Trade Liberalisation and Electricity Consumption: The Case of Bangladesh

Transcription

1 World Review of Business Research Vol. 8. No. 2. June 2018 Issue. Pp Trade Liberalisation and Electricity Consumption: The Case of Bangladesh Priyanka Mallick Proma 1 and Sakib Bin Amin 2 Recent decades have seen rapid growth of the world economy. This growth has been driven in part by the even faster rise in international trade. Total energy consumption in the world also increased drastically from 8,132 million tons in 1990 to 11,099 million tons in All these bring up an interesting question: how increase in international trade influence energy consumption. In our study we are trying to show the link between how increased trade through trade liberalization increases energy consumption to cope up with the increasing demand and maintain the productivity. The initial hypothesis was that trade liberalization leads to more electricity consumption and more energy consumption as a result. The variables used were trade openness (OPEN), electricity consumption (EC) and energy consumption (EU). The paper examines the cointegration and causal relationship between trade openness and electricity consumption in Bangladesh economy using annual data from 1980 to 2013, considering a multivariate model the Augmented Dickey Fuller (ADF) test was used. The test results show that all the variables are stationary at their first difference form, I (1). Next, the Johansen cointegration method was applied which revealed that there is long-run cointegration between the variables, followed by the Granger causality test that revealed that both electricity consumption and energy use strongly causes trade openness in long-run. The results helped us to conclude that there is a unidirectional causality running from electricity consumption to trade liberalization in Bangladesh which rejects the initial hypothesis of this paper for Bangladesh economy.therefore, more electricity consumption leads to more trade. Field of Research: Economics 1. Introduction Recent decades have seen rapid growth of the world economy. This growth has been driven in part by the even faster rise in international trade. The growth in trade is in turn the result of both technological developments and concerted efforts to reduce trade barriers. Some developing countries have opened their own economies to take full advantage of the opportunities for economic development through trade. Over the past 50 years many economies such aschina, India, and United States and so on have experienced large increase in their international trade, national income and energy consumption. In 2010, the world GDP growth rate was 3.6%. In the same year 1 Assistant Professor, School of Business and Economics, North South University, Dhaka, Bangladesh, sakib.amin@northsouth.edu 1 BS in Economics, School of Business and Economics, North South University, Dhaka, Bangladesh, priyanka_996@hotmail.com 26

2 the rate if International trade growth in developed country was 12.9% and in the developing countries and common-interest countries combined together growth was 16.7%. Total energy consumption in the world increased from 8,132 million tons in 1990 to 11,099 million tons in All these bring up an interesting question: how increase in international trade influence energy consumption. Acute shortage of energy sources in developing countries in general and South Asian countries in particular has shown that energy has become a binding input for any production process. Energy consumption being a vital input in production process, affects GDP directly. Availability of energy at reasonable cost improves competiveness of home products in international market, increases exports and affects GDP indirectly. In addition, demand for heavy machinery and electrical equipment- basic components for industrial growth also depends on sufficient supply of energy. The relationship between energy consumption and output is a vastly studied area in energy economics (Lee, 2005; Khan and Qayyum, 2006; Noor and Siddiqi,2010) and the relationship between trade and output is a broadly studied area in international economics (Kemal et al, 2002; Din, 2004). There have been various studies to find out the relationship between trade and energy consumption, also electricity consumption in few of them and the types causalities you will see in the literature review part. However, very few studies have been conducted taking electricity as a specific source of energy and taking Bangladesh into consideration.in our study we are trying to show the link between how increased trade through trade liberalization increases energy consumption to cope up with the increasing demand and maintain the productivity and the causalities. Here we want to look into electricity consumption more specifically compared to other forms of energy consumption. The initial motivation behind this research was the idea that since Bangladesh is historically opening up to engaging in bilateral and multilateral trades due to the increasing trade openness, such globalization moves should ideally result in increasing demand for electricity for more production and economic growth. The initial hypothesis was that trade liberalization leads to more electricity consumption and more energy consumption as a result. The variables used were trade openness (OPEN), electricity consumption (EC) and energy consumption (EU). Hence, trade openness-electricity consumption was inspected in an empirical model. To the best of my knowledge, there had not been much work done regarding this topic exclusively in context of Bangladesh andthus, this paper fills in that research gap. The research findings are new and different from the previous related ones as it concludes the causal relationship between trade liberalization and electricity consumption and the impacts of it on each other. In this paper the following questions have been answered: Is there any causality between trade liberalization and electricity consumption in Bangladesh? Which direction does the causality run from? The rest of the paper is organized as: 27

