ECONOMIC GROWTH AND ENERGY

19th EBES CONFERENCE - ISTANBUL PROCEEDINGS CD - Volume 2

May 26-28, 2016, Istanbul, TURKEY

ECONOMIC GROWTH AND ENERGY CONSUMPTION FOR OECD COUNTRIES Hasan Hüseyin YILDIRIM1 1

Lecturer, University of Balikesir, Burhaniye School of Applied Sciences, Department of Banking and Finance, Abstract Following the 19th century Energy became an important and indispensable input to production and consumption activities in all over the world. In the meantime Energy has become a very determinant factor for growth for national economies. In this study we aim to investigate the relationship between economic growth and energy consumption for OECD countries. Panel data method and co-integration tests will be employed to analyze OECD member countries over the period 1960-2014. GDP per capita will be the proxy for the economic growth and energy consumption per capita will be taken for energy consumption on yearly basis. INTRODUCTION Energy plays a vital role in the process of economic growth and development of a nation. In the absence of sufficient energy industry, transport and social life are subject to fail(Chang et al., 2014). Energy products constitute the largest cost item in the production processes in the modern economy. Being unable to meet energy demand due to population growth, industrialization and rising living standards can cause social, political and military conflicts. Global primary energy consumption has poorly increased by just 0.9% in 2014, after a marked (slow down) deceleration over 2013 (+2.0%) and well below the 10-year average of 2.1%. Growth in 2014 slowed for every fuel other than nuclear power, which was also the only fuel to grow at an above-average rate. Growth was significantly below the 10-year average for Asia Pacific, Europe & Eurasia, and South & Central America. OECD consumption fell by 0.9%, which was a larger fall than the recent historical average.

1227



Table 1: World Primary Energy Consumption (2014)

Region Total North America Total S.& Cent. America Total Europe & Eurasia Total Middle East Total Africa Total Asia Pacific Total World Percent (%)

Oil (Mt)

Natural Gas (Mtoe)

Coal (Mtoe)

1024.40 326.50 858.90 393.00 179.40 1428.90 4211.10 0.33

866.30 153.10 908.70 418.60 108.10 610.70 3065.50 0.24

488.90 31.60 476.50 9.70 98.60 2776.60 3881.80 0.30

Primary Energy Consumption 2014 Nuclear HydroRenewEnergy Electricity ables (Mtoe) (Mtoe) (Mtoe) 216.10 153.50 73.60 4.70 155.40 21.50 266.10 195.70 124.40 1.00 5.20 0.30 3.60 27.50 2.90 82.50 341.60 94.20 574.00 879.00 316.90 0.04 0.07 0.02

Total

Percent (%)

2822.60 692.80 2830.30 827.90 420.10 5334.60 12928.40

0.22 0.05 0.22 0.06 0.03 0.41

Table 1 shows us the distribution of primary energy consumption in 2014 (BP, 2015). We can see the utilization of primary energy sources for the regions shows proportions of 22% in North America, 5% in S. & Cent. America, 23% in Europe & Eurasia, 6%, in Middle East,%3 in Africa, 41% in Asia Pacific region. In 2014, oil which is the largest share of the world consumption of primary energy resources constituted 33% of whole consumption. Oil consumption is followed by coal, which is 30% of the world consumption. Natural gas is the third with 24% of primary energy consumption. 87% of world primary energy consumption is supplied by these three energy items, as a fossil source of energy. The use of energy as a global commodity in the process of economic growth and development is not only necessary but highly significant. Energy plays a crucial part in promoting an economic system on demand and supply side. Where, on the demand side, energy being an important product of consumers, they decide to purchase it in order to maximize their utility. While, on the supply side, energy is an essential factor of production in addition to capital, labor and materials and it seems also to play a predominant part in the economic and social growth of rural areas (Azam et al., 2015:734). The study further added that all these suggest that there should be a causal linkage running from energy consumption to Gross Domestic Product (GDP) as well as vice versa. The importance of energy as an input in the growth and development process gained more prominence following the oil price increases in 1973/74 and 1978/79 than at any other time. Subsequently, a significant volume of research-most of it in developed countries, primarily because of the large role energy and energy based inputs play in their production processes has investigated the relationship between energy and economic growth (Reddy, 1998).

