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The effect of economic growth on energy efficiency: Evidence from high, upper-middle and lower-middle income countries
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Procedia Computer Science 158 (2019) 523–532
3rd World Conference on Technology, Innovation and Entrepreneurship (WOCTINE) 3rd World Conference on Technology, Innovation and Entrepreneurship (WOCTINE)
The effect of economic growth on energy efficiency: Evidence The effect of economic growth on energy efficiency: Evidence from high, upper-middle and lower-middle income countries from high, upper-middle and lower-middle income countries 1
Sefer Sener11**, Ahmet Tarik Karakas22 Sefer Sener , Ahmet Tarik Karakas
Istanbul University, Faculty of Economics, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey Sciences, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey IstanbulUniversity, University,Institute Facultyof ofSocial Economics, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey 2 Istanbul University, Institute of Social Sciences, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey 21 Istanbul
Abstract Abstract In this paper, we examine the effect of economic growth on energy efficiency using panel data analysis for high, upper-middle and Inlower-middle this paper, weincome examine the effect of economic growth1995-2016. on energy efficiency using data analysis high, upper-middle and countries between the periods Six models are panel established for twoforeconomic levels, namely lower-middle income countries between the (GDP) periods–economy 1995-2016. modelsand areenergy established for is two economicaslevels, namelyof industry sector and gross domestic product as Six a whole–, intensity calculated an indicator industry sector and Because gross domestic product (GDP)between –economy as a whole–, intensitywhich is calculated an indicator of energy efficiency. of strong interaction countries, we useand testsenergy and methods take intoasaccount the cross energy efficiency. Because of strongour interaction between countries, we use tests and methods which and taketointo account the cross sectional dependence. We identify hypothesis as “economic growth decreases energy intensity” prove this hypothesis sectional dependence. We identify ourAugmented hypothesis as “economic growth decreases energy intensity” to prove is this hypothesis we implement cointegration test and Mean Group estimator. Our findings show that theand hypothesis valid for high we cointegration test andgroups. Augmented estimator. Our is findings show that the hypothesis is valid forgroup. high andimplement upper-middle income country On theMean otherGroup side, our hypothesis rejected for lower-middle income country and income country groups. On the other side,economic our hypothesis rejected lower-middle income group. Ourupper-middle findings suggest that the negative relationship between growthisand energyfor intensity increases as thecountry countries reach Our findings suggest thattothe negative relationship between economic growth and energy intensity increases as the countries reach from low income level high. from low income level to high. © 2019 The Author(s). Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. ©Peer-review 2019 The Author(s). Published byofElsevier B.V. committee of the 3rd World Conference on Technology, Innovation and under responsibility the scientific Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Entrepreneurship Entrepreneurship Entrepreneurship Keywords: Economic growth, energy efficiency, energy intensity, panel data analysis Keywords: Economic growth, energy efficiency, energy intensity, panel data analysis
1. Introduction 1. Introduction Energy is one of the most important sources for social and economic development. Obtaining energy in a safe, Energy is high one of the most important sources for social and economic Obtaining in a levels safe, continuous, quality, efficient and environment-friendly way increasesdevelopment. the living conditions andenergy prosperity continuous, quality, efficienteconomic and environment-friendly wayactivities increases the be living conditions and prosperity levels of countries.high In order to achieve growth, production must carried out continuously. Therefore, ofsafe countries. In order to achieve economic growth, productionactivities activitieshave mustgreat be carried out continuously. and inexpensive energy supply required for production importance for economicTherefore, growth. safe and inexpensive energy supply required for production activities have great importance for economic growth. * Corresponding author. E-mail address: [email protected] * Corresponding author. E-mail address: [email protected] 1877-0509 © 2019 The Author(s). Published by Elsevier B.V. Peer-review responsibility ofPublished the scientific of the 3rd World Conference on Technology, Innovation and Entrepreneurship 1877-0509 © under 2019 The Author(s). by committee Elsevier B.V. Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Entrepreneurship
1877-0509 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Entrepreneurship 10.1016/j.procs.2019.09.084
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Energy resources are vary depending on geographical position of the countries. Countries which are rich or poor in terms of their energy resources are affected at different levels economically and politically in the event of an energy crisis. High unemployment, recession and uncertainty in the expectations, which are emerging more with the energy crises, create a risky environment for the countries all over the world. Therefore, properly implementation of energy policies for sustainable development and growth depends on safe, efficient, continuous and environment friendly energy supply. In addition to supplying the required energy within the scope of these criteria, countries have developed a number of new strategies on energy use. The concept of energy efficiency is one of the strategies put forwarded on energy use and this concept is based on the using less energy or supplying less costly to product same amount goods or services. In this context, considering the close relationship between energy policies and economic activity, associate economic growth to energy efficiency constitutes the importance of this paper. Energy intensity is one of the most important indicators of energy efficiency and it is formulated as the amount of energy consumed per economic output. In addition to this, it is a common tool used for monitoring and comparing energy efficiency between countries. Energy intensity varies depending on factors such as energy consumption, GDP and technology. In today’s world, level of development a country in terms of energy use is determined by energy consumption per capita and energy intensity. In this context, aim of this study is investigate the effect of economic growth on energy intensity. In order to achieve this aim, we create a hypothesis that “economic growth decreases energy intensity”, and we examine this hypothesis in high, upper-middle and lower-middle-income country groups for GDP and industry sector economic levels. This paper is organized as follows. In section 2, we present the empirical literature on the relationship between economic growth and energy intensity. In section 3, we describe the model and econometric methodology used in the study. And later, we examine the relationship between economic growth and energy intensity in this chapter. 2. Literature review In the literature, studies investigating the relationship between energy and economic growth started with oil crises in 1973-1974 and 1978-1979. In these studies, usually the relationship between energy consumption and economic growth was investigated. Since 1990s, the claim that fossil energy consumption causes global warming has shifted the focus of energy studies to carbon dioxide emission and energy efficiency. On the other hand, the number of studies examining the relationship between energy efficiency and economic growth is limited in the literature. Hanley et al. [1] investigate the economic and environmental impacts of energy efficiency using the Regional Computable General Equilibrium model for Scotland. In the study using data of 1999, the short, medium and long run effects of 5% energy efficiency increase in all economic factors on GDP were estimated. According to findings of the analysis, GDP will increase 0.06% in the short term, 0.10% in the medium term and 0.88% long term, as a result of 5% energy efficiency increase. Their findings suggest that Scottish economy could earn an extra growth increase in energy efficiency. Allan et al. [2] investigate the impact of energy efficiency on economic growth in United Kingdom using Economy-Energy-Environment Computable General Equilibrium (CGE) method. Short and long term effects of 5% energy efficiency increase in industry sector on GDP were demonstrated by using the input-output tables created for the year 2000. The analysis results show that when energy efficiency increases by 5%, GDP will increase 0.11% in the short term and 0.17% in the long term. Bataille and Melton [3] search the impact of energy efficiency on economic growth in Canada using annual data between the periods 2002 and 2012. Regional General Equilibrium Energy Model (RGEEM) was used in the study and it was estimated that the improvements in energy efficiency increased the GDP by 2% (0.19% a year). Choi, Park and Yu [4] explore the relationship between energy efficiency and firm growth in 21 sub-branches of manufacturing industry for France, Japan, Germany, South Korea, United Kingdom and United States using microlevel company panel data between the periods 1992 and 2005. They used energy intensity (EI) as an indicator of energy efficiency. In addition to this, they also calculated relative energy intensity (REI) and included in the analysis. According to the findings of the analysis, while firm profitability increased with low REI, there was no relationship
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between low EI and firm profitability. Deichmann et al. [5] examine the relationship between energy intensity and economic growth for 137 countries between the periods 1990 and 2014 using yearly panel data set. Using Flexible Piecewise Linear Regression method, they found a negative relationship between energy intensity and economic growth. This result shows that as economies of low-income countries grow, there will be a rapid improvement in their energy intensities. Another finding obtained from the study is that energy efficiency policies will have more critical importance, when low income countries exceed lower-middle income level. Rajbhandari and Zhang [6] analyse the impact of energy efficiency on economic growth using Panel Vector Autoregression (PVAR) for high and middle-income countries between the periods 1978 and 2012. According to findings of the analysis, they found unidirectional long-run causality from economic growth to lower energy intensity for all country groups. On the other hand, they found bidirectional long-run causality between energy intensity and economic growth for lower-middle income country group. Based on the results of the analysis, they emphasized that energy efficiency could contribute to GDP in the long-run. Mahmood and Ahmad [7] examine the relationship between energy intensity and economic growth using Least Squares with Dummy Variable (LSDV) and General Method of Moments (GMM) for the years 1995-2015 in 19 European Union member countries. They proved a negative relationship between energy intensity and economic growth and interpreted the reason of low energy intensity as the transition from the industrial sector to the less energy intensive sectors. 3. Data description, methods and findings In this paper, we investigate the effect of economic growth on energy efficiency for the years 1995-2016 in high, upper-middle and lower-middle income countries. We include 23 high, 22 upper-middle and 17 lower-middle income countries (62 in total) to the analysis. Table 1 presents country groups selected for our study by income level. Table 1. Countries used in the study Country groups
Countries
High income
Australia, Austria, Belgium, Denmark, Finland, France, Germany, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, United Kingdom, United States
Upper middle income
Argentina, Azerbaijan, Brazil, Bulgaria, China, Colombia, Costa Rica, Croatia, Hungary, Iran, Iraq, Lebanon, Malaysia, Mexico, Poland, Romania, Russian Federation, Serbia, South Africa, Thailand, Turkey, Venezuela
Lower middle income
Bangladesh, Cameroon, Egypt, India, Indonesia, Kyrgyzstan, Moldova, Morocco, Nigeria, Pakistan, Philippines, Senegal, Sri Lanka, Sudan, Tunisia, Ukraine, Uzbekistan
We use energy intensity as an indicator of energy efficiency. Energy intensity is calculated by dividing the amount of energy consumed to economic output. The model used in the calculation of energy intensity is given in Equation 1. 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑜𝑜𝑜𝑜 𝐺𝐺𝐺𝐺𝐺𝐺) =
𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝑜𝑜𝑜𝑜 𝐺𝐺𝐺𝐺𝐺𝐺) 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 (𝑜𝑜𝑜𝑜 𝐺𝐺𝐺𝐺𝐺𝐺)
(1)
In the study, we set up a models based on two different economic levels, namely industrial sector and GDP (economy as a whole). The reason for such a distinction is that industry sector and GDP have different structures in terms of energy intensity. Therefore, we decided to set up a different model -6 in total- for each economic level and country group. In order to obtain energy intensity data, industrial sector and total energy consumption data are taken from International Energy Agency [8]; GDP and industrial sector value added (IVA) data are taken from World Bank (World Development Indicators) database [9]. In order to standardize the variables, we take natural logarithms of them. Table 2 presents the variables and their unit values.
