CO2 emissions, energy consumption, and output in France

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1 Nanyang Technological University From the SelectedWorks of James B Ang 2007 CO2 emissions, energy consumption, and output in France James B Ang, Nanyang Technological University Available at:

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3 ARTICLE IN PRESS Energy Policy 35 (2007) Abstract CO 2 emissions, energy consumption, and output in France James B. Ang Department of Economics, Monash University, 900 Dandenong Road, Caulfield East VIC 3145, Australia Received 23 January 2007; accepted 23 March 2007 Available online 29 May 2007 This paper examines the dynamic causal relationships between pollutant emissions, energy consumption, and output for France using cointegration and vector error-correction modelling techniques. We argue that these variables are strongly inter-related and therefore their relationship must be examined using an integrated framework. The results provide evidence for the existence of a fairly robust longrun relationship between these variables for the period The causality results support the argument that economic growth exerts a causal influence on growth of energy use and growth of pollution in the long run. The results also point to a uni-directional causality running from growth of energy use to output growth in the short run. r 2007 Elsevier Ltd. All rights reserved. Keywords: Energy use; Pollutant emissions; Multivariate causality; France 1. Introduction The economy of France was severely affected by the oil shocks in the early 1970s. In the aftermath of the oil shocks, the French government took several important steps to restructure the country s energy policy, aiming to reduce its overwhelming reliance on imported petroleum supplies and to achieve energy independence. This has led to the development of nuclear programs for electricity generation. Per capita CO 2 emissions in France have been declining since Between 1980 and 2000, France s per capita CO 2 emissions declined at an average annual rate of 1.5%. The decline was mainly due to more extensive use of natural gas and nuclear power. France contributed to about 1.6% of the world s total CO 2 emissions in However, its CO 2 emissions are the lowest among the major Western European countries. Under the 1997 Kyoto Protocol, France is committed to reduce its CO 2 emissions back to the level in 1990 (i.e., 102 million metric tonnes). An increase in carbon tax has been proposed to help achieve this target. However, there has been no systematic time series investigation so far to analyse the relationship Tel.: ; fax: address: james.ang@buseco.monash.edu.au. between pollutant emissions, energy use and aggregate output for France. This paper is an attempt to fill the gap. The relationships between output and energy consumption, as well as output and environmental pollution, have been the subject of intense research over the past few decades. An assessment on the existing literature reveals that most studies focus on testing the nexus of either output-energy or output-pollution separately while no investigation has so far been made to examine these two links under the same framework. The main contribution of this paper is that for the first time an attempt is made to examine the dynamic relationship between pollutant emissions, energy consumption, and economic development under an integrated framework. Given that these three variables are strongly inter-related, the use of a naı ve bivariate framework may be subject to the problems of omitted variables bias. We investigate the case of France, one of the European economies with a huge increase in energy consumption, for the period We prefer a country-specific case study to a cross-sectional study since empirical analyses conducted at the aggregate level are unable to capture and account for the complexity of the economic environments and histories of each individual country. Hence, any inference drawn from these studies provides only a general understanding of how the variables are broadly related, /$ - see front matter r 2007 Elsevier Ltd. All rights reserved. doi: /j.enpol