1228



The literature concerning the relationship between energy consumption and economic growth has led to the emergence of two opposite views. One point of view suggests that energy use is a limiting factor to economic growth. The other point of view suggests that energy is neutral to growth. This is known in the literature as the ‘neutrality hypotheses’ which proposes that the cost of energy is a small proportion of GDP, and so it should not have a significant impact on output growth. It has also been argued that the possible impact of energy use on growth will depend on the structure of the economy and the stage of economic growth of the country concerned. As the economy grows its production structure is likely to shift towards services, which are not energy-intensive activities (Mehrara, 2006). Günümüzde enerji tüketimi ile büyüme arasındaki ilişkiyi inceleyen pek çok çalışma bulunmaktadır. Bu ilişki çalışmaların bir kısmında sadece ülke bazında ele alınırken bir kısmında da birden fazla ülke grupları arasında ele alınmıştır. Bu çalışmada 1962-2012 yılları arasında Austria, Belgium, Canada, Denmark, France, Germany, Greece, Iceland, Ireland, Lexembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom, United Stated and Italy içeren OECD üyesi olan 20 ülkeye ait yıllıkkişi başı GSYİH ve kişi başı elektrik enerjisi tüketimi verileripanel veri analizi ile test edilmiştir. This study consists of five chapters. The first part is about global energy outlok and the importance of relation between economic growth and energy consumption forcountries. The second part is literature section of the economic growth and energy consumption. In the third part of the study, data sets and methods of the study in the methodology section are described. In the fourth section of the study empirical outputs gathered by the chosen method are presented. In the fifth chapter of this study, the results are conluded. LITERATURE REVIEW Relationship between economic growth and energy consumption has been widely studied in recent literature (Pablo-Romero and De Jesus, 2016). About this matter was found at least 51 empirical studies on the relationship between Gross Domestic Product (GDP) growth and energy consumption in the last two decades (Menegaki, 2014). The study of this relationship has received new attention from the environmental perspective. This dynamic relationship between economic growth, energy consumption and environmental pressure has been studied for different regions and countries. Among those 51 studies on the first relationship

1229



maybe cited those for Europe (Acaravci and Ozturk,2010; Kasman and Duman, 2015), Latin America and the Caribbean (LAC) (Chang and Carballo, 2011), BRIC (Pao and Tsai,2011), MENA (Arouri et al.,2012; Omri, 2013), ASEAN (Saboori and Sulaiman, 2013), OECD (Saboori et al.,2014) and Turkey (Bozkur and Akan, 2014)among others. Knowledge of this relationship between economic growth and energy is considered to be extremely important for the development of effective energy and environmental policies to promote sustainable development. The literature offers inconsistent results on the relationship between energy consumption and economic growth after the pioneering work of Kraft and Kraft (1978). As pointed out by Toman and Jemelkova (2003), the lack of consensus may be due to the heterogeneity in climate conditions among countries, the changing energy consumption patterns, the structure and stages of economic development within a country and among countries, the alternative econometric methodologies employed, the presence of omitted variable bias, and varying time horizons of the studies conducted. Lee and Chang (2008) and Lee et al. (2008) employed panel cointegration technique to examine the relationships among energy consumption, GDP, and capital in 16 Asian and 22 OECD countries over 3 and 4 decades, respectively. Lee and Chang (2008) found a long-run causal relationship from energy consumption to GDP while Lee et al. (2008) suggested a bidirectional relationship in the OECD sample. Taken together, the inconclusive results of these studies are possibly due to the omission of non-energy inputs. By contrast, in a recent bivariate panel data study, Joyeux and Ripple(2011) found causality flowing from output to energy consumption for 56 developed and developing economies and Chontanawat et al. (2008) found causality running from energy consumption to GDP and stated that this result is more valid in the developed OECD countries compared with the developing non-OECD countries in a group of 100 countries. Through a panelvector error correction model consisting of GDP, energy consumption, and energy prices for 26 OECD countries, Costantini and Martini (2010) found that in the short run, there is bidirectional causality found between energy consumption and real GDP. However, in the long run, they found that real GDP growth drives energy consumption. Belke et al. (2001) indicated the presence of a bidirectional causal relationship between energy consumption and economic growth for 25 OECD countries. Recent studies of Chiou-Wei et al. (2008), Huang et al. (2008) and Fallahi