3 Section 2 provides literature review followed by an overview of Bangladesh in Section 3 and Methodology in section 4. Section 5 contains results and concluding section is in section Literature Review A wide number of studies have been carried out to find a conclusive relationship among trade liberalization, energy consumption and electricity consumption. However, studies vary substantially across countries and no certain policy recommendations can be made. The relationship concerning the above mentioned variables depicts an issue of great significance. Shahbazet (2014) study explores the relationship between trade openness and energy consumption using data of 91 high, middle and low income countries. The study covers the period of Panel unit roots, LLC unit root tests,ips unit root test, MW unit Root Test, Panel cointegration test, Panel causality test, Homogenous non-causality test, Homogenous causality test, Heterogeneous causality test, heterogeneous noncausality test were used in the paper. The results confirm the presence of cointegration between the variables using panel cointegration and causality approaches for long run and causal relationship between the variables. The relationship between trade openness and energy consumption has been found to be an inverted U-shaped in high income countries but U-shaped in middle and low income countries. The homogenous and non-homogenous causality analysis reveals the bidirectional causality between trade openness and energy consumption. Nazarzedeh et al, (2015) used panel data estimation techniques in a study to examine the impact of international trade on energy consumption in a sample of 10 OPEC countries during using Panel Unit Root Test, Cointegration Test and Granger Causality Test. They also examined the impact of GDP on energy prices and energy consumption. The results show a statistically significant relationship between energy consumption and trade. So they conclude that an increase in trade effects energy demand in these countries. Lean and Smyth (2014) examined the relationship between electricity consumption, international trade and economic growth in Bhutan using an augmented production function framework. The main findings of their study arethat in the long run a 1% increase in electricity consumption generates 0.03% 0.05% increase in output. A 1% increase in total trade results in 0.5% increase in output and a 1% increase in trade openness results in 1% increase in output. The study points out that aunidirectional Granger causality runs from electricity consumption to economic growth. The implications their findings are that Bhutan is energy dependent and that it can promote economic growth through further investment in hydropower. Ali et al, (2015) attempted a study to analyze the impact of energy consumption, economic growth, financial development, economic globalization along with poverty incidence measured by poverty head count ratio on carbon emissions in the Pakistan economy over the period of using ADF unit root test followed by a 28