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Table 2. Variables and unit values used in the analysis Abbreviation
Variable (unit value)
LNTEI
Total Energy intensity (MJ/constant 2010 US$ GDP)
LNGDP
Gross domestic product (constant 2010 US$)
LNIEI
Industry energy intensity (MJ/constant 2010 US$ IVA)
LNIVA
Industry value added (constant 2010 US$)
Panel data in econometric applications are preferred because they can work with more complex behavior models and can enrich empirical analysis in ways that may not be possible if we use only cross section (one year, one month etc.) or time series data (one country, one firm etc.) [10]. Taking these advantages into account, we think that panel data analysis is right method for our model including 62 countries. The wide method framework of panel data allows the implementation of many estimators suitable with purpose of the study and structure of variables. In order to determine which panel data estimators will be used, firstly, we test whether there is a cross section dependence and unit root or not. 3.1. Cross sectional dependence and unit root test in panel data Unit root tests are divided into two groups as first and second generation unit root tests with the assumption that whether there is a relationship between cross sections or not. First generation panel unit root tests such as Maddala and Wu [11], Choi [12], Levin, Lin and Chu [13] assume that there is no cross sectional dependence. Second generation unit root tests such as Phillips and Sul [14], Bai and Ng [15], Moon and Perron [16], Pesaran [17] assume that there is cross sectional dependence in panel. Therefore, as a first step we prove whether cross sectional dependence in panels or not. When working with macroeconomic or financial data, cross-section dependence should be taken into account because of strong interaction between countries [18]. In the global world, acting on the assumption that macroeconomic variables do not affect each other reduces the reliability of the analyses. Cross sectional dependence is investigated according to number of countries (N) and years (T). When N>T, Friedman [19] and Pesaran [20] tests can be used for examine cross sectional dependence. On the other side, under T>N condition, Breusch-Pagan [21] test can be used. To examine cross sectional dependence, we firstly set up fixed-effects and random-effects models. Breusch-Pagan [21] test only in fixed-effect models; Friedman [19] and Pesaran [20] tests can be estimated in both fixed and random effects models. Form of fixed and random effect models for testing cross sectional dependence is as follows: 𝐿𝐿𝐿𝐿𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖
(2)
𝑖𝑖: 1, 2, … , 𝑁𝑁 (𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐) 𝑎𝑎𝑎𝑎𝑎𝑎 𝑡𝑡: 1, … , 22 (1995, … , 2016)
Table 3. Friedman [19], Pesaran [20] and Breusch-Pagan [21] cross sectional dependence test results High income
Upper-middle income
Lower-middle income
LNTEI
LNIEI
LNTEI
LNIEI
LNTEI
LNIEI
Friedman
154.3*
182.8*
16.62
29.65
Pesaran CD
24.47*
27.14*
0.038
0.737 1121.6*
722.02*
Fixed-effects regression
Breusch-Pagan Random-effects regression Friedman
187.1*
185.4*
12.32
16.04
Pesaran
29.65*
27.82*
-0.83
-1.32
Breusch-Pagan Note: The null hypothesis is that there is no cross sectional dependence.
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Table 3 indicates Friedman [19], Pesaran [20] and Breusch-Pagan [21] cross sectional dependence test results. Test results demonstrate a strong relationship between cross sections in high and lower-middle income country models. On the other hand, null hypothesis is accepted for upper-middle income countries and it is proven that there is no relationship between the cross sections. Based on cross sectional dependence test results, we carry out Pesaran [17] unit root test for high and lower-middle income country groups and Maddala and Wu [11] unit root test for uppermiddle income country group. Simple dynamic linear heterogeneous panel data model established for Pesaran [17] unit root test as follows: ∆𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑎𝑎𝑖𝑖 + 𝛽𝛽𝑖𝑖 𝑦𝑦𝑖𝑖,𝑡𝑡−1 + 𝛾𝛾𝑖𝑖 𝑓𝑓𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖
(3)
𝐻𝐻0 : 𝛽𝛽𝑖𝑖 = 0 for all 𝑖𝑖
(4)
𝐻𝐻1 : 𝛽𝛽𝑖𝑖 < 0, 𝑖𝑖 = 1,2, … . , 𝑁𝑁1 , 𝛽𝛽𝑖𝑖 = 0 𝑖𝑖 = 𝑁𝑁1 + 1, 𝑁𝑁1 + 2, … . , 𝑁𝑁
(5)
Where 𝑓𝑓𝑡𝑡 is the unobserved common effect, and 𝜀𝜀𝑖𝑖𝑖𝑖 is the individual-specific (idiosyncratic) error. The unit root hypothesis can be expressed as:
against the possibly heterogeneous alternatives,
Maddala and Wu [11] unit root test is described as the Fisher’s type test. Under the assumption of cross sectional independence, the statistic proposed by Maddala and Wu [11] defined as: 𝑁𝑁
𝑃𝑃𝑀𝑀𝑀𝑀 = −2 ∑ log(𝑝𝑝𝑖𝑖 )
(6)
𝑖𝑖=1
Table 4. Pesaran [17], Maddala and Wu [11] unit root test results LNGDP
∆LNGDP
LNIVA
∆LNIVA
LNTEI
∆LNTEI
LNIEI
∆LNIEI
Without trend
0.85
-8.77*
2.50
-10.6*
0.01
-15.1*
-1.52***
-14.2*
With trend
0.77
-6.43*
0.05
-8.87*
0.38
-14.9*
0.87
-13.1*
Without trend
18.68
208.6*
50.71
222.6*
36.90
343.8*
67.92**
478.3*
With trend
23.81
143.9*
130.8*
163.1*
63.61**
278.8*
111.7*
385.4*
Without trend
-1.29
-5.83*
1.40
-6.87*
-1.03
-10.4*
-2.12**
-13.6*
With trend
2.08
-5.38*
4.58
-6.86*
1.59
-9.05*
-1.32***
-11.6*
High income ( ) 1
Upper-middle income (2)
Lower-middle income (1)
Notes: ( ) Corresponding values to high and low-middle income country groups indicate CIPS statistics. ( ) Corresponding values to upper-middle income country group indicate chi-square statistics. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively. The null hypothesis indicates that the series is nonstationary. 1
2
Table 4 indicates that gross domestic product, industry value added, total energy intensity and industry energy intensity series have unit root. After taking first differences, we can say that the analysed variables are integrated of order one. 3.2. Panel cointegration test and coefficient estimation results While investigating the long-run relationship between variables in panel data, it is handled differently from the methods applied for time series or cross section data. If there is a relationship between the cross sections, the
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cointegration tests that ignore this situation cause misinterpretation of the test results. Therefore, we use Westerlund [22] cointegration test, which takes into account cross sectional dependence. Westerlund [22] presents two cointegration tests which are group statistics (𝐺𝐺𝑡𝑡 , 𝐺𝐺𝑎𝑎 ) and panel statistics (𝑃𝑃𝑡𝑡 , 𝑃𝑃𝑎𝑎 ). The process of obtaining Westerlund panel and group test statistics is carried out in three steps. These test statistics obtained in three steps are compared with critical values and decisions about cointegration relationship are made. The null hypothesis in Westerlund [22] is “there is no cointegration between the variables” and alternative hypothesis is “there is cointegration between the variables”. When cross sectional dependence is detected, Westerlund [22] suggests that the robust probability values obtained with bootstrap tests must be taken in consideration. We use robust probability values for high and lower-middle income country models due to cross sectional dependence and standard probability for upper-middle income country model. Table 5 presents Westerlund [22]cointegration test results. The results show that there is cointegration between the variables for high and upper-middle income country groups but there is no cointegration for lower-middle income country group. After proving the long run relationship between total energy intensity and gross domestic product, industry energy intensity and industry value added for high and uppermiddle income country groups, we can start to long run coefficient estimation. Table 5. Westerlund [22] cointegration test results High income
Upper-middle income
Lower-middle income
Gt
-3.21*
-5.44*
-1.58
Ga
-6.79*
-0.49
1.37
Pt
-2.66*
-5.23*
-0.90
Pa
-1.38
-6.81*
-0.82
Gt
-1.52*
-5.70*
-0.53
Ga
-2.21*
-1.79*
1.27
Pt
-1.65*
-5.25*
-2.00
Pa
-2.79*
-5.89*
-2.41
LNTEI-LNGDP model
LNIEI-LNIVA model
Notes: * denotes the statistical significance at 5% level. Standard probability values are used for lower-middle income country group.