1230



(2011), among others, imply that the interrelationship between energy consumption and economic variables might be inherently non-linear. The irreconcilable findings of empirical studies make it troublesome to suggest a certain policy recommendation for OECD countries. Most previous studies did not consider the changing of causality direction, which may be due to such as business cycles, wage rates, energy crises, and structural reforms as stated by Fallahi (2011) and this creates room for a frequency-based rather than a conventional causality analysis between energy consumption and economic growth. METHODOLODOGY Data Set Data set used in this study includes the period 1962-2012 and analyses have been conducted by using twenty EOCD countries and 51 observations on annually data basis. Closing data pertain to GDP per capita and Electric power consumption (kWh per capita). Both data have been obtained from World Bank data base. Excel 2010 and E-Views 8.0 package programs have been used for processing the data and implementation of econometric analyses. Methods Panel data analyses embody information across both time and space. By using this approach we can bring to light the expected values and relationship between macroeconomic variables. Importantly, a panelkeeps the same objects or entities and measures some quantity about them over time. Therefore we can put together observations for individuals, countries, firms and other entities for a specific period of time. (Tatoğlu, 2012: 2). Economic data are often non-stationary or have means, variances and covariances that change over time. A statistical analaysis for a time series should be done whether it has a constant mean over time. The use of non-stationary data can lead to spurious regressions. If twovariables are non-stationary, a regression of one on the other couldhave a high R2 even if the two are totally unrelated. So, if standardregression techniques are applied to nonstationary data, the end resultcould be a regression that looks good but fundamentally they are valueless.Such a model would be termed a spurious regression.

1231



Recent literature suggests that panel-based unit root tests have higher power than unit root tests based on individual time series1. While these tests are commonly termed “panel unit root” tests, theoretically, they are simply multiple-series unit root tests that have been applied to panel data structures (where the presence of cross-sections generates “multiple series” out of a single series). These tests can be done for multiple series. In this sudy, Levin, Lin and Chuunit root tests and Fisher ADF and Fisher Philips and Perron panel unit root tests are employed. Levin, Lin and Chu (2002) panel unit root test assumes that each unit has the same autoregressive parameter. In other words, Levin, Lin, and Chu propose a test which has an alternative hypothesis that the ρ i are identical. Because ρ i is fixed across i, this is one of the most complicated of the tests because the data from the different individuals need to be combined into a single final regression. Three models can be applied: (1) (2) (3)

The Fisher-ADF and PP panel unit root tests allow for individual unit root processes so that ρ i may vary across cross-sections. The tests are all characterized by the combining of individual unit root tests to derive a panel-specific result. Panel unit root tests are similar, but not identical, to unit root tests carried out on a single series. Fisher tests use unit root tests for each entity and then p-values obtained from these tests constitute the basis for executing the whole test. On the hand, Fisher ADF and FisherPhilips-Perron tests are executed for each entity.

1

Levin, Lin and Chu (2002), Breitung (2000), Im, Pesaran and Shin (2003), Fisher-type tests using ADF and PP tests (Maddala and Wu (1999) and Choi (2001)), and Hadri (2000).

1232



Levin, Lin, and Chu tests assume that there is a common unit root process so that ρ i is identical across cross-sections. Under the null hypothesis, there is a unit root, while under the alternative, there is no unit root.The LLC method requires a specification of the number of lags used in each cross-section ADF regression. On top of it, the exogenous variables used in the test equations must be specified. There is an option to include no exogenous regressors, or to include individual constant terms (fixed effects), or to employ individual constants and trends. An alternative approach to panel unit root tests uses Fisher’s results to derive tests that combine the p-values from individual unit root tests. This idea has been proposed by Maddala and Wu, and by Choi.The exogenous variables for the test equations and the number of lags used in each cross-section ADF regression must be specified for Fisher tests.Since Fisher tests allows us to use unbalanced panel data they are more flexible. Once you have been able to classify your variables as stationary we are in position to sort out long-run and short-run effects in your model, and to set up a model where statistical inference will be meaningful. The extensive interest in and the availability of panel data has led to an emphasis on extending various statistical tests to panel data. Recent literature has focused on tests of cointegration in a panel setting. (Pedroni 1999, Pedroni 2004, Kao 1999 and a Fisher-type test using an underlying Johansen methodology Maddala and Wu 1999). Pedroni (1999) extended his panel cointegration testing procedure for the models, where there are more than one independent variable in the regression equation. Basically, it employs four panel statistics and three group panel statistics to test the null hypothesis of no cointegration against the alternative hypothesis of cointegration. In the case of panel statistics, the first-order autoregressive term is assumed to be the same across all the cross sections, while in the case of group panel statistics the parameter is allowed to vary over the cross sections. If the null is rejected in the panel case, then the variables of the production (labor productivity) function are cointegrated for all the sectors. On the other hand, if the null is rejected in the group panel case, then cointegration among the relevant variables exists for at least one of the sectors. (A Panel Unit Root and Panel Cointegration