4 cointegration test and a granger causality test.they found both long run and short run causality between the variables. Nasreen and Anwar (2014) uses the data of 15 Asian countries covering the period of to examine the causal relationship between economic growth, trade openness and energy consumption applying panel cointegration and causality approaches. Empirical results of the study confirm the presence of cointegration between variables. The impact of economic growth and trade openness on energy consumption is found to be positive. The panel Granger causality analysis used in the study reveals the bidirectional causality between economic growth and energy consumption, trade openness and energy consumption. Acaravci1 et al, (2015) conducted a studywere they aimed to explore the long-run and causal relationships between electricity consumption per capita, real gross domestic product (GDP) per capita, trade openness and foreign direct investment inflows per capita in Turkey for the period of The study uses the autoregressive distributed lag model and the augmented Granger causality model where the bounds F- test for cointegration test yields evidence of a long run relationship between variables. The overall results from the three error-correction based Granger causality models showed that there is a unidirectional short-run, long-run and strong causalities running from the electricity consumption per capita to real GDP per capita. However, there is no causal evidence from the real GDP per capita to electricity consumption per capita. This result also supportedthat; growth hypothesis is confirmed in Turkey. As a policy implication the study suggested that the energy growth policies regarding electricity consumption should be adopted in such a way that the development of this sector stimulates economic growth. Lean and Smyth (2010) examines the causal relationship between economic growth, electricity generation, exports and prices in a multivariate modelemploying annual data for Malaysia from 1970 to The study found that there is unidirectional Granger causality running from economic growth to electricity generation. The policy implication of the result suggested by the study is that electricity conservation policies, including efficiency improvement measures and demand management policies, which are designed to reduce the wastage of electricity and curtail generation can be implemented without having an adverse effect on Malaysia s economic growth. Sadorsky (2010) uses panel cointegration data estimation techniques to examine the impact of trade on energy consumption in a sample of 8 Middle Eastern countries covering the time period of 1980 to Short-run dynamics of the study show Granger causality from exports to energy consumption, and a bi-directional feedback relationship between imports and energy consumption. Long run elasticities estimated from FMOLS also show that a 1% increase in per capita exports increases per capita energy consumption by 0.11% whereas, a one percent increase in per capita imports increases per capita energy consumption by 0.04%. The study suggested thatthese results are important in establishing that increased trade affects energy demand in the Middle East in both the short and long-run. It also has implications for energy policy and environmental policy. 29

5 Shahbaz et al, (2013) conducted another study exploring the effects of financial development, economic growth, coal consumption and trade openness on environmental performance using time series data over the period in context of South Africa. To test the long run relationship among the variables the ARDL bounds testing approach to cointegration has been used. However, short run dynamics have been investigated by applying error correction method (ECM). The unit root properties of the variables were examined by applying Saikkonen and Lütkepohl (2002. Econometric Theory 18, ) structural break unit root test. The findings of this study confirmed long run relationship among the variables. The results showed that a rise in economic growth increases energy emissions, while financial development reduces it. It also reveals that coal consumption has significant contribution to deteriorate environment in South African economy. The empirical results of the study also verified the existence of environmental Kuznets curve. Karanfila and Li, (2015) examined the long- and short-run dynamics between electricity consumption and economic activities in a study where they used panel data of per capita electricity consumption and per capita GDP of 160 countries for the time period of using panel data analysis, accounting for the degree of electricity dependence and the level of urbanization. In order to capture the differences in this relationship in the study the full sample has been divided into various subsamples based on countries' income levels, regional locations and OECD memberships. This framework is argued and found to be appropriate since the causal links and inferences arising therefrom differ considerably among the subsamples, which led them to conclude that the electricity-growth nexus is highly sensitive to regional differences, countries' income levels, urbanization rates as well as supply risks. We can see that most of the papers mentioned above and most of the papers altogether have not taken electricity consumption as a special variable or have not shown any relationship between trade liberalization and electricity consumption but has rather shown the impact of lade liberalization on energy consumption as a whole thus skipping out our research question. The main hypothesis of this paper was based on this gap- Trade liberalization leads to more electricity consumption ; to find the causal relationship between them and the direction of the causality based on our data set. We have provided a summary of some of the recent studies based on trade liberalisation, energy consumption and electricity consumption as discussed above in the following table (See Appendix) 3. Overview of Trade and Electricity Consumption in Bangladesh Bangladesh has experienced rapid growth ofenergy consumption over the last two decades. This trend will intensify further more in the upcoming years as economic growth and development efforts accelerate Bangladesh strives to become a middleincome country by Development experience in other countries shows that energy consumption tends to rise quickly when per capita income reaches between US$1,000 and US$10,000, a range that Bangladesh is currently attaining. Energy supply must thus increase rapidly in order to sustain the country s growth momentum. Energy is a key resource for economic growth in any country and a key ingredient in improving the socioeconomic conditions in poorer ones. In Bangladesh, electricity is the most widely 30