We use Augmented Mean Group (AMG) method to estimate the effect of economic growth on energy intensity for selected country groups. We prefer this method because it is a modern estimator that takes into account cross sectional dependence and gives us both country specific coefficients and mean group coefficients. AMG estimator, developed by Eberhardt and Bond [23] uses Monte Carlo simulation. It takes into account cross sectional dependence by inclusion of a ‘common dynamic effect’ in the country regression. The model developed by Eberhardt and Bond [23] is as follows: 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽𝑖𝑖′ 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 𝑥𝑥𝑚𝑚𝑚𝑚𝑚𝑚 = 𝜋𝜋𝑚𝑚𝑚𝑚 +
𝑚𝑚 = 1, … , 𝑘𝑘
′ 𝛿𝛿𝑚𝑚𝑚𝑚 𝑔𝑔𝑚𝑚𝑚𝑚
𝑓𝑓𝑡𝑡 = 𝜑𝜑 ′ 𝑓𝑓𝑡𝑡−1 + 𝜀𝜀𝑡𝑡
𝑣𝑣𝑣𝑣
𝑢𝑢𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝜆𝜆′𝑖𝑖 𝑓𝑓𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖
+ 𝜌𝜌1𝑚𝑚𝑚𝑚 𝑓𝑓1𝑚𝑚𝑚𝑚 +. . . +𝜌𝜌𝑛𝑛𝑛𝑛𝑛𝑛 𝑓𝑓𝑛𝑛𝑛𝑛𝑛𝑛 + 𝑣𝑣𝑚𝑚𝑚𝑚𝑚𝑚
𝑣𝑣𝑣𝑣
𝑓𝑓.𝑚𝑚𝑚𝑚 ⊂ 𝑓𝑓𝑡𝑡
𝑔𝑔𝑡𝑡 = 𝜅𝜅 ′ 𝑔𝑔𝑡𝑡−1 + 𝜀𝜀𝑡𝑡
(7)
(8)
(9)
Where 𝑥𝑥𝑖𝑖𝑖𝑖 is a vector of observable covariates, 𝛼𝛼𝑖𝑖 is a combination of group-specific fixed effects, 𝑓𝑓𝑡𝑡 is a set of common factors and 𝜆𝜆𝑖𝑖 is country-specific factor loadings. In equation 5, 𝑚𝑚 = 1, … , 𝑘𝑘 is represents 𝑘𝑘 observable regressors, which are modelled as linear functions of unobserved common factors 𝑓𝑓𝑡𝑡 and 𝑔𝑔𝑡𝑡 , with country-specific factor loadings respectively. The model setup thus introduces cross section dependence in the observables and unobservables. Equation 6 indicates the progress of the unobserved factors. AMG estimator is carried out in two stages:
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AMG - Stage (1) AMG - Stage (2)
∆𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑏𝑏 ′ ∆𝑥𝑥𝑖𝑖𝑖𝑖 + ∑𝑇𝑇𝑡𝑡=2 𝑐𝑐𝑐𝑐 ∆𝐷𝐷𝑡𝑡 + 𝑒𝑒𝑖𝑖𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑎𝑎𝑖𝑖 +
𝑏𝑏𝑖𝑖′ 𝑥𝑥𝑖𝑖𝑖𝑖
+ 𝑐𝑐𝑖𝑖 𝑡𝑡 +
𝑑𝑑𝑖𝑖 𝑢𝑢̂𝑡𝑡•
+ 𝑒𝑒𝑖𝑖𝑖𝑖
529 7
⇒ 𝑐𝑐̂𝑡𝑡 ≡ 𝑢𝑢̂𝑡𝑡• ⇒ 𝑏𝑏̂𝐴𝐴𝐴𝐴𝐴𝐴 ≡ 𝑁𝑁 −1 ∑İ 𝑏𝑏̂İ
(10)
(11)
In the first stage, the model is estimated by taking first difference of variables and adding year dummy variable. The main purpose this process is ensure that nonstationary variables and unobservable effects do not cause errors. In the second stage, this variable is included in each of the N standard country regressions.
Table 6 represents AMG country groups regressions results. Results of the analysis show that an increase in gross domestic product (industry added value) decreases total energy intensity (industry energy intensity) for high and upper-middle income country groups. On the other hand, for lower-middle income group, gross domestic product (industry value added) have no effect on total energy intensity (industry energy intensity). According to high income country group regression results, 1% increase in gross domestic product causes 0.48% decrease in total energy intensity. In industrial sector, 1% increase in industry value added causes 0.64% decrease in industry energy intensity. Table 6. AMG country groups regression results Country group
High income
Upper-middle income
Lower-middle income
Dependent variable
LNTEI
LNTEI
LNTEI
LNGDP
-0.48*
LNIVA
LNIEI
LNIEI
-0.59* -0.64*
LNIEI
-0.25 -0.69*
Common process (1)
1.01*
0.95***
Country trend
0.00
0.00
Intercept
14.3*
17.2*
17.37*
18.6*
Observations (countries)
506 (23)
506 (23)
484 (22)
484 (22)
-0.33 1.05*
0.78*
0.00
-0.01
9.01***
9.84**
374 (17)
374 (17)
Notes: *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively. ( 1) Cross sectional dependence is taken into account by including ‘common process’ variable in the regression. 'Common process' and 'country trend' variables are not included for upper-middle income group model.
For upper-middle income country group, 1% increase in gross domestic product causes 0.59% decrease in total energy intensity, similarly, 1% increase in industry value added causes 0.69% decrease in industry energy intensity. As the coefficients in low-middle income country group regression is negative but statistically insignificant, the relationship between the variables is not proved in group mean scale. Coefficients presented in Table 6 show mean results of each country group. Therefore, we present country specific coefficients in Table 7, Table 8 and Table 9 to do more detailed analysis. Table 7. High income group country-specific coefficients Dependent variable: LNTEI for LNGDP, LNIEI for LNIVA Country
LNGDP LNIVA
Country
LNGDP
LNIVA Country
LNGDP
LNIVA
Ireland
-0.38*
-0.72*
Portugal
0.52*
-0.00
Australia -0.46*
-0.84
Austria
-0.73*
-0.48*** Italy
0.06
0.01
Singapore
-0.44
-1.06**
Belgium
-0.95*
-1.08
-0.50**
-0.60*
Spain
0.26*
-0.33***
Japan
Denmark -0.71*
-0.38*** Korea,
-0.34*** -0.52** Sweden
-0.95*
-0.98*
Finland
-0.68*
-0.81*
Luxembourg
-0.38*** -0.97*
Switzerland
-0.49**
-0.68*
France
-0.42*
-0.62*
Netherlands
-1.03*
United Kingdom -0.47*
Germany -0.85*
-0.47*
New Zealand -0.64
-0.68** United States
Iceland
-1.53*
Norway
-0.80*
-0.75*
-0.65*
-0.64*
-0.18
-0.19*** -0.31
Notes: Corresponding values to countries refer to coefficients. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively.
