 for all i [11,12] and alternative heterogeneous hypothesis is HA : φi o 1 for all I [13,14]. If null hypothesis of no cointegration is rejected, one may state that yit and explanatory variables of K it ; Lit and Biomassit are cointegrated. This cointegration relation gives long run equilibrium parameter estimates of δi ; θi ; ρi and σ i : This paper employs cointegration analyses through the methodologies of Ordinary Least Squares (OLS) and Dynamic Ordinary Least Squares (DOLS). Rewriting Eq. (5), the panel fixed effect model in matrix form can be shown as follows: yit ¼ δi þx0it B þ nit ; i ¼ 1; 2; …; N and t ¼ 1; 2; …; T:

ð7Þ

where xi;t is k  n matrix of K it ; Lit and Biomassit , x0it is transpose of matrix x, B is k  1 vector of slope parameters. In order for one to correct the possible endogeneity and serial correlation problems from OLS regressions, he or she may follow DOLS regression by adding differenced leads and lags as follows: yit ¼ δi þx0it B þ

k X

τi;k Δxit þ k þ nit

ð8Þ

j ¼ k

If yit and xit are I(1) and If yit and xit are cointegrated, the long run parameters of OLS and DOLS are obtained as is shown in seminal works of Kao and Chiang [19] and Pedroni [20]. P   PT N i¼1 t ¼ 1 ðxit  xi Þ yit  yi  bols ¼ P ð9Þ PT N 0 i¼1 t ¼ 1 ðxit  xi Þðxit  xi Þ

bdols ¼ N

1

N X

T X

i¼1

t¼1

!1 8 it 8 0it

T X



8 it yit  yi



! ð10Þ

t¼1

where 8 it represents 2ðK þ 1Þ  1 vector  of explanatory variables including xi;t  xi ; Δxi;t  K ; …; Δxi;t þ K . 3.2. Data This paper follows panel data of G7 countries; Canada, France, Germany, Italy, Japan, the UK and the USA for the period 1980– 2009. The variables employed are: (i) the natural log of GDP [rgdpo, expenditure-side real GDP at chained PPPs (in mil. 2005US$)], (ii) capital stock [ck, capital stock at current PPPs (in mil. 2005US$)], (iii) human capital [hc, index of human capital per person], based on years of schooling [21] and returns to education [22] and (iv) biomass consumption [used extraction of Biomass]. The first three variables are obtained from Penn World Table [23] and the data for biomass is extracted from Global Material Flow Database [24]. Before launching data analyses to inspect the possible impact of biomass, human capital and capital stock on GDP within G7 region, one may consider some data table observations to figure out general outlook of the countries’ economies and/or to see, i.e., if countries follow homogeneous or heterogeneous structures in terms of their

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135

Table 1 The mean of G7 variables (1980–2009). Sources: 1- GDP, Human Capital Index, Capital Stock and population are extracted from Penn World Table, version 8.0, www.ggdc.net/pwt. 2- Total Energy Supply is extracted from IEA, Energy Balances of OECD Countries CD ROM, 2010 http://www.iea.org. 3- Biomass used is extracted from Global Material Flows, 2014 http://www.materialflows.net/data/datadownload/.

Country

GDP [million US$ (2005)]

Biomass used [ktoe]a

Total Energy Supply Biomass % of Total Energy Human Capital [ktoe] Supply Indexb

Capital Stock [million US$ Population (2005)] [million]

Canada France Germany Italy Japan UK US G7

863,105.281 1,439,714.00 1,984,326.65 1,361,822.84 3,135,079.17 1,423,790.01 9,350,922.31 2,794,108.61

28,134.584 32,050.792 23,965.511 13,948.149 8223.5850 15.158.358 161,161.18 40,377.4513

319,071.26 114,478.66 161,372.40 26,301.833 83,198.000 228,350.16 1,628,310.0 365,868.902

2,371,081.7 4,673,379.62 6,637,828.18 5,112,631.00 11,564,118.4 3,288,553.19 26,714,037.8 8,623,089.98

a b

0.091543 0.297254 0.151873 0.538963 0.109551 0.067476 0.098992 0.193665

3.117167 2.595700 2.700994 2.568708 3.027266 2.642018 3.478343 2.875742

29.01357 59.45997 80.58065 57.35668 123.2303 58.20869 266.9917 96.40594

1 kt (kilotonne, TNT)¼ 0.099933123148944 ktoe (kt oil equivalent). Based on years of schooling and returns to education.