6 used form of energy. However, since independence from Pakistan in 1971, the country has struggled to generate adequate amount of electricity to meet demand. Meanwhile, state-owned electricity utilities suffer from large deficits. The energy sector has also failed to attract adequate private investments due to poor pricing policies. This lack of investment is a major contributing factor to Bangladesh s energy crisis. The government has committed to ensuring access to affordable and reliable electricity for all citizens by However, at present only about half of the population has access to electricity, although supply is hardly reliable. To improve the situation, the government has adopted a comprehensive energy development strategy to explore supply-side options along with demand management that conserves energy and discourages inefficient use. In the case of electricity, the country s total installed generation capacity was 2,350 million watts (MW) while the derated capacity was 1,719 MW in FY1992 (Annual Report, BPDB). The installed capacity increased to 8,819 MW in FY2012 with the corresponding derated capacity of 8,149 MW. One important aspect of recent developments is that a significant portion of the additional electricity generation has come from liquid fuel-based power plants (diesel, high-speed furnace oil) which, as we have seen earlier, have raised the total contribution of liquid fuels in power generation to 17 per cent in FY2012, up from only 5 per cent in FY2009. Electricity generation in Bangladesh is overwhelmingly natural gas-based. In FY2011, nearly 82 per cent of the evening peak electricity was generated using natural gas, 12.6 per cent by liquid fuel, 2.5 per cent by coal and 2.8 per cent by hydro. In FY2010, the power generation mix was somewhat different, in that the contribution of natural gas was 89 per cent while the share of liquid fuel was only 5 per cent. The change in the fuel mix of electricity generation has significant implications for cost structure and total subsidy cost. The use of liquid fuel high-speed diesel and furnace oil has increased significantly in the last two years, which has, in turn, increased the per-unit generation cost of electricity in FY2011 and FY2012. Under the short-term plan, a number of power plants have been installed through the quick rental processes to meet the demand for electricity. The use of liquid fuel was also introduced in power generation to diversify the energy mix. This, however, resulted in a huge cost escalation in electricity production because of the use of expensive imported liquid fuel. In order to reduce the production cost, policy emphasis now is on installing large power plants based on indigenous and imported coal as the primary fuel. Under the present plan, 12 large power plants based on natural gas, liquid fuel, dual-fuel and coal having a total capacity of 3,146 MW will be installed in different parts of the country by The BPDB has also taken initiatives to repair and renovate its 26 power units, which are 15 to 25 years old and have a total capacity of generating 1,679 MW of electricity. Steps have also been taken to set up substations and transmission lines at different voltage levels to transmit electricity generated in the power plants to the load centres. According to the Power Division of the Ministry of Power, Energy and Mineral Resources (MoF, 2012), as of September 2013, a total of 57 plants with a capacity of about 4,432 MW have been commissioned, 33 plants with a capacity of 6,569 MW are under construction. 19 projects with a capacity of about 3,974 MW are under the 31