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According to high income countries country-specific results, we find that 1% increase in gross domestic product decreases total energy intensity in almost every country. Our findings show that “economic growth decreases energy intensity” hypothesis is valid for high income countries except Italy, New Zealand and Singapore. In industrial sector, 1% increase in industry value added decreases industry energy intensity in 17 of 23 countries and coefficients differ to more or less intensive energy use in industrial production of countries. The industry value added mean coefficient is higher than gross domestic product mean coefficient; this result shows that energy use is more important in industry sector. Table 8. Upper-middle income group country-specific coefficients Dependent variable: LNTEI for LNGDP, LNIEI for LNIVA Country
LNGDP LNIVA Country
LNGDP LNIVA
Country
Argentina
-0.46*
-0.71*
Hungary
-0.87*
-1.24*
Russian Fed. -0.95*
-1.09*
Azerbaijan -0.97*
-1.34*
Iran
0.25*
0.74*
Serbia
-0.81*
-1.36*
Brazil
-0.16*
0.05
Iraq
-0.88*
-1.01*
South Africa -0.61*
-0.40*
Bulgaria
-1.36*
-1.30*
Lebanon
-0.72*
-0.96*
Thailand
0.02
-0.15*
China
-0.42*
-0.29*
Malaysia -0.17*
-0.22*** Turkey
-0.35*
-0.55*
Colombia
-0.68*
-0.88*
Mexico
-0.37*
-0.27*
-0.59*
-0.36
Costa Rica -0.21*
0.32
Poland
-0.95*
-1.55*
Croatia
-0.65*
Romania -1.29*
-1.88*
-0.65*
Venezuela
LNGDP LNIVA
Notes: Corresponding values to countries refer to coefficients. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively.
Upper-middle income country-specific regression results confirm the hypothesis that “economic growth decreases energy intensity” in 20 of 22 countries. Our hypothesis is not valid for Thailand because coefficient of LNGDP is statistically insignificant. In industry sector of upper-middle income countries, our hypothesis is accepted in 19 countries but is not valid for Brazil and Costa Rica due to statistically insignificant coefficients. On the contrary, “economic growth increases energy intensity” for Iran. Table 9. Lower-middle income group country-specific coefficients Dependent variable: LNTEI for LNGDP, LNIEI for LNIVA Country
LNGDP LNIVA Country
LNGDP LNIVA Country
LNGDP LNIVA
Bangladesh -0.52*
0.16
Moldova
1.08*
-0.54*
Sri Lanka
-1.05*
-2.38*
Cameroon
2.45*
-0.45
Morocco
-0.03
0.57
Sudan
-0.39*
-0.89*
Egypt
0.77**
0.11
Nigeria
-1.01*
0.25
Tunisia
-0.08
-0.73***
India
0.14
0.53**
Pakistan
-0.09
-0.06
Ukraine
-0.20*
-0.46*
Indonesia
-1.09*
-1.08*
Philippines 0.60
0.33
Uzbekistan -1.96*
-1.67*
-0.39
Senegal
0.99
Kyrgyzstan -1.03
-1.97*
Notes: Corresponding values to countries refer to coefficients. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively.
Country specific coefficients estimated for lower-middle country group contain different findings compared to other country groups which are examined above. In this country group that our hypothesis is not confirmed in group mean, the results differ to structures of the countries. At GDP level, our hypothesis is accepted for Bangladesh, Indonesia, Nigeria, Senegal, Sri Lanka, Sudan, Ukraine and Uzbekistan, but is rejected for Cameroon, Egypt and Moldova. Also, our hypothesis is not valid for other countries in the model because coefficient of LNGDP is statistically insignificant. In industry sector, our hypothesis is accepted for Indonesia, Moldova, Sri Lanka, Sudan,
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531 9
Tunisia, Ukraine and Uzbekistan, but is rejected for India. In the literature, the studies of Deichmann et al. [5], Mahmood and Ahmad [7] support our hypothesis that “economic growth decreases energy intensity”. Deichmann et al. [5], Mahmood and Ahmad [7] prove the negative relationship between energy intensity and economic growth. Our findings support this negative relationship between the variables. Deichmann et al. [5] also state that we can see rapid improvements in energy intensity as the economies of low-income countries grow. Our findings suggest that the negative relationship between economic growth and energy intensity increases as the country reaching from low income level to high. With this suggestion, our findings are similar with Deichmann et al. [5]. 4. Conclusion In this paper, we examine the effect of economic growth on energy efficiency using panel data analysis for high, upper-middle and lower-middle income countries between the periods 1995-2016. We include 23 high, 22 uppermiddle and 17 lower-middle income countries in the analysis. We use energy intensity as an indicator of energy efficiency. We identify our hypothesis as “economic growth decreases energy intensity” and to prove this hypothesis we implement cointegration test and Augmented Mean Group estimator. As a result of Westerlund [22] cointegration test, there is cointegration between the variables for high and uppermiddle income country groups but there is no cointegration for lower-middle income country group. AMG estimator results demonstrate that an increase in gross domestic product (industry added value) decrease total energy intensity (industry energy intensity) for high and upper-middle income country groups. On the other hand, for lower-middle income group, gross domestic product (industry value added) have no effect on total energy intensity (industry energy intensity). Our findings show that “economic growth decreases energy intensity” hypothesis is valid for high income and upper-middle income country groups countries but is not valid for lower-middle income country group. For low income countries, increase in energy intensity is a result of industrialization and urbanization. But, as the countries reach from low income level to high, their economic structures become less energy intensive. Deindustrialization process begins and service sector, which is less energy intensive as against industry sector, become more important. In addition, energy efficiency policies can be implemented easily with technological developments today. In consideration of these findings, we can say that energy efficiency policies will have more critical importance for countries, when they exceed lower-middle income level. References [1] Hanley, Nick D., Peter G. McGregor, J. Kim Swales, and Karen Turner. (2006) “The impact of a stimulus to energy efficiency on the economy and the environment: A regional computable general equilibrium analysis” Renewable Energy 31: 161–171. [2] Allan, Grant, Nick Hanley, Peter McGregor, Kim Swales, and Karen Turner. (2007) “The impact of increased efficiency in the industrial use of energy: a computable general equilibrium analysis for the United Kingdom” Energy Economics 29: 779–798. [3] Bataille, Chris and Noel Melton. (2017) “Energy efficiency and economic growth: A retrospective CGE analysis for Canada from 2002 to 2012” Energy Economics 64: 118-130. [4] Choi, Bongseok, Wooyung Park, and Bok-Keun Yu. (2017) “Energy intensity and firm growth” Energy Economics 54: 399-410. [5] Deichmann, Uwe, Anna Reuter, Sebastian Vollmer, and Fan Zhang. (2018) “Relationship between energy intensity and economic growth: New evidence from a multi-country multi-sector data set” Policy Research Working Paper No: 8322. [6] Rajbhandari, Ashish, and Fan Zhang. (2018) “Does energy efficiency promote economic growth? Evidence from a multicountry and multisectoral panel dataset” Energy Economics 69: 128-139. [7] Mahmood, Tahir, and Eatzaz Ahmad. (2018) “The relationship of energy intensity with economic growth: Evidence for European economies” Energy Strategy Reviews 20: 90-98. [8] IEA. (2018) “World Energy Balances 2018” OECD Publishing, Paris, https://doi.org/10.1787/world_energy_bal-2018-en. [9] The World Bank, World Development Indicators, https://data.worldbank.org/, 03.18.2019. [10] Gujarati, Damodar N., and Dawn C. Porter. (2009) “Basic econometrics”, Fifth Edition, McGraw-Hill / Irwin.
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[11] Maddala, Gangadharrao Soundalyarao, and Shaowen Wu. (1999) “A comparative study of unit root tests with panel data and a new simple test” Oxford Bulletin of Economics and Statistics 61: 631- 652. [12] Choi, In. (2001) “Unit root tests for panel data” Journal of International Money and Finance 20 (2): 249-272. [13] Levin, Andrew, Chien-Fu Lin, and Chia-Shang James Chu. (2002) “Unit root tests in panel data: asymptotic and finite-sample properties” Journal of Econometrics 108 (1): 1-24. [14] Phillips, Peter C. B., and Donggyu Sul. (2003) “Dynamic panel estimation and homogeneity testing under cross section dependence” The Econometrics Journal 6 (1): 217-259. [15] Bai, Jushan, and Serena Ng. (2004) “A panic attack on unit roots and cointegration” Econometrica 72 (4): 1127-1177. [16] Moon, Hyungsik Roger, and Benoit Perron. (2004) “Testing for a unit root in panels with dynamic factors” Journal of Econometrics 122 (1): 81-126. [17] Pesaran, M. Hashem. (2007) “A simple panel unit root test in the presence of cross section dependence” Journal of Applied Econometrics 22 (2): 265-312. [18] Westerlund, Joakim, and David L. Edgerton. (2008). “A simple test for cointegration in dependent panels with structural breaks” Oxford Bulletin of Economics and Statistics 70 (5): 665-704. [19] Friedman, Milton. (1937) “The use of ranks to avoid the assumption of normality implicit in the analysis of variance” Journal of the American Statistical Association 32 (200): 675-701. [20] Pesaran, M. Hashem. (2004) “General diagnostic tests for cross section dependence in panels” Center for Economic Studies & Ifo Institute for Economic Research Working Paper No: 1229. [21] Breusch, Trevor S., and Adrian Rodney Pagan. (1980) “The lagrange multiplier test and its application to model specification in econometrics”, Review of Economic Studies 47 (1): 239–253. [22] Westerlund, Joakim. (2007) “Testing for error correction in panel data” Oxford Bulletin of Economics and Statistics 69 (6): 709-748. [23] Eberhardt, Markus, and Stephen Bond. (2009) “Cross-section dependence in non-stationary panel models: a novel estimator” Munich Personal Repec Archive Paper Series No: 17692.