4 ARTICLE IN PRESS J.B. Ang / Energy Policy 35 (2007) and the results cannot be generalized. This consideration highlights the importance of adopting an in-depth case study approach in order to inform appropriate analytical as well as policy debates in this subject. We formulate and estimate a vector error-correction model (VECM) using the full-information maximum likelihood method by treating all the underlying variables as endogenous variables. Unlike structural models, the proposed reduced-form model in this study does not require a priori information on parameters. The short-run dynamics are modelled appropriately in order to capture the long-run cointegrating relationship of these variables. Finally, to supplement the findings of the long-run cointegrated relationship, we also perform causality tests to shed light on the causal relationships between output and energy use, as well as output and pollutant emissions. The rest of the paper is organized in the following manner. An analytical framework is set out in Section 2. Section 3 describes the model, data and estimation methodology. Section 4 discusses the empirical findings, and the last section summarizes and concludes the paper. 2. Analytical framework Economic development is closely related to energy consumption since more energy consumption leads to higher economic development through the enhancement of productivity. However, it is also equally likely that more efficient use of energy, which could result in a reduction in energy consumption, may require a higher level of economic development. That is, better economic performance may be a catalyst for energy efficiency. As such, energy consumption and economic development may be jointly determined, and the direction of causality cannot be determined a priori. The importance of this nexus has been rigorously tested in the literature. In an influential study, Kraft and Kraft (1978) found a uni-directional causality running from output to energy consumption for the United States during the period Following their seminal work, the subsequent studies of Akarca and Long (1980), Yu and Choi (1985), Erol and Yu (1987), Abosedra and Baghestani (1989) and Hwang and Gum (1991), which differ in terms of the time period covered, country chosen, econometric techniques employed, and the control variables used in the estimation, have either confirmed or contradicted the results of Kraft and Kraft (1978). With the development of time series techniques, the recent studies of Masih and Masih (1996, 1997), Cheng and Lai (1997), Glasure and Lee (1998), Asafu-Adjaye (2000), Stern (2000), Yang (2000) Jumbe (2004), and Paul and Bhattacharya (2004) have tended to focus on the cointegrating relationship between income and energy consumption. The relationship between output and pollution level has also been well documented in the literature of Environmental Kuznets Curve (EKC). The EKC hypothesis postulates that the relationship between economic development and the environment resembles an inverted U-curve. That is, environmental damage first increases with income, then stabilizes and eventually declines. Antweiler et al. (2001) and Coxhead (2003) postulate that this non-linear relationship between environmental pollution and income levels can be explained by three factors: scale, composition, and technique effects. The scale effect occurs as pollution increases with the size of the economy. The composition effect refers to the change in the production structure of an economy from agriculture based to industry and service based that results in the reallocation of resources. Finally, the pollution income relationship also depends on techniques of production. An improvement in techniques of production, i.e., the technique effect, may reduce the amount of pollutant emissions per unit of production. Whether continued increase in national income brings greater harm to the environment is critical for the design of development strategies for an economy. Hence, a number of studies have attempted to assess the relationship between environmental pollution and income. The empirical results of Hettige et al. (1992), Cropper and Griffiths (1994), Selden and Song (1994), Grossman and Krueger (1995) and Martinez-Zarzoso and Bengochea-Morancho (2004) are consistent with the EKC hypothesis. However, higher national income does not necessarily warrant greater efforts to contain the emissions of pollutants. The empirical results of Holtz-Eakin and Selden (1995) and Shafik (1994) show that pollutant emissions are monotonically increasing with income levels. Given that energy consumption has a direct impact on the level of environmental pollution, the above discussion clearly highlights the importance of linking these two strands of literatures together. Hence, to avoid problems of misspecification, these two hypotheses must be tested under the same framework. 3. Model, data and methodology 3.1. Model and data In the above discussions, we have seen that energy consumption is a key determinant of CO 2 emissions. On the other hand, output and CO 2 emissions have a nonlinear quadratic relationship according to the EKC hypothesis. Hence, the long-run steady-state relationship between CO 2 emissions, energy use, and output can be specified as follows: C t ¼ b 0 þ b 1 E t þ b 2 G t þ b 3 G 2 t þ t, (1) where C t is the CO 2 emission (measured in metric tonnes per capita); E t the commercial energy use (measured in kg of oil equivalent per capita); G t the per capita real GDP (measured in local currency); and G t 2, the square of per capita real GDP (measured in local currency).

5 4774 ARTICLE IN PRESS J.B. Ang / Energy Policy 35 (2007) The parameters b 1, b 2 and b 3 are the long-run elasticities of CO 2 emissions with respect to energy use, per capita real GDP and squared per capita real GDP, respectively. The sign of b 1 is expected to be positive. Under the EKC hypothesis, b 2 is expected to be positive whereas a negative sign is expected for b 3. The statistical insignificance of b 3 suggests a monotonic increase in the relationship between pollutant emissions and per capita income. CO 2 emissions are those pollutants stemming from the burning of fossil fuels and the manufacture of cement. They include contributions to the CO 2 produced during consumption of solid, liquid, and gas fuels and gas flaring. Commercial energy use equals the indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport (World Development Indicator, 2005). Annual data covering the period are used in the study. All series are converted into natural logarithms for the usual statistical reasons. Thus, the series can be interpreted in growth terms after taking the first difference. It is evident from Fig. 1 that the levels of per capita real GDP and per capita energy consumption have significantly increased over time. Per capita CO 2 emissions increased initially but declined dramatically after the early 1980s. In panel (d) we observe a quadratic relationship between per capita real GDP and per capita CO 2 emissions. This is consistent with the prediction of the EKC hypothesis. Hence, the use of a quadratic specification is necessary to capture the long-run relationship of these variables ln (per capita CO 2 emissions) 3.2. Econometric methodology Our empirical estimation has two objectives. The first is to examine how the variables are related in the long run. The second is to examine the dynamic causal relationships between the variables. We begin our analysis by maintaining the assumption that the model in Eq. (1) can be approximated by a levels VAR model, which can be augmented with intercepts. A VAR approach serves our estimation purpose well since it avoids the endogeneity problems by treating all variables to be endogenous. Accordingly, the levels VAR is given by y t ¼ a 0 þ Xp A j y t j þ t, (2) where y t ¼½C t E t G t G 2 t Š0. The series C t, E t, G t and G 2 t can be either I(0) or I(1). a 0 is a vector of constant terms or a 0 ¼½a C a E a G a G 2Š 0, and A j is a matrix of VAR parameters for lag j. The vector of error terms t ¼½ C E G G 2Š 0 INð0; OÞ. The testing procedure involves three steps. We begin by performing an integration analysis using three unit root tests augmented Dickey Fuller (ADF) test, Phillips Perron (PP) test, and Elliott Rothenberg Stock (ERS) test. The choice of the ERS test to complement the widely employed ADF and PP tests is motivated by the argument that when a linear trend is present in the series, the use of ERS can substantially improve the power of the unit root test over the conventional tests (Elliott et al., 1996) ln(per capita real GDP) Fig. 1. Time series plots of the variables. (a) ln (per capita CO 2 emissions), (b) ln (per capita energy consumption), (c) ln (per capita real GDP), and (d) the relationship between ln (per capita CO 2 emissions) and ln (per capita real GDP).