7 tendering process and nine plants with a capacity of 3,542 MW are at initial stages. The access to electricity has been raised from 47 per cent to 62 per cent (including renewable energy) in the country and per capita electricity generation increased from 220 kwh in 2009 to 321 kwh in 2013.The government s reform agenda for the electricity sector primarily intends to mitigate the acute supply-demand gap in electricity for which immediate, short-, medium- and long-term plans have been adopted under the PSMP According to MoF (2013), from January to December 2011, a total of 920 MW of power was planned to be added to the national grid under the short-term plan. Subsequently, the plan was slightly revised and it was stipulated that by December 2011, a total of 2,194 MW of additional electricity would be supplied to the national grid through the installation of 11 power plants in the public sector and 16 in the private sector. In practice, a total of 1,763 MW power was added to the national grid, of which 800 MW was obtained from 10 public sector power plants and 963 MW from 12 private sector power plants. These initiatives culminated in the implementation of more than 80 per cent of the Short Term Plan. Up to April 2012, a total of 3,268 MW of electricity has been added to the national grid. In the medium term, the plan has been to install power plants with a total of 8,622 MW, of which 1,986 MW would be installed in 2012, 3,339 MW in 2013 and 3,297 MW in According to the revised Plan, steps have been taken to install power plants with a total capacity of 8,622 MW, of which 951 MW would be installed in 2012, 2,013 MW in 2013 (including 500 MW of import) and 1,988 MW in 2014 to ensure electricity for all by 2021has been made in the long term plan, the revised Plan aims to install power plants with capacities of 2,701 MW in 2015, 2,914 MW in 2016 and 3,250 MW in Data Set, Model Variables and Methodology At first, data of all the variables were tested for unit root in order to determine the stationarity of the variables that were considered in our study. The ADF unit root test was used to detect possible existence of unit roots, if any, in our data set. Once the variables were found to be stationary, cointegration test was run to find possible linear combinations of the variables which could be considered stationary. Moreover, following confirmation of cointegration between the concerned variables we finally used the Granger Causality tools for determining the direction of causalities between the variables. It is important to test data, especially time series data, for stationarity since non stationarity of time series data leads to spurious regression unless there is the existence of at least one cointegrating relationship. It is important to mention that unit root tests tend to have non-standard and non-normal asymptotic distributions, which are highly affected as the deterministic terms such as constant, time trend etc. are included. A time trend is considered as an extraneous regressor and the power of the test could be reduced by its inclusion. However, if the true data generating process were trend stationary, then failing to include a time trend could also result in a reduction in power of the test. Moreover, this loss of power due to the exclusion of a time trend when it should be present is more severe than the reduction in power associated with the inclusion of a time trend when it is extraneous (Lopez et al., 2005). While conducting the unit root test, it is important to choose the optimum lag length. The software Eviews 7.1 used in our 32

8 paper automatically chooses the appropriate lag length based on the Schwartz Information Criterion (SIC). Furthermore, the Johansen procedure was applied to test for cointegration, which is known to provide a unified framework for estimation and testing of cointegration relations in the context of VAR error correction models. We estimated an Unrestricted Vector of Autocorrelation of the following form for this purpose: x = + x + x + x + + x + θ x + u t α θ1 t 1 θ 2 t 2 θ3 t 3 LL θ k 1 t k+ 1 Where is the difference operator; xt is a (n x 1) vector of non-stationary variables (in levels); and Ut is the (n x 1) vector of random errors. The matrix θk contains the information on long run relationship between variables, for instance, if the rank of θk = 0, the variables are not cointegrated. On the other hand if rank (usually denoted by r) is equal to 1, there exists one cointegrating vector and finally if 1 < r < n, there are multiple cointegrating vectors. Johansen (1990) derive two tests for cointegration, namely the trace test and the maximum Eigen value test. The trace statistic test evaluates the null hypothesis that there are at most r cointegrating vectors whereas the maximal Eigen value test, evaluates the null hypothesis that there are exactly r cointegrating vectors in xt. According to cointegration analysis, when two variables are cointegrated then there exist at least one direction of causality. Granger-causality, introduced by Granger (1969, 1980, 1988), is one of the important matters that has been much studied in empirical macroeconomics and empirical finance. The presence of non-stationarity can lead to ambiguous or misleading conclusions in the Granger causality tests (Engel and Granger, 1987). Only when the variables are cointegrated, it is possible to deduce that a long run relationship exists between the non-stationary time series. When we take y and x as our variables of interest, then the Granger causality test (Granger, 1969) determines whether past values of y add to the explanation of current values of x as provided by information in past values of x itself. If previous changes in y do not help explain current changes in x, then y does not Granger cause x. In a similar way, we can examine if x Ganger causes y just be interchanging them and carrying out this process again. There could be four probable outcomes: (i) x Granger causes y (ii) y Granger causes (iii) Both x and y granger causes the other and (iv) neither of the variables Granger causes the other. In this paper, the causality tests among all the concerned variables are conducted. For this the following two sets of equation are estimated: x + u t = α 0 + α1xt 1 + LL + α l xt l + β1yt 1 + LL + βl yt l t = α 0 + α1 yt 1 + LL + α l yt l + β1xt 1 + LL + βl xt l y + v k t k t t t We consider the above sets of equation for all possible pairs of (x, y) series in the group. The reported F-statistics are the Wald statistics for the joint hypothesis. 33