1233



Test of the Complementarity Hypothesis in the Mexican Case, 1960-2001. ECONOMIC GROWTH CENTER YALE UNIVERSITY) The can be summarized as below: (4)

The Kao test follows the same basic approach as the Pedroni tests, but specifies crosssection specific intercepts and homogeneous coefficients on the first-stage regressors. Kao (1999) Panel Cointegration tests are based on DF (Dickey Fuller) and ADF(Augmented Dickey Fuller) tests. Under the null hypothesis there is no cointegration (

.

(5)

Based on the results obtained from cointegration analyses for panel data, panel casuality tests are employed. In order to perform a casuality test a Vector Error Correction Model can be predicted by using VAR.

EMPIRICAL RESULTS In this analysis, GDP per capita and electric power consumption per capita data have been used for 20 OECD member countries (Austria, Belgium, Canada, Denmark, France, Germany, Greece, Iceland, Ireland, Lexembourg, Netherlands, Norway, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom, United Stated and Italy) and all data have been obtained from World Bank data base. Panel data analysis has been employed to investigate whether there is a relationship between GDP per capita and electric power consumption in the countries mentioned above. Levin, Lin&Chu, ADF, PP (1979) panel unit root tests have been conducted for the set wovariables to see whether they are sationary or not.

1234



Table2: Results of Unit Root Test (Level Values) Unit Root Test Type

Levin, Lin &Chu

ADF- Fisher Chisquare

PP-Fisher Chi-square

Variables Include in Test Equation

GDP per capita (current US$) 4.786 (1.000) -1.143 (0.126) 3.318 (1.000) 40.945 (0.428) 1.381 (1.000) 25.598 (0.962)

Individual intercept Individual intercept and trend Individual intercept Individual intercept and trend Individual intercept Individual intercept and trend

Electric power consumption (kWh per capita -6.111 (0.000) 7.047 (1.000) 75.671 (0.006) 14.802 (0.999) 77.081 (0.004) 10.662 (1.000)

In Table 2 above stationarity level results for two variables are shown. Under these hypotheses, H 0 : Variable is non-stationary. There is unit root. H 1 : Variable is stationary. There is not unit root. the null hypothesis cannot be rejected for GDP per capita with constant, with constant and trend at 1% significance level. Moreover, for electric power consumption variable the null hypothesis cannot be rejeceted with constant and trend at 1% significance level. Since they are not stationary, stationarity process has to be employed. Table3: Results of UnitRoot Test (First Difference) Unit Root Test Type

Levin, Lin &Chu

ADF- Fisher Chisquare

PP-Fisher Chi-square

Variables Include in Test Equation Individual intercept Individual intercept and trend Individual intercept Individual intercept and trend Individual intercept Individual intercept and trend

1235

GDP per capita (current US$) -16.608 (0.000) -17.062 (0.000) 341.99 (0.000) 297.87 (0.000) 385.33 (0.000) 333.29 (0.000)

Electric power consumption (kWh per capita -9.152 (0.000) -12.340 (0.000) 191.89 (0.000) 240.15 (0.000) 458.78 (0.000) 486.24 (0.000)



When we take the first difference, both variables become stationary at 1% significance level as shown in Table 3.

