Renewable, NonRenewable Energy Consumption, Economic Growth and CO2 emission: Evidence for Iran Soheila Khoshnevis Yazdi1Bahman Khanalizadeh 2 Nikos Mastorakis3 Abstract In this study, we employ ARDL approach to provide evidence on the CO2 emissions, economic growth and coal consumption renewable energy consumption, nonrenewable energy consumption and population in Iran using for an extended time period, i.e. from 1975 2011. Furthermore, the causal relationships among the variables are further examined using the Granger causality test. Our empirical results suggest that the variables are cointegrated in the case of Iran. In other words, there is a longrun relationship between CO2 emissions, economic growth and. Granger causality test reveal a strong evidence of bidirectional causality running from economic growth to CO2 emissions. Moreover, there is bidirectional causality between economic growth and renewable energy consumption in the short and long run. Keywords ARDL.CO2 emissions, Economic growth, Iran, Non Renewable Energy, Renewable Energy, I. INTRODUCTION NERGY is also an essential factor of production E[1]. All production involves the transformation or movement of matter in some way and all such transformations require energy. Some aspects of organized matter that is information might also be considered to be nonreproducible inputs. Energy as a factor of production must be incorporated into machines, workers, and materials in order to be made useful. This provides a biophysical justification for treating capital; labor etc. as factors of production [2]. Energy plays a crucial role in the economic 1. S. Koshnevis Yazdi is with the Department of Economics College of Law, Political Science & Economics Islamic Azad University, South Tehran Branch, Tehran, No.223, North iranshahr St, Tehran, Iran. Also with the Post Doc Project WSEAS Research Department, WSEAS,A.I.Theologou 1723, Zografou, 15773, Athens, GREECE (corresponding author to provide phone:00989128441103; email: soheila_khoshnevis@ yahoo.com). 2.B. Khanalizadeh is faulty the Department of Economics College of Law, Political Science & Economics Islamic Azad University, South Tehran Branch, Tehran, No.223, North iranshahr St, Tehran, Iran. (khanali.bahman@yahoo.com). 3.Nikos Mastorakis is Technical University of Sofia, Department of Industrial Engineering, Sofia Bulgaria (mastor@tusofia.bg) development of a country. It enhances the productivity of factors of production and increases living standards. It is extensively recognized that economic development and energy consumption are interdependent]. Energy demand, supply and pricing impact on the socioeconomic development, the living standards and the overall quality of life of the people [3]. On the other hand, higher level of economic development could induce more energy consumption. The increasing concern over global warming and growing carbon dioxide (CO2) emissions in developing countries like India, associations between environmental pollutants and economic activities in developing countries have received more and more attention. Unfortunately, despite the abundance of research on this topic, the empirical literature has not yet reached a consensus about whether economic growth is the cause or effect of energy consumption, and whether economic growth is the solution or source of environmental pollution problem (known as EKC hypothesis). Thus, further investigation of the relationship between, carbon emissions, and economic growth appears warranted. II. LITERATURE SURVEY The causal relationship between co2 emission and economic growth is a wellstudied topic in the energy economics literature. A large number of studies show the causal relationship between both variables; conducted for developing and developed economies. Kraft and Kraft [4] tested for causality between energy consumption and GNP for this country for the 194774 periods by utilizing Sims methodology. They concluded unidirectional causality running from GNP to energy consumption. Energy plays a vital role in economic development. It performs a key for sustainable development. The findings of the previous studies suggested that the results are inconclusive depending on the methods and time period employed. Alam et al. [5] investigated the causal relationship between energy consumption, CO2 emissions and income in India. Evidence supported bidirectional causality between energy consumption and CO2 emissions in the long run. However, there was no causality between ISBN: 9781618042392 399
energy consumption and income as well as between CO2 emissions and income. Thus, the authors conclude that energy conservation policies could be implemented without affecting economic growth. In addition, reducing CO2 would be less easy in India due to the absence of causality in any direction. Managi [6] indicated that economic growth and the decrease of environmental degradation are compatible in accordance to the Environmental Kuznets Curve (EKC) hypothesis. It has been proposed that an inverted U shaped relationship between economic growth and environmental pollution is empirically associated with smaller levels of pollution after some threshold income point. On using a simple regression analysis Li et al. [7] used panel unit root, heterogeneous panel cointegration and dynamic OLS methods to examine the relationship between energy consumption and