GDPs, biomass consumption, the percentage usage of biomass in total energy supply, human capital, capital stock and population. Table 1 reveals the mean values of variables for G7 countries. The UK, Italy and France are close to each other in terms of their GDP averages, as GDP of the US appears to be prominently above other G7 countries GDPs. The biomass usages of Canada, France and Germany seem to be not distant from each other and the human capital index of France, Germany, Italy and the UK cluster around 2.5 values. The capital stocks of G7 countries, on the other hand, bear more heterogeneous structures than the other variables given in Table 1. The italic and bold values in the table represent maximum and minimum values of the variables, respectively. Among G7 countries, the US has the maximum mean values of GDP (9,350,922.31 milllion US$), biomass usage (161,161.18 ktoe), total energy supply (1,628,310 ktoe), human capital index (3.478343), capital stock (26,714,037.8 million US$) and population (266.9917 million) Within G7 countries, on the other hand, the minimum values of GDP, biomass used, total energy supply, biomass % of total energy supply, human capital index, capital stock and population are 863,105,281 million US$ (Canada), 13,948,149 ktoe (Italy), 26,301,833 ktoe (Italy), 0.067476 (UK), 2.568708 (Italy), 2,371,081.7 million US$ (Canada) and 29.01357 million (Canada), respectively. Table 1 asserts, as well, that Italy has the highest percentage of biomass usage in her total energy supply (0.54%) together with her lowest total energy supply and biomass consumption in G7 region. One may also notice that France and Italy reach relatively higher biomass consumption percentage of total energy supply than G7 average of biomass consumption percentage of total energy supply during period 1980–2009. Following population, one may also want to consider per capita mean values of related variables. Table 2 gives per capita mean values of GDP, biomass consumption, total energy supply and capital stock. Table 2 reveals that US economy employs highest per capita mean values of GDP and capital stock whereas Italian economy experiences minimum per capita values of GDP and total energy supply and that the UK economy has minimum per capita capital stock among other G7 countries. Table 2 shows, as well, that Canadian economy follows highest averages of per capita biomass usage and total energy supply whereas Italian economy has the minimum mean per capita values of GDP and total energy supply within G7 countries. One observes also that the UK, Japan, Italy and Germany yield relatively lower mean per capita biomass consumption than G7 average of mean per capita biomass consumption. After having an initial inspection through observations of minimum and

Table 2 The mean of per capita G7 variables (1980–2009). Sources: 1- Penn World Table, version 8.0, www.ggdc.net/pwt. 2- IEA, energy balances of OECD countries CD ROM, 2010 http://www.iea.org. 3- Global material flows, 2014 http://www.materialflows.net/data/ datadownload/.

Country

GDP [million US$ (2005)]

Biomass Total Energy used [ktoe]a Supply [ktoe]a

Capital Stock [million US$ (2005)]

Canada France Germany Italy Japan UK US G7

29,748.3282 24,213.1644 24,625.3504 23,743.0551 25,440.8167 24,460.0928 35,023.2696 26,750.5824

969.704227 10,997.3103 539.031429 1,925.30642 297.41026 2002.61983 243.182637 458.566159 66.7334722 675.142462 260.413994 3922.95638 603.618688 6098.7289 425.727815 3725.80435

81,723.1899 78,597.0747 82,374.9682 89,137.497 93,841.5266 56,495.9128 100,055.686 83,175.1222

a

1 kt (kilotonne, TNT) ¼0.099933123148944 ktoe (kt oil equivalent).

maximum values and some heterogeneous structures of related variables in G7 region, the succeeding graphical and econometrical analyses will examine if there exist some co-movements or longrun relations between GDP (economic growth) and biomass consumption, labor (human capital index) and capital (capital stock) within G7 countries.

4. Estimation results One can get visual inspections of panel data through Figs. 1–4. Figs. 1–4 plot the G7 panel data for GDP, capital stock, human capital index and biomass consumption, respectively. All variables are in natural logarithms. The figures give the line graphs for observations through the combinations of scales of observations (vertical axes) and individual countries from 1980 to 2009 (horizontal axes). Hence the cross sections of 1–7 on horizontal axes denote Canada, France, Germany, Italy, Japan, the UK and the USA. One may conclude from the initial observations for four variables that (i) there is a co-movement among the series, (ii) the individual countries seem to follow identical patterns for each variable yet they differ from each other at every individual data point and (iii) the real GDP, capital stock, human capital index and

15.5 15.0 14.5 14.0 13.5 13.0

1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7 Canada

France

Germany

Italy

Japan

UK

1.1 1.0 0.9 0.8 0.7 0.6

USA

Canada

Fig. 1. Panel G7 natural log of real GDP (y) data 1980–2009.