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Procedia Computer Science 158 (2019) 523–532
3rd World Conference on Technology, Innovation and Entrepreneurship (WOCTINE) 3rd World Conference on Technology, Innovation and Entrepreneurship (WOCTINE)
The effect of economic growth on energy efficiency: Evidence The effect of economic growth on energy efficiency: Evidence from high, upper-middle and lower-middle income countries from high, upper-middle and lower-middle income countries 1
Sefer Sener11**, Ahmet Tarik Karakas22 Sefer Sener , Ahmet Tarik Karakas
Istanbul University, Faculty of Economics, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey Sciences, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey IstanbulUniversity, University,Institute Facultyof ofSocial Economics, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey 2 Istanbul University, Institute of Social Sciences, Central Campus, Beyazit-Fatih, Istanbul 34452, Turkey 21 Istanbul
Abstract Abstract In this paper, we examine the effect of economic growth on energy efficiency using panel data analysis for high, upper-middle and Inlower-middle this paper, weincome examine the effect of economic growth1995-2016. on energy efficiency using data analysis high, upper-middle and countries between the periods Six models are panel established for twoforeconomic levels, namely lower-middle income countries between the (GDP) periods–economy 1995-2016. modelsand areenergy established for is two economicaslevels, namelyof industry sector and gross domestic product as Six a whole–, intensity calculated an indicator industry sector and Because gross domestic product (GDP)between –economy as a whole–, intensitywhich is calculated an indicator of energy efficiency. of strong interaction countries, we useand testsenergy and methods take intoasaccount the cross energy efficiency. Because of strongour interaction between countries, we use tests and methods which and taketointo account the cross sectional dependence. We identify hypothesis as “economic growth decreases energy intensity” prove this hypothesis sectional dependence. We identify ourAugmented hypothesis as “economic growth decreases energy intensity” to prove is this hypothesis we implement cointegration test and Mean Group estimator. Our findings show that theand hypothesis valid for high we cointegration test andgroups. Augmented estimator. Our is findings show that the hypothesis is valid forgroup. high andimplement upper-middle income country On theMean otherGroup side, our hypothesis rejected for lower-middle income country and income country groups. On the other side,economic our hypothesis rejected lower-middle income group. Ourupper-middle findings suggest that the negative relationship between growthisand energyfor intensity increases as thecountry countries reach Our findings suggest thattothe negative relationship between economic growth and energy intensity increases as the countries reach from low income level high. from low income level to high. © 2019 The Author(s). Published by Elsevier B.V. © 2019 The Authors. Published by Elsevier B.V. ©Peer-review 2019 The Author(s). Published byofElsevier B.V. committee of the 3rd World Conference on Technology, Innovation and under responsibility the scientific Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Entrepreneurship Entrepreneurship Entrepreneurship Keywords: Economic growth, energy efficiency, energy intensity, panel data analysis Keywords: Economic growth, energy efficiency, energy intensity, panel data analysis
1. Introduction 1. Introduction Energy is one of the most important sources for social and economic development. Obtaining energy in a safe, Energy is high one of the most important sources for social and economic Obtaining in a levels safe, continuous, quality, efficient and environment-friendly way increasesdevelopment. the living conditions andenergy prosperity continuous, quality, efficienteconomic and environment-friendly wayactivities increases the be living conditions and prosperity levels of countries.high In order to achieve growth, production must carried out continuously. Therefore, ofsafe countries. In order to achieve economic growth, productionactivities activitieshave mustgreat be carried out continuously. and inexpensive energy supply required for production importance for economicTherefore, growth. safe and inexpensive energy supply required for production activities have great importance for economic growth. * Corresponding author. E-mail address: [email protected] * Corresponding author. E-mail address: [email protected] 1877-0509 © 2019 The Author(s). Published by Elsevier B.V. Peer-review responsibility ofPublished the scientific of the 3rd World Conference on Technology, Innovation and Entrepreneurship 1877-0509 © under 2019 The Author(s). by committee Elsevier B.V. Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Entrepreneurship
1877-0509 © 2019 The Authors. Published by Elsevier B.V. Peer-review under responsibility of the scientific committee of the 3rd World Conference on Technology, Innovation and Entrepreneurship 10.1016/j.procs.2019.09.084
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Energy resources are vary depending on geographical position of the countries. Countries which are rich or poor in terms of their energy resources are affected at different levels economically and politically in the event of an energy crisis. High unemployment, recession and uncertainty in the expectations, which are emerging more with the energy crises, create a risky environment for the countries all over the world. Therefore, properly implementation of energy policies for sustainable development and growth depends on safe, efficient, continuous and environment friendly energy supply. In addition to supplying the required energy within the scope of these criteria, countries have developed a number of new strategies on energy use. The concept of energy efficiency is one of the strategies put forwarded on energy use and this concept is based on the using less energy or supplying less costly to product same amount goods or services. In this context, considering the close relationship between energy policies and economic activity, associate economic growth to energy efficiency constitutes the importance of this paper. Energy intensity is one of the most important indicators of energy efficiency and it is formulated as the amount of energy consumed per economic output. In addition to this, it is a common tool used for monitoring and comparing energy efficiency between countries. Energy intensity varies depending on factors such as energy consumption, GDP and technology. In today’s world, level of development a country in terms of energy use is determined by energy consumption per capita and energy intensity. In this context, aim of this study is investigate the effect of economic growth on energy intensity. In order to achieve this aim, we create a hypothesis that “economic growth decreases energy intensity”, and we examine this hypothesis in high, upper-middle and lower-middle-income country groups for GDP and industry sector economic levels. This paper is organized as follows. In section 2, we present the empirical literature on the relationship between economic growth and energy intensity. In section 3, we describe the model and econometric methodology used in the study. And later, we examine the relationship between economic growth and energy intensity in this chapter. 2. Literature review In the literature, studies investigating the relationship between energy and economic growth started with oil crises in 1973-1974 and 1978-1979. In these studies, usually the relationship between energy consumption and economic growth was investigated. Since 1990s, the claim that fossil energy consumption causes global warming has shifted the focus of energy studies to carbon dioxide emission and energy efficiency. On the other hand, the number of studies examining the relationship between energy efficiency and economic growth is limited in the literature. Hanley et al. [1] investigate the economic and environmental impacts of energy efficiency using the Regional Computable General Equilibrium model for Scotland. In the study using data of 1999, the short, medium and long run effects of 5% energy efficiency increase in all economic factors on GDP were estimated. According to findings of the analysis, GDP will increase 0.06% in the short term, 0.10% in the medium term and 0.88% long term, as a result of 5% energy efficiency increase. Their findings suggest that Scottish economy could earn an extra growth increase in energy efficiency. Allan et al. [2] investigate the impact of energy efficiency on economic growth in United Kingdom using Economy-Energy-Environment Computable General Equilibrium (CGE) method. Short and long term effects of 5% energy efficiency increase in industry sector on GDP were demonstrated by using the input-output tables created for the year 2000. The analysis results show that when energy efficiency increases by 5%, GDP will increase 0.11% in the short term and 0.17% in the long term. Bataille and Melton [3] search the impact of energy efficiency on economic growth in Canada using annual data between the periods 2002 and 2012. Regional General Equilibrium Energy Model (RGEEM) was used in the study and it was estimated that the improvements in energy efficiency increased the GDP by 2% (0.19% a year). Choi, Park and Yu [4] explore the relationship between energy efficiency and firm growth in 21 sub-branches of manufacturing industry for France, Japan, Germany, South Korea, United Kingdom and United States using microlevel company panel data between the periods 1992 and 2005. They used energy intensity (EI) as an indicator of energy efficiency. In addition to this, they also calculated relative energy intensity (REI) and included in the analysis. According to the findings of the analysis, while firm profitability increased with low REI, there was no relationship