At this stage of the study a cointegration test has been conducted to see the relationship between two variables. The results are shown in Table 4 below: Table 4: Panel Cointegration Test Results

Test Type

Pedroni (EngleGrangerBased)

Kao(EngleGrangerBased)

Test Statistics

GDP per capita Dependent Variable

Panel v- Statistic Panel rho- Statistic Panel PP- Statistic Panel ADF- Statistic Grup rho- Statistic Grup PP-Statistic Grup ADF- Statistic

Statistic -0.6666 -26.379 -18.218 -16.949 -20.874 -18.438 -16.438

Prob. 0.7475 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000

Electric Power Consumption per capita Dependent Variable Statistic Prob. -2.0515 0.9799 -26.392 0.000 -18.293 0.000 -17.696 0.000 -26.664 0.000 -21.488 0.000 -16.395 0.000

ADF

3.0050

0.0013

8.8417

0.000

Under the null and alternative hypotheses, H 0 : There is no cointegration H 1 : There is cointegration Pedroni test statistics confirms with majority that the cointegration relationship exists. (6 out of 7 test statistics). Kao coinintegration test statistics also confirm the same results at 5 % significance level. In other words, the analysis affirms that GDP per capita in OECD countries and electric power consumption per capita are cointegrated in the long run and they move together. Granger causality test results are shown below in Table 5. Tablo 5: Granger Causality- Block Exogeneity Wald Test Results Dependent Independent First Difference of First Difference of Electric GDP per capita power consumption Models (current US$) (kWh per capita) First Difference of Electric First Difference of power consumption GDP per capita (kWh per capita) (current US$)

Chi-Square

Prob.

Direction

52.9706

0.0000

Unidirection

80.3349

0.0000

Unidirection

When we examine the results we conclude that two variables are affecting each other. GDP per capita causes Electric Power consumption and the vice versa is also valid. According to

1236



this result, if GDP "Granger-causes" electric power consumption, then past values of GDP should contain information that help spredict electric consumption above and beyond the information contained in past values of electric power consumption per capita alone. CONCLUSION There are many research papers on searching the relationship between energy consumption and economic growth with mixed findings. Some of them support and claim that energy consumption leads an economic growth, and many other analyses claim that economic growth leads energy consumption. In some researhes we find a bilateral relationship betweeen the set wovariables. In this study we investigate the relationship between economic growth and energy consumption for OECD countries. Panel data method and co-integration tests have been employed. At the first stage of the study the stationarity transformation has been made by taking the first difference of the variables and then cointegration and Granger causality tests are applied to GDP and energy consumption data for 20 OECD country over the period 19602014. GDP andenergyconsumptionpercapitavariablesarecointegratedandGrangercauseeachother. First conclusion would be energy consumption per capita affects GDP per capita. In other words, an increase or decrease in energy consumption will increase or decrease GDP. Energy consumption plays an important role on economic growth, directly on labor and capital component and indirectly on production process as well. Electric consumption is an incentive factor and indispensable insurance for a sustainable economic growth. Second conclusion would be GDP per capita affects energy consumtion per capita. In other words, an increase or decrease GDP per capita will increase or decrease energy consumption per capita. Developing countries that are increasing their aggregate GDP and their production are subject to demand more and more energy sources. Counries that are in short providing the appropriate energy demand will be importing energy. This might eventually cause current and trade deficit.

1237



Another result of the increase in energy consumption due to the economic growth is that it come swith environmental problems. The exploitation of fosil fuels as a source of energy consumption increases corbon emissions. The largest contribution to the provision of energy demand without increasing carbon emissions will be provided by renewable energy sources. While developing and developed countries are making economic progress, they should consider using renewable energy sources, so that they will at least reduce most costly environmental problems.

REFERENCES Acaravci, A., & Ozturk, I. (2010). On the relationship between energy consumption, CO 2 emissions and economic growth in Europe. Energy, 35(12), 5412-5420. Arouri, M. E. H., Youssef, A. B., M'henni, H., & Rault, C. (2012). Energy consumption, economic growth and CO 2 emissions in Middle East and North African countries. Energy Policy, 45, 342-349. Azam, M., Khan, A. Q., Bakhtyar, B., & Emirullah, C. (2015). The causal relationship between energy consumption and economic growth in the ASEAN-5 countries. Renewable and Sustainable Energy Reviews, 47, 732-745. Belke, A., Dobnik, F., Dreger, C., 2001. Energy consumption and economic growth: new insights into the cointegration relationship. Energy Economics 33(5), 782–789. Bozkurt, C., & Akan, Y. (2014). Economic growth, CO2 emissions and energy consumption: the Turkish case. International Journal of Energy Economics and Policy, 4(3), 484. BP, 2015, Statistical Review of World Energy June 2015, https://www.bp.com/content/dam/bp/pdf/ energy-economics/statistical-review-2015/bp-statistical-review-of-world-energy-2015-full-report.pdf Chang, C. C., & Carballo, C. F. S. (2011). Energy conservation and sustainable economic growth: The case of Latin America and the Caribbean. Energy Policy,39(7), 4215-4221. Chiou-Wei, S.Z., Chen, Ching-Fu, Zhu, Z., 2008. Economic growth and energy consumption revisitedevidence from linear and nonlinear Granger causality. Energy Economics 30(6), 3063–3076.