France

1980-2009

17.6

Germany

Italy

Japan

UK

USA

Fig. 3. Panel G7 natural log of human capital index (L) data 1980–2009.

1980-2009 log of used extraction of Biomass (in kt)

14.5

16.4 16.0 15.6 15.2 14.8 14.4 14.0

Canada

France

Germany

Italy

Japan

UK

USA

14.0 13.5 13.0 12.5 12.0 11.5 11.0

- 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09

16.8

1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7

17.2

1 - 84 1 - 89 1 - 94 1 - 99 1 - 04 1 - 09 2 - 84 2 - 89 2 - 94 2 - 99 2 - 04 2 - 09 3 - 84 3 - 89 3 - 94 3 - 99 3 - 04 3 - 09 4 - 84 4 - 89 4 - 94 4 - 99 4 - 04 4 - 09 5 - 84 5 - 89 5 - 94 5 - 99 5 - 04 5 - 09 6 - 84 6 - 89 6 - 94 6 - 99 6 - 04 6 - 09 7 - 84 7 - 89 7 - 94 7 - 99 7 - 04 7 - 09

log of capital stock at current PPPs (in mil. 2005 US$)

1.2

- 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09

16.0

1980-2009 1.3

1 1 1 1 1 1 2 2 2 2 2 2 3 3 3 3 3 3 4 4 4 4 4 4 5 5 5 5 5 5 6 6 6 6 6 6 7 7 7 7 7 7

1980-2009 16.5

log of human capital index (schooling, returns to education)

F. Bilgili, I. Ozturk / Renewable and Sustainable Energy Reviews 49 (2015) 132–138

- 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09 - 84 - 89 - 94 - 99 - 04 - 09

log of real GDP at chained PPPs (in mil. 2005 US$)

136

Canada

France

Germany

Italy

Japan

UK

USA

Fig. 2. Panel G7 natural log of capital stock (K) data 1980–2009.

Fig. 4. Panel G7 natural log of biomass consumption (biomass) data 1980–2009.

biomass consumption of panel data for G7 countries, on average, tend to go up through time. The mean slopes of GDP, capital stock, human capital index and biomass consumption are 0.719, 0.775, 0.053 and 0.610, respectively. Considering panel mean slopes, one may indicate that time trends of all panel variables are positively sloped through time yet some individual countries follow negative trends (Italy and Japan yields decreasing biomass consumption at decreasing rates). Also capital stock increases relatively faster than other variables while human capital index grows slightly through time in comparison with other variables. At this point, before conducting unit root and cointegration analyses to explore if there happens long run equilibrium between variables, one may consider causalities, if exist, from biomass, human capital and capital stock to GDP. Pairwise Panel Causality Tests of Dumitrescu and Hurlin [25] indicate that biomass, capital stock and human capital individually causes GDP with W-stat(prob.) of 5.764(0.039), 5.730 (0.041) and 6.133(0.017), respectively. The values in parentheses are probability statistics of W-stat. Dumitrescu and Hurlin [25] demonstrates, on the other hand, that GDP does not cause human capital but causes biomass consumption and capital stock. This paper follows next panel stationarity and panel cointegration tests to provide researcher with more statistical evidence to explore whether or not the variables of interest have statistically significant cointegration equilibrium. Table 3 shows panel unit root tests and all tests consist of constant and trend terms and the probabilities are given in parentheses. In Table 3, within dimension tests (panel tests) assume common