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between low EI and firm profitability. Deichmann et al. [5] examine the relationship between energy intensity and economic growth for 137 countries between the periods 1990 and 2014 using yearly panel data set. Using Flexible Piecewise Linear Regression method, they found a negative relationship between energy intensity and economic growth. This result shows that as economies of low-income countries grow, there will be a rapid improvement in their energy intensities. Another finding obtained from the study is that energy efficiency policies will have more critical importance, when low income countries exceed lower-middle income level. Rajbhandari and Zhang [6] analyse the impact of energy efficiency on economic growth using Panel Vector Autoregression (PVAR) for high and middle-income countries between the periods 1978 and 2012. According to findings of the analysis, they found unidirectional long-run causality from economic growth to lower energy intensity for all country groups. On the other hand, they found bidirectional long-run causality between energy intensity and economic growth for lower-middle income country group. Based on the results of the analysis, they emphasized that energy efficiency could contribute to GDP in the long-run. Mahmood and Ahmad [7] examine the relationship between energy intensity and economic growth using Least Squares with Dummy Variable (LSDV) and General Method of Moments (GMM) for the years 1995-2015 in 19 European Union member countries. They proved a negative relationship between energy intensity and economic growth and interpreted the reason of low energy intensity as the transition from the industrial sector to the less energy intensive sectors. 3. Data description, methods and findings In this paper, we investigate the effect of economic growth on energy efficiency for the years 1995-2016 in high, upper-middle and lower-middle income countries. We include 23 high, 22 upper-middle and 17 lower-middle income countries (62 in total) to the analysis. Table 1 presents country groups selected for our study by income level. Table 1. Countries used in the study Country groups
Countries
High income
Australia, Austria, Belgium, Denmark, Finland, France, Germany, Iceland, Ireland, Italy, Japan, Korea, Luxembourg, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland, United Kingdom, United States
Upper middle income
Argentina, Azerbaijan, Brazil, Bulgaria, China, Colombia, Costa Rica, Croatia, Hungary, Iran, Iraq, Lebanon, Malaysia, Mexico, Poland, Romania, Russian Federation, Serbia, South Africa, Thailand, Turkey, Venezuela
Lower middle income
Bangladesh, Cameroon, Egypt, India, Indonesia, Kyrgyzstan, Moldova, Morocco, Nigeria, Pakistan, Philippines, Senegal, Sri Lanka, Sudan, Tunisia, Ukraine, Uzbekistan
We use energy intensity as an indicator of energy efficiency. Energy intensity is calculated by dividing the amount of energy consumed to economic output. The model used in the calculation of energy intensity is given in Equation 1. 𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸𝐸 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠(𝑜𝑜𝑜𝑜 𝐺𝐺𝐺𝐺𝐺𝐺) =
𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡𝑡 𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓𝑓 𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒𝑒 𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑜𝑜𝑜𝑜 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 (𝑜𝑜𝑜𝑜 𝐺𝐺𝐺𝐺𝐺𝐺) 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣𝑣 𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎𝑎 (𝑜𝑜𝑜𝑜 𝐺𝐺𝐺𝐺𝐺𝐺)
(1)
In the study, we set up a models based on two different economic levels, namely industrial sector and GDP (economy as a whole). The reason for such a distinction is that industry sector and GDP have different structures in terms of energy intensity. Therefore, we decided to set up a different model -6 in total- for each economic level and country group. In order to obtain energy intensity data, industrial sector and total energy consumption data are taken from International Energy Agency [8]; GDP and industrial sector value added (IVA) data are taken from World Bank (World Development Indicators) database [9]. In order to standardize the variables, we take natural logarithms of them. Table 2 presents the variables and their unit values.
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Table 2. Variables and unit values used in the analysis Abbreviation
Variable (unit value)
LNTEI
Total Energy intensity (MJ/constant 2010 US$ GDP)
LNGDP
Gross domestic product (constant 2010 US$)
LNIEI
Industry energy intensity (MJ/constant 2010 US$ IVA)
LNIVA
Industry value added (constant 2010 US$)
Panel data in econometric applications are preferred because they can work with more complex behavior models and can enrich empirical analysis in ways that may not be possible if we use only cross section (one year, one month etc.) or time series data (one country, one firm etc.) [10]. Taking these advantages into account, we think that panel data analysis is right method for our model including 62 countries. The wide method framework of panel data allows the implementation of many estimators suitable with purpose of the study and structure of variables. In order to determine which panel data estimators will be used, firstly, we test whether there is a cross section dependence and unit root or not. 3.1. Cross sectional dependence and unit root test in panel data Unit root tests are divided into two groups as first and second generation unit root tests with the assumption that whether there is a relationship between cross sections or not. First generation panel unit root tests such as Maddala and Wu [11], Choi [12], Levin, Lin and Chu [13] assume that there is no cross sectional dependence. Second generation unit root tests such as Phillips and Sul [14], Bai and Ng [15], Moon and Perron [16], Pesaran [17] assume that there is cross sectional dependence in panel. Therefore, as a first step we prove whether cross sectional dependence in panels or not. When working with macroeconomic or financial data, cross-section dependence should be taken into account because of strong interaction between countries [18]. In the global world, acting on the assumption that macroeconomic variables do not affect each other reduces the reliability of the analyses. Cross sectional dependence is investigated according to number of countries (N) and years (T). When N>T, Friedman [19] and Pesaran [20] tests can be used for examine cross sectional dependence. On the other side, under T>N condition, Breusch-Pagan [21] test can be used. To examine cross sectional dependence, we firstly set up fixed-effects and random-effects models. Breusch-Pagan [21] test only in fixed-effect models; Friedman [19] and Pesaran [20] tests can be estimated in both fixed and random effects models. Form of fixed and random effect models for testing cross sectional dependence is as follows: 𝐿𝐿𝐿𝐿𝑌𝑌𝑖𝑖𝑖𝑖 = 𝛽𝛽0 + 𝛽𝛽1 𝑋𝑋𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖
(2)
𝑖𝑖: 1, 2, … , 𝑁𝑁 (𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐) 𝑎𝑎𝑎𝑎𝑎𝑎 𝑡𝑡: 1, … , 22 (1995, … , 2016)
Table 3. Friedman [19], Pesaran [20] and Breusch-Pagan [21] cross sectional dependence test results High income
Upper-middle income
Lower-middle income
LNTEI
LNIEI
LNTEI
LNIEI
LNTEI
LNIEI
Friedman
154.3*
182.8*
16.62
29.65
Pesaran CD
24.47*
27.14*
0.038
0.737 1121.6*
722.02*
Fixed-effects regression
Breusch-Pagan Random-effects regression Friedman
187.1*
185.4*
12.32
16.04
Pesaran
29.65*
27.82*
-0.83
-1.32
Breusch-Pagan Note: The null hypothesis is that there is no cross sectional dependence.