9 We resorted to use of the Eviews 7.1 software for carrying out all econometric tests in our study.the variables are trade openness(open), electricity consumption (EC) and energy use (EU). All the dataa of our variables were collected from World Development Index (WDI) from The following graphs of Figure 1 show a trends from the tests at levels but at first difference form there is no trend. Figure 1: Trends of Variables 34

10 5. Results At first tests were carried out to check the stationarity of data set using ADF 1 unit root test. The motive is to find out whether our variables are stationary or not at their first level. In order to do so we assumed a hypothesis considering the data series to be nonstationary and integrated. If there is clear proof of rejection, it is only then we could reject our hypothesis. Table 1 shows the ADF statistics and corresponding p-values of all the variables in their level and first difference forms. Table 1: Augmented Dickey Fuller (ADF) Unit Root Test (lag=8) Panel 1: Levels - I(0) Variables ADF Statistics (only constant) Prob. Value ADF Statistics (constant and trend) Prob. Value Decision on stationarity OPEN Non-stationary considering both constant and constant and trend EC Non-stationary considering both constant and constant and trend EU Non-stationary considering both constant and constant and trend Panel 2: First Difference - I(1) Variables ADF Statistics (only constant) Prob. Value ADF Statistics (constant and trend) Prob. Value Decision on stationarity OPEN Stationary considering both constant and constant and trend EC Stationary considering both constant and constant and trend EU Stationary considering both constant and constant and trend The ADF test results show that all the variables are stationary at their first difference form, I(1).Following the ADF test we then ran the Johansen cointegration test to see whether the variables were cointegrated or not. Details of findings from Johansen cointegration test are shown in table 2 below. 1 For ADF test our hypothesis was that unit root exists or the data is non-stationary. We can reject this hypothesis only if our t- statistic is less than the test critical value at 10% level of significance 35

11 Table 2: Johansen Cointegration Test Results (Lag = 1) Johansen Test for Cointegration (Trace Test) Null Alternative Trace Statistic 95% Critical Value Conclusion r = 0 r = r <=1 r = cointegrating equations r <=2 r = Johansen Test for Cointegration (Maximum Eigenvalue Test) Null Alternative Max-Eigen Statistic 95% Critical Value Conclusion r = 0 r = r <=1 r = No cointegrating equation r <=2 r = The results reveal that there is long-run cointegration between the variables. Finally, after checking all the variables for stationarity and cointegrity, then Granger Causality Test was run to understand the direction of causalities between our variables. Results from this test are shown below in table 3. Table 3: Granger Causality Test Results (Lag=2) Null F- Statistic P-Value Conclusion Unidirectional causality ECOPEN EC does not Granger cause OPEN OPEN does not Granger cause EC EU does not Granger cause OPEN OPEN does not Granger cause EU EU does not Granger cause EC EC does not Granger cause EU Unidirectional causality EU OPEN Unidirectional causality EC EU The Granger causality results shows both electricity consumption and energy use strongly causes trade openness in long-run.therefore, the initial hypothesis stating 36