unit root process whereas between dimension (group mean tests) assume individual unit root process. Therefore, within dimension tests consider homogeneous structure while between dimension tests analyze heterogeneous structure of the variables. Breitung (t), Levin et al. (Wald), ADF-Fisher (chi-square) and IPS (Wald) unit root tests for the level of GDP (yit), for instance, reveal that the null of unit root is not rejected with the statistics of 3.148(0.999), 2.164(0.995), 3.256(0.998) and 1.949(0.974), respectively. The same tests for the first difference of yit yield the statistics of  7.388(0.000),  7.982 (0.000), 49.294(0.000) and  5.261(0.000). The latter statistics for differenced yit indicate that yit fluctuates but tends to return to its deterministically trending mean. According to Table 3 outcomes, hence, the natural log of yit and Kit are found integrated of order 1 [I(1)] by all statistics. All statistics reveal that Lit has unit root. Under homogeneous variance structure, the differenced Lit is not found stationary either. However, under heterogeneous variance structure, differenced Lit is found stationary [I(1)]. All eight statistics, except Levin et al. [26] t-statistic of between dimension, conclude that Biomassit follows stationary path [I(1)]. Therefore one may claim overall that all variables are stationary [I(1)]. Table 4 yields panel cointegration analyses’ results. All statistics considering both homogeneous variance structure and heterogeneous variance structure reveal that variables are cointegrated. Pedroni [16,17] and Kao [15,19] statistics follow the null hypothesis that estimated model is not cointegrated. Pedroni [16,17] cointegration tests (t, TN1, TN2, panel v, panel rho, panel t) assume alternative hypothesis of common autoregressive process. Kao [15,19] panel

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137

Table 3 Panel unit root test results. yit Level Within dimension Breitung-t [12] Levin et al. W [26] Between dimension ADF-Fisher chi-square IPS W [14] First difference Within dimension Breitung-t [12] Levin et al. t [26] Between dimension ADF-Fisher chi-square IPS W [14]

Kit

Lit

Biomassit

3.148 (0.999) 2.614 (0.995)

0.391 (0.652)  0.152 (0.439)

6.302 (1.000) 5.960 (1.000)

1.965 (0.975)  2.715 (0.003)

3.256 (0.998) 1.949 (0.974)

8.659 (0.852) 0.310 (0.621)

1.514 (1.000) 2.057 (0.980)

9.772 (0.778) 0.064 (0.525)

 7.388 (0.000)  7.982 (0.000)

 1.750 (0.040)  2.012 (0.022)

0.283 (0.611)  0.233 (0.490)

 5.584 (0.000)  9.064 (0.000)

49.294 (0.000)  5.261 (0.000)

23.702 (0.0499)  2.241 (0.125)

98.118 (0.000)  9.642 (0.000)

89.561 (0.000)  8.965 (0.000)

Table 4 Panel cointegration test results.

Homogeneous variance structure DF rho [15] DF t rho [15] DF rho star [15] DF t rho star [15] ADF [11] t rho NT [16] TN1 rho [16] TN2 rho [16] Panel v [17] Panel rho [17] Panel t nonparametric [17] Panel t parametric [17] Heteregeneous variance structure Group rho [17,18] Group t nonparametric [17,18] Group t parametric [17,18]

Statistic

Probability

 17.224  5.093  26.156  4.536  5.595  120.158  49.620  49.501 9.975  6.294  3.842  148.650

0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

 6.503  6.267  4.147

0.000 0.000 0.000

Table 5 Long run dynamics of yit in terms of K it ; Lit and Biomassit for G7. Homogeneous OLS

Homogeneous adjusted OLS

Homogeneous DOLS

Heterogeneous DOLS

Biomassit Kit Lit

0.575 (0.035) 0.609 (14.434) 1.198 (7.672)

0.588 (26.287) 0.609 (42.965) 1.183 (21.556)

0.082 (3.604) 0.521 (36.109) 1.483 (26.520)

0.689 (  15,821.317) 0.383 (141.411) 1.149 (78.679)

R2 Adj. R2

0.9518 0.9511

0.9518 0.9511

0.9833 0.9822

0.9884 0.9877

cointegration tests (DF rho, DF t, DF rho star, ADF) consider common autoregressive process of residuals of panel model, as well. Pedroni [16,17] group cointegration tests, on the other hand, follow alternative hypothesis of individual autoregressive process for residuals of cointegrating equilibrium. Considering both common and individual autoregressive processes, hence, Table 4 gives the output from unit root tests for residuals obtained from the model in which GDP, capital stock, human capital and biomass consumption are employed. Observing common and individual autoregressive processes, all statistics explore that the null hypotheses of no cointegration are rejected at 1% significance level. According to DF rho statistic of Kao [15,19], for instance, indicates that the probability of obtaining a statistical value of 17.224 or more than 17.224 (in absolute value) is zero under homogeneous structure. This result demonstrates the statistical evidence of cointegration between variables of interest. Under heterogeneous structure, group rho statistic of Pedroni [16,17],