economic growth for 30 provinces in China. Their results showed that there was a positive longrun relationship between energy consumption, CO2 emissions and economic growth. They found that a 1% increase in GDP per capita increased the energy consumption and CO2 emissions by 0.5% and 0.43% respectively. Richmond and Kaufmann (2006) investigate the EKC for CO2 using panel data for OECD countries and note that there is limited support for the EKC in the case of OECD countries. Mallick [8] investigates the link between energy use and economic growth using the Granger causality test on India's annual data for the period of 1970 to 2005. The tests suggest that economic growth fuels increased demand for both crude oil and electricity consumption while the growth in coal consumption drives economic growth. The variance decomposition analysis of Vector Auto regression (VAR) however suggests the possibility of a bidirectional influence between electricity consumption and economic growth. Sadorsky [9] uses vector auto regression techniques to study the relationship among renewable energy consumption, income, oil prices and CO2 emissions in the Group of 7 (G7) countries over the period of 1980e2005. The results show that in the longrun, an increase in real GDP per capita and carbon dioxide emissions per capita are found to contribute in increase in G7 renewable energy consumption per capita. Menyah and WoldeRufael [10] uses vector autoregression technique to study the relationship among CO2, renewable energy, nuclear energy and real output for the US over the period of 1960e2007. They find unidirectional causality running from nuclear energy consumption to CO2 emissions but no causality is found between renewable energy consumption and CO2 emissions. Boopen et al. [11] analyze the relationship between GDP and Carbon dioxide emission for Mauritius over the time period 19752007. The results reveal that GDP is linked with carbon dioxide emission and have a significant negative relationship between them. Shahbaz et al. [12] examine the relationship between income and energy consumption for Romania by using annual data for the time period 1980e2008. The results suggest a positive association between the real output and energy consumption in Romania. They further identified a strong positive correlation between the nonrenewable energy consumption and the carbon dioxide emissions. Li et al. [13] examined the relationship between CO2 emissions, energy consumption and economic development using a panel data of 28 provinces in China. The result confirmed the cointegration relationship among the three variables. Moreover, the study found evidence of bidirectional causality between CO2 emissions and energy consumption as well as between energy consumption and economic growth. In addition, energy consumption and economic growth caused CO2 emissions in the long run while CO2 emissions and economic growth were the longrun causes of energy consumption. The organization of the remainder of this paper is as follows. The next section briefly describes the empirical methodology employed. Section 4 describes the data and analyzes the main empirical findings. Finally, the summary and the policy implications of our findings are outlined in Section. III. DATA This study uses annual time series data for Iran from 1975 to 2011. The period was chosen based on the availability for all the data series. Real GDP per capita (GDP) in constant 2005 US$, real per capita fixed capital formation population size measured by total of people Iran. Renewable energy consumption in quadrillion Btu units is measured as wood, waste, geothermal, wind, photovoltaic, and solar thermal energy consumption. Nonrenewable energy sources include coal and coal products, oil, and natural gas. Therefore, in this study, nonrenewable energy consumption is measured as the aggregate of the consumption of all of these sources in quadrillion Btu units [14]. All data are from the World Development Indicators (WDI) online database [15]. All the variables transformed to natural logarithms for the purpose of the analysis. We have used Microfit 4 and Eviews 7.1 to conduct the analysis. 3.2 Model Following the recent empirical works it is possible to test the longrun relationship between CO2 emission and economic growth, renewable energy consumption, nonrenewable energy consumption and the total population size in a linear logarithmic using the following equation: (1) ISBN: 9781618042392 400
In order to find the longrun relationship between variables, the following linear logarithmic form is proposed: equation as an additional independent variable. The following model was employed to test the causal relationship between the variables Equation 3: (2) Following the recent empirical works it is possible to test the longrun relationship between variables. = + IV.. ECONOMETRIC APPROACH A. Unit Root Test First, the Augmented Dickey Fuller (ADF) tests are used to check whether each data series is integrated and has a unit root. The ADF test is based on the value of tstatistics for the coefficient of the lagged dependent variable compared with special calculated critical values. If the calculated value is greater than the critical value, then we reject the null hypothesis of a unit root; the unit root does not exist, and our variable is stationary [16]. B. Cointegration Analysis Engle and