12 trade liberalization causes energy consumption and electricity consumption was rejected, supporting that in actual case electricity consumption leads to lade liberalization.. 6. Conclusion There is a growing literature that examines the causality relationship between electricity consumption and trade openness. The empirical results have yielded resultrelated to the causal relationship between electricity consumption and economic growth. This study may be considered as a complementary study to the previous studies. However, there is no specific study done on it earlier and more specially taking electricity consumption as a variable which contributes to the body of knowledge. This paper investigates both the long-run and causal relationships between trade openness and electricity consumption, in Bangladesh for the period of by using ADF unit root test where the P values and the coefficientsof the test implies that any reducing in electricity consumption will negatively affect the economic growth. The Johansen Cointegration Test results reveal that there are three cointegrating equation between the variables. According to the Granger causality results, both electricity consumption and energy use strongly causes trade openness in long-run. The results also show that: (i) There is evidence of a unidirectional long-run causality running from the electricity consumption to trade openness, (ii) there is evidence of a unidirectional long-run causality running from energy use to trade openness, (iii) there are evidences of longrun causalities running from the electricity consumption to energy use. This gives answers our research question. Thus the initial hypothesis of this research is rejected and has given knowledge that implies that higher electricity consumption tends to have higher trade openness, but not the reverse case in Bangladesh that is a unique finding of this paper overall compared to the other studies.as a conclusion, energy conservation policies, such as rationing electricity consumption, are likely to have an adverse effect on real GDP of Bangladesh. One of the limitations of this research was the sample size and also that it was difficult to distinguish between total energy use and electricity usage separately in many cases when we were looking for some previous literature to have a deeper understanding of the impacts of it on trade openness as there were not much studies done related too it here in Bangladesh specifically. In future we will go with panel study including other related variables. So we recommend future researchers to conduct more studies regarding this as it needs to gain more attention and we hope our paper will provide at least some idea. 37

13 References Acaravci, A. 2010, Structural breaks, electricity consumption and economic growth: Evidence from Turkey, Journal for Economic Forecasting, Vol. 2, Pp Acaravci, A., Ozturk, I. 2012, Electricity consumption and economic growth nexus: A multivariate analysis for Turkey, Amfiteatru Economic, Vol. 14, No. 31, Pp Aktas, C., Yilmaz, V. 2008, Causal relationship between electricity consumption and economic growth in Turkey, Zonguldak Karaelmas Üniversitesi Sosyal Bilimler Dergisi, Vol. 4, No. 8, Pp Altinay, G., Karagol, E. 2005, Electricity consumption and economic growth: Evidence from Turkey, Energy Economics, Vol. 27, No.6, Pp Aslan, A. 2013, Causality between electricity consumption and economic growth inturkey: An ARDL bounds testing approach. Energy Sources Part B: Econ Plan Policy, Vol. 9,No.1, Pp Aslan, A. 2014, Electricity consumption, labor force and GDP in Turkey: Evidence from multivariate Granger causality. Energy Sources, Part B: Economics, Planning, and Policy, Vol. 2, Pp Bernard J.T. and Roland, M.1997, Rent dissipation through electricity prices of publicly owned utilities Can. J. Econ., Vol. 30, Pp Bayar, Y, 2014, Electricity Consumption and Economic Growth in Emerging Economies Journal of Knowledge Management, Economics and Information Technology, Vol.4, Pp.1-18 Chen, S., Kuo, H., Chen, C., 2007, The relationship between GDP and electricity consumption in 10 Asian countries, Energy Policy, Vol. 35,Pp CF., Abosedra, S. 2014, The impacts of tourism, energy consumption and political instability on economic growth in the MENA countries. Energy Policy Vol. 68, Pp Dhawan, U., Biswal, B. 1999, Re-examining the export-led growth hypothesis: A Multivariate cointegration analysis for India, Applied Economics,Vol.31, Pp Borenstein, S.2002, The trouble with electricity markets: understanding California's restructuring disaster, Journal of Economic Perspectives, Vol. 14, Pp Karanfil, F. 2008, Energy consumption and economic growth revisited: Does the size of the unrecorded economy matter?, Energy Policy, Vol. 36, Pp Lee, C.C. 2005, Energy consumption and GDP in developing countries: a cointegrated panel analysis, Energy Economics, Vol. 27, Pp Narayan, P.K. and Smyth, R. 2009, Multivariate granger causality between electricity consumption, exports and GDP: Evidence from a panel of Middle Eastern countries, Energy Policy, Vol. 39,Pp S. Ambec, C. Crampes, Electricity provision with intermittent sources of energy Resource, Energy Econ., Vol. 34, Pp Sadorsky, P. 2011, Trade and energy consumption in the Middle East, Energy Economics, Vol. 33,Pp Sadorsky, P. 2012, Energy consumption, output and trade in South America, Energy Economics, Vol. 34: Pp Shahbaz, M., Khan, S., Tahir, M.I. 2013, The dynamic links between energy consumption, economic growth, financial development and trade in China: Fresh 38