for example, also discloses the existence of cointegration with the rho value of  6.503 (0000). Therefore one can assert that yit ; K it ; Lit and Biomassit have long run equilibrium. All cointegration tests are done through (i) Gauss code of Kao and Chiang [19], (ii) NPT 1.3 Program written by Chiang and Kao [27] and (iii) Pedroni's RATS code [13]. Table 5 gives long run equilibrium parameter estimates of panel production (yit) function. Four methodologies (i) homogeneous estimations of OLS, (ii) homogeneous adjusted OLS, (iii) the homogeneous estimations of DOLS with one lead and two lags and (iv) the heterogeneous estimations of DOLS with one lead and two lags yield common outcome indicating that natural log of panel real GDP (yit) is increasing function of natural logs of panel capital stock, panel human capital index and panel biomass consumption data. All explanatory variables have statistically significant and positive impacts on production. The highest magnitude of influence on

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panel production data belongs to panel human capital data as expected. The panel output elasticity with respect to human capital is greater than 1.1. One percent increase in Biomassit, Kit and Lit variables will increase yit by 0.575, 0.609 and 1.198 percent, respectively, according to OLS estimations. Adjusted OLS have similar magnitudes with those of OLS. Homogeneous DOLS estimations express that the impacts of Biomassit, Kit and Lit on yit are 0.082, 0.521 and 1.483, respectively. DOLS estimations under heterogeneous variance structure yields the elasticities of GDP with respect to Biomassit, Kit and Lit are 0.689, 0.383 and 1.149, respectively. The main consideration of this paper is to reveal the possible effect of biomass consumption on GDP growth. The estimations of OLS, adjusted OLS, homogeneous DOLS and heterogeneous DOLS, on average, imply that a percent increase in biomass consumption of G7 countries will raise the GDP of G7 countries by 0.4835 percent. Homogeneous DOLS has the estimation of 0.082 whereas heterogeneous DOLS has that of 0.689. Among all four estimation methodologies, one may choose heterogeneous DOLS output in terms of R2 and adjusted R2. Overall all estimation outputs refer significant and considerable impact of biomass consumption on GDP growth. Gauss code of Kao and Chiang [19] and NPT 1.3 Program written by Chiang and Kao [27] with some modifications are launched to obtain the parameter estimations. The t ratios are given in parentheses. Adjusted OLS estimates are obtained through heteroskedasticconsistent standard errors. 5. Conclusion and policy implications This paper aims at estimating the possible impact of biomass consumption on GDP growth. Hence, paper observes the long run dynamics of real GDP by estimating the related panel data for G7 countries of Canada, France, Germany, Italy, Japan, the UK and the USA from 1980 to 2009. To this end, paper follows the production (yit) function in which capital stock (Kit), human capital index (Lit) and biomass consumption (Biomassit) are employed as explanatory variables. For this purpose, the panel unit root analyses, panel cointegration analyses, conventional OLS and dynamic OLS (DOLS) analyses are run throughout homogeneous and heterogeneous variance structures of the panel data. The homogeneous and heterogeneous findings reveal that the long run elasticities of panel real GDP data in terms of panel capital stock, panel human capital index and panel biomass consumption are significant and positive. Conventional homogeneous OLS estimations, for instance, indicate that 1% increase in independent variables will cause panel real GDP to increase by 0.575, 0.609 and 1.198 percent, respectively. Homogeneous adjusted OLS have close output with those of conventional OLS. Homogeneous DOLS estimations yield that panel capital stock, panel human capital index and panel biomass consumption have the impacts of 0.082, 0.521 and 1.483 on panel real GDP, respectively. Heterogeneous DOLS estimations provide the GDP elasticities of 0.689, 0.383 and 1.149 with respect to of panel capital stock, panel human capital index and panel biomass consumption, respectively. Overall, by considering all tests conducted within the text, one may claim that there is significant and substantive influence of biomass consumption on GDP growth. In other words, biomass energy consumption plays a vital role in the economic growth process directly and/or as a complement to capital and labor. Therefore, growth hypothesis is supported in G7 countries, which means that there is uni-directional causality from biomass energy consumption to economic growth. In this case, energy conservation

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