Granger [18] provided a totally new method for analyzing time series. It is well known that a time series model can only be built once the included series in the model are stationary. There is a great advantage in finding (longterm) cointegration relationships, as the series need no longer be transformed and, hence, the forecasting power increases substantially. The existence of longrun equilibrium (stationary) relationships among economic variables is referred to in the literature as cointegration. Engel and Granger pointed out that a linear combination of two or more nonstationary variables may be stationary. If such a stationary combination exists, then the nonstationary time series are said to be cointegrated. C. Granger Causality Analysis Cointegration tests are only able to indicate whether the variables are cointegrated and whether a longrun relationship exists between them. To test the direction of causality between CO2 emission, economic growth, renewable energy consumption, nonrenewable and population. The Granger [17] approach based on the Vector Error Correction Model (VECM) is employed. Considering each variable in turn as a dependent variable for model. The residuals obtained from estimating the longrun relationship between the variables in nonrenewable and renewable energy use models are used as dynamic error correction terms in the above equations. The test answers the question of whether x causes y or y causes x. x is said to be Granger caused by y if y helps in the prediction of the present value of x or equivalently if the coefficients on the lagged y s are statistically significant. In the presence of longrun relationship between variables in the model, the lagged Error Correction Term (ECMt1) was obtained from the longrun cointegration relationship and was included in the (3) ECMt1 is the lagged errorcorrection term. Residual terms are uncorrelated random disturbance term with zero mean and j s are parameters to be estimated. The direction of causality can be detected through the VECM of longrun cointegration. The VECM captures both the shortrun and the longrun relationships. The shortrun causality is based on a standard Ftest statistics to test jointly the significance of the coefficients of the explanatory variable in their first differences. The long run causality is based on a standard ttest. Negative and statistically significant values of the coefficients of the error correction terms indicate the existence of longrun causality. V. RESULTS AND DESCRIPTIVE STATISTICS In this empirical study we used Augmented DickeyFuller Stationary unit root tests to check for the integration order of each variable. We apply unit root tests to ensure that no variable is integrated at I (1) or beyond. We have used the ADF unit root test to check for stationarity. The results in Table 1 indicate that all variables are nonstationary at their level form and stationary at their first differences. Variabl e Table 1 Augmented DickeyFuller Stationary Test Results Constant No Trend Critical Value Variable Constant No Trend Critical Value Ln CO2 0.159886 2.945842 Ln CO2 4.723295 2.948404 Ln GDP 0.663392 2.954021 Ln GDP 3.720542 2.954021 Ln REC 2.665572 2.945842 Ln REC 5.703516 2.948404 Ln NREC 0.517503 2.945842 Ln NREC 5.673434 2.948404 Ln POP 9.604951 2.945842 Ln POP 3.764502 3.544284 The number inside brackets denotes the appropriate lag lengths which are chosen using Schwarz Criterion. * Denotes for 5% significance level Source: Author's Estimation using Eviews 7.1 ISBN: 9781618042392 401
The null hypotheses of no cointegration are rejected, implying longrun cointegration relationships amongst the variables. These variables share a common trend and move together over the long run. All estimated coefficients can be interpreted as longrun elasticities, given that variables are expressed in natural logarithms in table 2. Dependent Variable: Ln CO2 Table 2 Longrun Estimation Results Variable Coefficient Std. Error TStatistic Prob Ln GDP 0.72 **.14364 4.9746 Ln REC.044.034675 1.2588 [0.219] Ln NREC 0.18 **.056619 3.2805 [0.003] Ln POP 0.97 **.15992 6.0739 C 16.34** 2.5635 6.3735 Note** significant at 5 % level Source: Author's calculation using Microfit 4 The VECM is set up for investigating short and longrun causality. The optimum lags are selected relying on minimizing the Akaike Information Criterion (AIC). The maximum lag order two was set. With that maximum lag lengths setting, the ARDL (2, 0, 0, 0, 0) model is selected using AIC. ARDL (1, 0, 0, 0, 0) represents the ARDL model in which the income and GDP take the lag length 2. The longrun coefficients of GDP, REC, NREC and POP estimated from these techniques have the same magnitude at the 5% significance levels. For the model indicates that a 1% increase in real GDP increase CO2 emission by approximately 0.72% and a 1% increase in the nonrenewable energy consumption decrease real GDP by 0.18%. The coefficient on population (POP) shows a positive impact on CO2 emission in Iran. The elasticity of CO2 emission with respect to population ratio the long run 0.97, suggesting the contribution of increasing population to CO2 emission is significant during the estimation period. The longrun estimated coefficient related to renewable energy show that, a 1% increase in renewable energy consumption decrease CO2 emission by 0.44% and is statistically insignificant. Thus, renewable