1238



Chontanawat, J., Hunt, L.C., Pierse, R., 2008. Does energy consumption cause economic growth? Evidence from asystematic study of over 100 countries. Journal of Policy Modeling 30,209–220. Costantini, V., Martini, C.,2010. The causality between energy consumption and economic growth: a multi-sectoral analysis using non-stationary cointegrated panel data. Energy Economics 32,591–603. Fallahi, F., 2011. Causal relationship between energy consumption (EC) and GDP: a Markov-switching (MS) causality. Energy 36, 4165–4170. Huang, B.N., Hwang, M.J., Yang, C.W., 2008. Does more energy consumption bolster economic growth? An application of the nonlinear threshold regression model. Energy Policy36,755–767. Joyeux, R., Ripple, R.D.,2011. Energy consumption and real income: a panel cointegration multicountry study. Energy Journal 32(2), 107–141. Kasman, A., & Duman, Y. S. (2015). CO2 emissions, economic growth, energy consumption, trade and urbanization in new EU member and candidate countries: a panel data analysis. Economic Modelling, 44, 97-103. Kraft,J.,Kraft,A.,1978.On the relationship between energy and GNP .Journal of Energy and Development 3,401–403. Lee, C.C.,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. Lee, C.C.,Chang,C.P.,Chen,P.F., 2008. Energy-income causality in OECD countries revisited: the key role of capital stock. Energy Economics 30, 2359–2373. Mehrara, M. (2007). Energy consumption and economic growth: the case of oil exporting countries. Energy policy, 35(5), 2939-2945. Menegaki, A. N. (2014). On energy consumption and GDP studies; A meta-analysis of the last two decades. Renewable and Sustainable Energy Reviews,29, 31-36. Omri, A. (2013). CO 2 emissions, energy consumption and economic growth nexus in MENA countries: evidence from simultaneous equations models.Energy economics, 40, 657-664

1239



Pablo-Romero, M. D. P., & De Jesús, J. (2016). Economic growth and energy consumption: The Energy-Environmental Kuznets Curve for Latin America and the Caribbean. Renewable and Sustainable Energy Reviews, 60, 1343-1350. Pao, H. T., & Tsai, C. M. (2011). Modeling and forecasting the CO 2 emissions, energy consumption, and economic growth in Brazil. Energy, 36(5), 2450-2458. Reddy, M. (1998). Energy consumption and economic activity in Fiji. Journal of Pacific Studies, 22(1), 81-96. Saboori, B., & Sulaiman, J. (2013). CO 2 emissions, energy consumption and economic growth in Association of Southeast Asian Nations (ASEAN) countries: a cointegration approach. Energy, 55, 813822. Saboori, B., Sapri, M., & bin Baba, M. (2014). Economic growth, energy consumption and CO2 emissions in OECD (Organization for Economic Co-operation and Development's transport sector: A fully modified bi-directional relationship approach. Energy, 66, 150-161. Soytas, U., Sari, R., 2003. Energy consumption and GDP: causality relationship in G-7 countries and emerging markets. Energy Economics 25 (1), 33–37. Tatoğlu, Ferda Yerdelen. “Panel veri ekonometris: stata uygulamalı”. Beta Basım Yayın, 2012. Toman, M. A., & Jemelkova, B. (2003). Energy and economie development: An assessment of the state of knowledge. The Energy Journal, 93-112. Toman,T., Jemelkova, B.,2003. Energy and economic development: an assessment of the state of knowledge. Energy Journal 24,93–112.

1240