14 evidence from multivariate framework analysis, Energy Economics, Vol.40, Pp Shahbaz, M., Zeshan, M., Afza, T. 2012, Is energy consumption effective to spur economic growth in Pakistan? New evidence from bounds test to level relationships and Granger causality tests, Economic Modelling, Vol.29, Pp Srinivasan, S. 2013, Electricity as a traded good, Energy Policy, Vol. 62, Pp

15 Appendix Trade Liberalization and Electricity Consumption Authors Countries and Variables Used Methodology Findings Period of Study Shahbaz et al, (2014) Nazarzedeh et al, (2015) Lean and Smyth (2014) Ali et al, (2015) 91 countries OPEC countries, Bhutan, Pakistan Trade openness, energy consumption Energy consumption, Trade Electricity consumption, International trade, economic growth Energy consumption, economic growth, financial development, economic globalization Panel unit roots, There is a LLC unit root tests, IPS unit root test, cointegration between the MW unit Root Test, variables; The Panel cointegration test, Panel causality relationship between trade test, Homogenous openness and non-causality test, energy Homogenous consumption is causality test, inverted U-shaped Heterogeneous in high income causality test, counties and U- heterogeneous shaped in middle non-causality test, and low income countries, Bidirectional causality between trade openness and energy consumption. Panel data Statistically estimation significant relationship between energy consumption and trade. An increase in trade affects energy demand. Granger causality Unidirectional test Granger causality running from electricity consumption to economic growth. ADF unit root test Long-run, shortrun causality have been found between the variables. 40

16 Trade Liberalization and Electricity Consumption (Continued) Nasreen and Anwar, (2014) Acaravci et al, (2015) HooiHooi Lean, Russell Smyth (2010) 15 countries Asian countries, Turkey, Malaysia, Economic growth, trade openness and energy consumption electricity consumption per capita, real gross domestic product (GDP) per capita, trade openness and foreign direct investment. economic electricity generation, and prices growth, exports Panel cointegration, Granger causality test Autoregressive distributed lag model and the augmented Granger causality model Granger causality test Cointegration between variables, bidirectional granger causalitybetween economic growth and energy consumption Trade openness and energy consumption. Long-run Relationship between variables, unidirectional shortrun, long-run and strong causalities running from the electricity consumption per capita to real GDP per capita Unidirectional Granger causality running from economic growth to electricity generation. Perry Sadorsky (2011) Shahbaz et al, (2013) Karanfila and Li (2015) 8 middle-eastern countries, South Africa, countries Trade, energy consumption financial development, economic growth, coal consumption and trade openness electricity consumption, economic activities panel cointegration data estimation ARDL bounds testing, ECM, Saikkonen and Lütkepohl structural break unit root test Panel data analysis Causality from exports to energy consumption, bidirectional feedback relationship between imports and energy consumption long run relationship among the variables, a rise in economic growth increases energy emissions, while financial development reduces, electricity-growth nexus is highly sensitive to regional differences, countries income levels, urbanization rates and supply risks. 41