energy cannot play an important role in economic growth. The results of cointegration in Table 3 show that the F statistic is not greater than its upper bound critical value. However, we can conclude that cointegration is supported by the significantly negative coefficient obtained for ECM t1. The error correction mechanism (ECM) is used to check the shortrun relationship among the variables. The coefficient of ECM t1 is statistically significant at 5% level of significance which indicates that speed of adjustment for shortrun to research in the longrun equilibrium is significant. The error correction term is statistically significant and its magnitude is quite higher indicates a faster return to equilibrium in the case of disequilibrium. This term shows the speed of adjustment process to restore the equilibrium. The relatively high coefficients imply a faster adjustment process. The values of the coefficients of ECMt1 (0.70) indicating that the variables will adjust to the longrun equilibrium in about 1.4 period following a shortrun shocks. The coefficient of REC is negative and insignificant. The shortrun elasticity of renewable energy with respect to CO2 emission is negative and statistically insignificant. The coefficient value of 0.18 suggests that a 1% nonrenewable energy consumption leads to around 0.18% decrease in CO2 emission. Table 3. Error correction model (ECM) for shortrun elasticity ARDL (2,0,0,0,0)selected based on Akaike Information Criterion Dependent Variable: D(Ln CO2) Variable Coefficient TStatistic Probability D Ln GDP 0.71 4.9746 D Ln REC.044 1.2588 [0.219] D Ln NREC 0.18 3.2805 [0.003] D Ln POP 0.97 6.0739 C 16.34 6.3735 [0.000} ECM ( 1) 0.70 5.8215 The ShortRun Diagnostic Test Results RSquared.62735 Akaike info Criterion Schwarz Criterion 45.7963 40.3526 FStatistic 7.8562 [.000] DurbinWatson 2.0315 Note: **shows a percent level of 5%, *shows a percent level of 10%. Source: Author's calculation using Microfit 4 The coefficient of real GDP and population in shortrun are positive and significant. The elasticity of CO2 emission to CO2 emission is 0.71 which suggests that 1% increase in CO2 emission will lead to around 0.71% increase in real GDP in the shortrun in Iran. The existence of a cointegration relationship among the variables, as shown by the cointegration test, indicates ISBN: 9781618042392 402
that there is Granger causality in these variables in at least one direction, but it does not show the direction of this causality. Table 4 shows the results of error correction based Granger causality, including weak shortterm Granger causality and longterm Granger causality. ISBN: 9781618042392 403
Varia ble CO2 GDP REC NRE C POP CO2 16.3138** 4.3396** [0.037] 2.2962 [0.130] 1.2508 [0.263] Table 4 VECM Granger Causality results GDP 24.746 6** 4.8946* * [0.027] 3.4782 [0.062] 1.7753 [0.183] Shortrun REC 1.5845 [0.208] 2.9523 [0.086].15178 [0.697].32298 [0.570] NREC 10.7614** [0.001] 1.7206 [0.190] 1.1151 [0.291].079691 [0.778] x y means x Granger causes y. Note: **denote the statistical significance at the 5% levels. Source: Author's calculation using Microfit 4 POP 36.8924** 5.4980** [0.019] 4.5799** [0.032] 3.5266 [0.060] Long run ECM(1) 33.8898 ** 25.0021 ** 11.7392 ** [0.001] 4.1298* * [0.042] 1.2673 [0.260] The results of the causality test based on the VECM. Granger causality tests suggest a bidirectional flow, at 5% significance level, for real per capita GDP and CO2 emission in the Iranian case, in the shortrun; and a unidirectional flow, in the direction from nonrenewable energy to CO2 emission in the shortrun (at 5% level). The Granger causality test results funding the Also, these findings show that nonrenewable energy consumption and economic growth has unidirectional. There is also bidirectional causal relationship renewable energy consumption and CO2 emission. VI. CONCLUSION This study attempts to investigate the relationship between CO2 emissions, economic growth and renewable energy consumption, nonrenewable energy consumption and population in Iran using ARDL approach for an extended time period, i.e. from 1975 2011. The timeseries properties of the data were assessed using several unit root tests (ADF). Empirical findings indicate that both series are clearly nonstationary, as and I (1) process The analysis shows that CO2 emissions and GDP are cointegrated. This means that there is (possibly bidirectional) causality relationship between the two. Then, cointegration analysis revealed that there is a longrun relationship between GDP and CO2 emissions based on a VEC model after testing for multivariate cointegration between CO2 emissions and per capita GDP. The shortrun dynamics of the variables show that the flow of causality runs from CO2 emissions use to GDP, and there is a longrun bidirectional causal relationship (or feedback effect) between the two series. So, CO2 emissions and economic growth complement each other such that radical energy conservation measures may significantly hinder economic growth. This has important policy consequences, as it suggests that energy restrictions do not seem to harm economic growth in Iran Also, the growth in GDP per capita leads to a similar growth in energy consumption per capita We find evidence that energy consumption and economic growth move together In Iran. 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