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Table 3 indicates Friedman [19], Pesaran [20] and Breusch-Pagan [21] cross sectional dependence test results. Test results demonstrate a strong relationship between cross sections in high and lower-middle income country models. On the other hand, null hypothesis is accepted for upper-middle income countries and it is proven that there is no relationship between the cross sections. Based on cross sectional dependence test results, we carry out Pesaran [17] unit root test for high and lower-middle income country groups and Maddala and Wu [11] unit root test for uppermiddle income country group. Simple dynamic linear heterogeneous panel data model established for Pesaran [17] unit root test as follows: ∆𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑎𝑎𝑖𝑖 + 𝛽𝛽𝑖𝑖 𝑦𝑦𝑖𝑖,𝑡𝑡−1 + 𝛾𝛾𝑖𝑖 𝑓𝑓𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖
(3)
𝐻𝐻0 : 𝛽𝛽𝑖𝑖 = 0 for all 𝑖𝑖
(4)
𝐻𝐻1 : 𝛽𝛽𝑖𝑖 < 0, 𝑖𝑖 = 1,2, … . , 𝑁𝑁1 , 𝛽𝛽𝑖𝑖 = 0 𝑖𝑖 = 𝑁𝑁1 + 1, 𝑁𝑁1 + 2, … . , 𝑁𝑁
(5)
Where 𝑓𝑓𝑡𝑡 is the unobserved common effect, and 𝜀𝜀𝑖𝑖𝑖𝑖 is the individual-specific (idiosyncratic) error. The unit root hypothesis can be expressed as:
against the possibly heterogeneous alternatives,
Maddala and Wu [11] unit root test is described as the Fisher’s type test. Under the assumption of cross sectional independence, the statistic proposed by Maddala and Wu [11] defined as: 𝑁𝑁
𝑃𝑃𝑀𝑀𝑀𝑀 = −2 ∑ log(𝑝𝑝𝑖𝑖 )
(6)
𝑖𝑖=1
Table 4. Pesaran [17], Maddala and Wu [11] unit root test results LNGDP
∆LNGDP
LNIVA
∆LNIVA
LNTEI
∆LNTEI
LNIEI
∆LNIEI
Without trend
0.85
-8.77*
2.50
-10.6*
0.01
-15.1*
-1.52***
-14.2*
With trend
0.77
-6.43*
0.05
-8.87*
0.38
-14.9*
0.87
-13.1*
Without trend
18.68
208.6*
50.71
222.6*
36.90
343.8*
67.92**
478.3*
With trend
23.81
143.9*
130.8*
163.1*
63.61**
278.8*
111.7*
385.4*
Without trend
-1.29
-5.83*
1.40
-6.87*
-1.03
-10.4*
-2.12**
-13.6*
With trend
2.08
-5.38*
4.58
-6.86*
1.59
-9.05*
-1.32***
-11.6*
High income ( ) 1
Upper-middle income (2)
Lower-middle income (1)
Notes: ( ) Corresponding values to high and low-middle income country groups indicate CIPS statistics. ( ) Corresponding values to upper-middle income country group indicate chi-square statistics. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively. The null hypothesis indicates that the series is nonstationary. 1
2
Table 4 indicates that gross domestic product, industry value added, total energy intensity and industry energy intensity series have unit root. After taking first differences, we can say that the analysed variables are integrated of order one. 3.2. Panel cointegration test and coefficient estimation results While investigating the long-run relationship between variables in panel data, it is handled differently from the methods applied for time series or cross section data. If there is a relationship between the cross sections, the
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cointegration tests that ignore this situation cause misinterpretation of the test results. Therefore, we use Westerlund [22] cointegration test, which takes into account cross sectional dependence. Westerlund [22] presents two cointegration tests which are group statistics (𝐺𝐺𝑡𝑡 , 𝐺𝐺𝑎𝑎 ) and panel statistics (𝑃𝑃𝑡𝑡 , 𝑃𝑃𝑎𝑎 ). The process of obtaining Westerlund panel and group test statistics is carried out in three steps. These test statistics obtained in three steps are compared with critical values and decisions about cointegration relationship are made. The null hypothesis in Westerlund [22] is “there is no cointegration between the variables” and alternative hypothesis is “there is cointegration between the variables”. When cross sectional dependence is detected, Westerlund [22] suggests that the robust probability values obtained with bootstrap tests must be taken in consideration. We use robust probability values for high and lower-middle income country models due to cross sectional dependence and standard probability for upper-middle income country model. Table 5 presents Westerlund [22]cointegration test results. The results show that there is cointegration between the variables for high and upper-middle income country groups but there is no cointegration for lower-middle income country group. After proving the long run relationship between total energy intensity and gross domestic product, industry energy intensity and industry value added for high and uppermiddle income country groups, we can start to long run coefficient estimation. Table 5. Westerlund [22] cointegration test results High income
Upper-middle income
Lower-middle income
Gt
-3.21*
-5.44*
-1.58
Ga
-6.79*
-0.49
1.37
Pt
-2.66*
-5.23*
-0.90
Pa
-1.38
-6.81*
-0.82
Gt
-1.52*
-5.70*
-0.53
Ga
-2.21*
-1.79*
1.27
Pt
-1.65*
-5.25*
-2.00
Pa
-2.79*
-5.89*
-2.41
LNTEI-LNGDP model
LNIEI-LNIVA model
Notes: * denotes the statistical significance at 5% level. Standard probability values are used for lower-middle income country group.
We use Augmented Mean Group (AMG) method to estimate the effect of economic growth on energy intensity for selected country groups. We prefer this method because it is a modern estimator that takes into account cross sectional dependence and gives us both country specific coefficients and mean group coefficients. AMG estimator, developed by Eberhardt and Bond [23] uses Monte Carlo simulation. It takes into account cross sectional dependence by inclusion of a ‘common dynamic effect’ in the country regression. The model developed by Eberhardt and Bond [23] is as follows: 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝛽𝛽𝑖𝑖′ 𝑥𝑥𝑖𝑖𝑖𝑖 + 𝑢𝑢𝑖𝑖𝑖𝑖 𝑥𝑥𝑚𝑚𝑚𝑚𝑚𝑚 = 𝜋𝜋𝑚𝑚𝑚𝑚 +
𝑚𝑚 = 1, … , 𝑘𝑘
′ 𝛿𝛿𝑚𝑚𝑚𝑚 𝑔𝑔𝑚𝑚𝑚𝑚
𝑓𝑓𝑡𝑡 = 𝜑𝜑 ′ 𝑓𝑓𝑡𝑡−1 + 𝜀𝜀𝑡𝑡
𝑣𝑣𝑣𝑣
𝑢𝑢𝑖𝑖𝑖𝑖 = 𝛼𝛼𝑖𝑖 + 𝜆𝜆′𝑖𝑖 𝑓𝑓𝑡𝑡 + 𝜀𝜀𝑖𝑖𝑖𝑖
+ 𝜌𝜌1𝑚𝑚𝑚𝑚 𝑓𝑓1𝑚𝑚𝑚𝑚 +. . . +𝜌𝜌𝑛𝑛𝑛𝑛𝑛𝑛 𝑓𝑓𝑛𝑛𝑛𝑛𝑛𝑛 + 𝑣𝑣𝑚𝑚𝑚𝑚𝑚𝑚
𝑣𝑣𝑣𝑣
𝑓𝑓.𝑚𝑚𝑚𝑚 ⊂ 𝑓𝑓𝑡𝑡
𝑔𝑔𝑡𝑡 = 𝜅𝜅 ′ 𝑔𝑔𝑡𝑡−1 + 𝜀𝜀𝑡𝑡
(7)
(8)
(9)
Where 𝑥𝑥𝑖𝑖𝑖𝑖 is a vector of observable covariates, 𝛼𝛼𝑖𝑖 is a combination of group-specific fixed effects, 𝑓𝑓𝑡𝑡 is a set of common factors and 𝜆𝜆𝑖𝑖 is country-specific factor loadings. In equation 5, 𝑚𝑚 = 1, … , 𝑘𝑘 is represents 𝑘𝑘 observable regressors, which are modelled as linear functions of unobserved common factors 𝑓𝑓𝑡𝑡 and 𝑔𝑔𝑡𝑡 , with country-specific factor loadings respectively. The model setup thus introduces cross section dependence in the observables and unobservables. Equation 6 indicates the progress of the unobserved factors. AMG estimator is carried out in two stages:
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AMG - Stage (1) AMG - Stage (2)
∆𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑏𝑏 ′ ∆𝑥𝑥𝑖𝑖𝑖𝑖 + ∑𝑇𝑇𝑡𝑡=2 𝑐𝑐𝑐𝑐 ∆𝐷𝐷𝑡𝑡 + 𝑒𝑒𝑖𝑖𝑖𝑖 𝑦𝑦𝑖𝑖𝑖𝑖 = 𝑎𝑎𝑖𝑖 +
𝑏𝑏𝑖𝑖′ 𝑥𝑥𝑖𝑖𝑖𝑖
+ 𝑐𝑐𝑖𝑖 𝑡𝑡 +
𝑑𝑑𝑖𝑖 𝑢𝑢̂𝑡𝑡•
+ 𝑒𝑒𝑖𝑖𝑖𝑖
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⇒ 𝑐𝑐̂𝑡𝑡 ≡ 𝑢𝑢̂𝑡𝑡• ⇒ 𝑏𝑏̂𝐴𝐴𝐴𝐴𝐴𝐴 ≡ 𝑁𝑁 −1 ∑İ 𝑏𝑏̂İ
(10)
(11)
In the first stage, the model is estimated by taking first difference of variables and adding year dummy variable. The main purpose this process is ensure that nonstationary variables and unobservable effects do not cause errors. In the second stage, this variable is included in each of the N standard country regressions.
Table 6 represents AMG country groups regressions results. Results of the analysis show that an increase in gross domestic product (industry added value) decreases total energy intensity (industry energy intensity) for high and upper-middle income country groups. On the other hand, for lower-middle income group, gross domestic product (industry value added) have no effect on total energy intensity (industry energy intensity). According to high income country group regression results, 1% increase in gross domestic product causes 0.48% decrease in total energy intensity. In industrial sector, 1% increase in industry value added causes 0.64% decrease in industry energy intensity. Table 6. AMG country groups regression results Country group
High income
Upper-middle income
Lower-middle income
Dependent variable
LNTEI
LNTEI
LNTEI
LNGDP
-0.48*
LNIVA
LNIEI
LNIEI
-0.59* -0.64*
LNIEI
-0.25 -0.69*
Common process (1)
1.01*
0.95***
Country trend
0.00
0.00
Intercept
14.3*
17.2*
17.37*
18.6*
Observations (countries)
506 (23)
506 (23)
484 (22)
484 (22)
-0.33 1.05*
0.78*
0.00
-0.01
9.01***
9.84**
374 (17)
374 (17)
Notes: *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively. ( 1) Cross sectional dependence is taken into account by including ‘common process’ variable in the regression. 'Common process' and 'country trend' variables are not included for upper-middle income group model.
For upper-middle income country group, 1% increase in gross domestic product causes 0.59% decrease in total energy intensity, similarly, 1% increase in industry value added causes 0.69% decrease in industry energy intensity. As the coefficients in low-middle income country group regression is negative but statistically insignificant, the relationship between the variables is not proved in group mean scale. Coefficients presented in Table 6 show mean results of each country group. Therefore, we present country specific coefficients in Table 7, Table 8 and Table 9 to do more detailed analysis. Table 7. High income group country-specific coefficients Dependent variable: LNTEI for LNGDP, LNIEI for LNIVA Country
LNGDP LNIVA
Country
LNGDP
LNIVA Country
LNGDP
LNIVA
Ireland
-0.38*
-0.72*
Portugal
0.52*
-0.00
Australia -0.46*
-0.84
Austria
-0.73*
-0.48*** Italy
0.06
0.01
Singapore
-0.44
-1.06**
Belgium
-0.95*
-1.08
-0.50**
-0.60*
Spain
0.26*
-0.33***
Japan
Denmark -0.71*
-0.38*** Korea,
-0.34*** -0.52** Sweden
-0.95*
-0.98*
Finland
-0.68*
-0.81*
Luxembourg
-0.38*** -0.97*
Switzerland
-0.49**
-0.68*
France
-0.42*
-0.62*
Netherlands
-1.03*
United Kingdom -0.47*
Germany -0.85*
-0.47*
New Zealand -0.64
-0.68** United States
Iceland
-1.53*
Norway
-0.80*
-0.75*
-0.65*
-0.64*
-0.18
-0.19*** -0.31
Notes: Corresponding values to countries refer to coefficients. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively.
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According to high income countries country-specific results, we find that 1% increase in gross domestic product decreases total energy intensity in almost every country. Our findings show that “economic growth decreases energy intensity” hypothesis is valid for high income countries except Italy, New Zealand and Singapore. In industrial sector, 1% increase in industry value added decreases industry energy intensity in 17 of 23 countries and coefficients differ to more or less intensive energy use in industrial production of countries. The industry value added mean coefficient is higher than gross domestic product mean coefficient; this result shows that energy use is more important in industry sector. Table 8. Upper-middle income group country-specific coefficients Dependent variable: LNTEI for LNGDP, LNIEI for LNIVA Country
LNGDP LNIVA Country
LNGDP LNIVA
Country
Argentina
-0.46*
-0.71*
Hungary
-0.87*
-1.24*
Russian Fed. -0.95*
-1.09*
Azerbaijan -0.97*
-1.34*
Iran
0.25*
0.74*
Serbia
-0.81*
-1.36*
Brazil
-0.16*
0.05
Iraq
-0.88*
-1.01*
South Africa -0.61*
-0.40*
Bulgaria
-1.36*
-1.30*
Lebanon
-0.72*
-0.96*
Thailand
0.02
-0.15*
China
-0.42*
-0.29*
Malaysia -0.17*
-0.22*** Turkey
-0.35*
-0.55*
Colombia
-0.68*
-0.88*
Mexico
-0.37*
-0.27*
-0.59*
-0.36
Costa Rica -0.21*
0.32
Poland
-0.95*
-1.55*
Croatia
-0.65*
Romania -1.29*
-1.88*
-0.65*
Venezuela
LNGDP LNIVA
Notes: Corresponding values to countries refer to coefficients. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively.
Upper-middle income country-specific regression results confirm the hypothesis that “economic growth decreases energy intensity” in 20 of 22 countries. Our hypothesis is not valid for Thailand because coefficient of LNGDP is statistically insignificant. In industry sector of upper-middle income countries, our hypothesis is accepted in 19 countries but is not valid for Brazil and Costa Rica due to statistically insignificant coefficients. On the contrary, “economic growth increases energy intensity” for Iran. Table 9. Lower-middle income group country-specific coefficients Dependent variable: LNTEI for LNGDP, LNIEI for LNIVA Country
LNGDP LNIVA Country
LNGDP LNIVA Country
LNGDP LNIVA
Bangladesh -0.52*
0.16
Moldova
1.08*
-0.54*
Sri Lanka
-1.05*
-2.38*
Cameroon
2.45*
-0.45
Morocco
-0.03
0.57
Sudan
-0.39*
-0.89*
Egypt
0.77**
0.11
Nigeria
-1.01*
0.25
Tunisia
-0.08
-0.73***
India
0.14
0.53**
Pakistan
-0.09
-0.06
Ukraine
-0.20*
-0.46*
Indonesia
-1.09*
-1.08*
Philippines 0.60
0.33
Uzbekistan -1.96*
-1.67*
-0.39
Senegal
0.99
Kyrgyzstan -1.03
-1.97*
Notes: Corresponding values to countries refer to coefficients. *, ** and *** denote the statistical significance at the 1%, 5% and 10% levels, respectively.
Country specific coefficients estimated for lower-middle country group contain different findings compared to other country groups which are examined above. In this country group that our hypothesis is not confirmed in group mean, the results differ to structures of the countries. At GDP level, our hypothesis is accepted for Bangladesh, Indonesia, Nigeria, Senegal, Sri Lanka, Sudan, Ukraine and Uzbekistan, but is rejected for Cameroon, Egypt and Moldova. Also, our hypothesis is not valid for other countries in the model because coefficient of LNGDP is statistically insignificant. In industry sector, our hypothesis is accepted for Indonesia, Moldova, Sri Lanka, Sudan,
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Tunisia, Ukraine and Uzbekistan, but is rejected for India. In the literature, the studies of Deichmann et al. [5], Mahmood and Ahmad [7] support our hypothesis that “economic growth decreases energy intensity”. Deichmann et al. [5], Mahmood and Ahmad [7] prove the negative relationship between energy intensity and economic growth. Our findings support this negative relationship between the variables. Deichmann et al. [5] also state that we can see rapid improvements in energy intensity as the economies of low-income countries grow. Our findings suggest that the negative relationship between economic growth and energy intensity increases as the country reaching from low income level to high. With this suggestion, our findings are similar with Deichmann et al. [5]. 4. Conclusion In this paper, we examine the effect of economic growth on energy efficiency using panel data analysis for high, upper-middle and lower-middle income countries between the periods 1995-2016. We include 23 high, 22 uppermiddle and 17 lower-middle income countries in the analysis. We use energy intensity as an indicator of energy efficiency. We identify our hypothesis as “economic growth decreases energy intensity” and to prove this hypothesis we implement cointegration test and Augmented Mean Group estimator. As a result of Westerlund [22] cointegration test, there is cointegration between the variables for high and uppermiddle income country groups but there is no cointegration for lower-middle income country group. AMG estimator results demonstrate that an increase in gross domestic product (industry added value) decrease total energy intensity (industry energy intensity) for high and upper-middle income country groups. On the other hand, for lower-middle income group, gross domestic product (industry value added) have no effect on total energy intensity (industry energy intensity). Our findings show that “economic growth decreases energy intensity” hypothesis is valid for high income and upper-middle income country groups countries but is not valid for lower-middle income country group. For low income countries, increase in energy intensity is a result of industrialization and urbanization. But, as the countries reach from low income level to high, their economic structures become less energy intensive. Deindustrialization process begins and service sector, which is less energy intensive as against industry sector, become more important. In addition, energy efficiency policies can be implemented easily with technological developments today. In consideration of these findings, we can say that energy efficiency policies will have more critical importance for countries, when they exceed lower-middle income level. References [1] Hanley, Nick D., Peter G. McGregor, J. Kim Swales, and Karen Turner. (2006) “The impact of a stimulus to energy efficiency on the economy and the environment: A regional computable general equilibrium analysis” Renewable Energy 31: 161–171. [2] Allan, Grant, Nick Hanley, Peter McGregor, Kim Swales, and Karen Turner. (2007) “The impact of increased efficiency in the industrial use of energy: a computable general equilibrium analysis for the United Kingdom” Energy Economics 29: 779–798. [3] Bataille, Chris and Noel Melton. (2017) “Energy efficiency and economic growth: A retrospective CGE analysis for Canada from 2002 to 2012” Energy Economics 64: 118-130. [4] Choi, Bongseok, Wooyung Park, and Bok-Keun Yu. (2017) “Energy intensity and firm growth” Energy Economics 54: 399-410. [5] Deichmann, Uwe, Anna Reuter, Sebastian Vollmer, and Fan Zhang. (2018) “Relationship between energy intensity and economic growth: New evidence from a multi-country multi-sector data set” Policy Research Working Paper No: 8322. [6] Rajbhandari, Ashish, and Fan Zhang. (2018) “Does energy efficiency promote economic growth? Evidence from a multicountry and multisectoral panel dataset” Energy Economics 69: 128-139. [7] Mahmood, Tahir, and Eatzaz Ahmad. (2018) “The relationship of energy intensity with economic growth: Evidence for European economies” Energy Strategy Reviews 20: 90-98. [8] IEA. (2018) “World Energy Balances 2018” OECD Publishing, Paris, https://doi.org/10.1787/world_energy_bal-2018-en. [9] The World Bank, World Development Indicators, https://data.worldbank.org/, 03.18.2019. [10] Gujarati, Damodar N., and Dawn C. Porter. (2009) “Basic econometrics”, Fifth Edition, McGraw-Hill / Irwin.
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