6 ARTICLE IN PRESS J.B. Ang / Energy Policy 35 (2007) The second step is to test for cointegration using the Johansen s (1988) approach for each of the VARs constructed in levels. However, it is possible that given the small sample size used in this study (41 annual observations), the Johansen test statistics may be biased (Cheung and Lai, 1993). Hence, we follow the approach of Reinsel and Ahn (1992), who suggest multiplying the Johansen statistics with the scale factor (N pk)/n, where N is the number of observation, and p and k are the order of the VAR and the dimensions, respectively. This procedure corrects for small sample bias so that proper inference can be made. The Johansen s maximum likelihood test is complemented by the ARDL bounds test of Pesaran et al. (2001) to provide a sensitivity check on the results. This approach has been widely adopted in the energy literature (see, e.g., Fatai et al., 2003; Narayan and Smyth, 2005; Wolde- Rufael, 2006; Narayan and Singh, 2007). 1 The ARDL bounds test developed by Pesaran et al. (2001) can be estimated by OLS. Pesaran and Shin (1998) show that the OLS estimators of the short-run parameters are consistent and the ARDL based estimators of the long-run coefficients are super-consistent in small sample sizes. Hence, valid inferences on the long-run parameters can be made using standard normal asymptotic theory. The procedure involves estimating the following single-equation conditional error-correction model: DC t ¼ a 0 þ b 0 C t 1 þ Xk þ Xp i¼1 g 0i DC t i þ Xp b j DET j;t 1 i¼0 X k g ji DDET j;t i þ t, where C t is the per capita CO 2 emissions, and DET t is a vector of the determinants of C t, which includes E t, G t and G 2 t.anf-test for the joint significance of coefficients on lagged levels terms of the conditional ECM ðh 0 : b 0 ¼ b 1 ¼¼b k ¼ 0Þ can be employed to bounds test for the existence of a long-run relationship in Eq. (3). The test for cointegration is provided by two asymptotic critical value bounds when the independent variables are either I(0) or I(1). The lower bound assumes all the independent variables are I(0), and the upper bound assumes they are I(1). If the test statistics exceed their respective upper critical values, the null is rejected and we can conclude that a long-run relationship exists. Our causality tests are preceded by cointegration testing since the presence of cointegrated relationships have implications for the way in which causality testing is carried out. If cointegration is detected, the third step is to test for causality by employing the appropriate types of causality tests available in the recent literature. According to Engle and Granger (1987), cointegrated variables must ð3þ have an error correction representation in which an error correction term (ECT) must be incorporated into the model. Accordingly, a VECM is formulated to reintroduce the information lost in the differencing process, thereby allowing for long-run equilibrium as well as short-run dynamics. For the 4-variable case with one cointegrated relationship, the VECM can be expressed as follows: DC t ¼ a 1 þ a 11 ECT t 1 þ Xp 1 f 1j DC t j þ Xp 1 y 1j DE t j þ Xp 1 1 This technique has also been widely adopted in other fields. See, e.g., Narayan and Narayan (2005), Narayan and Smyth (2006a, b) and Ang (2007), among others. c 1j DG t j þ Xp 1 d 1j DG 2 t j þ 1t, DE t ¼ a 2 þ a 21 ECT t 1 þ Xp 1 f 2j DC t j þ Xp 1 y 2j DE t j þ Xp 1 c 2j DG t j þ Xp 1 d 2j DG 2 t j þ 2t, DG t ¼ a 3 þ a 31 ECT t 1 þ Xp 1 f 3j DC t j þ Xp 1 y 3j DE t j þ Xp 1 c 3j DG t j þ Xp 1 d 3j DG 2 t j þ 3t, DG 2 t ¼ a 4 þ a 41 ECT t 1 þ Xp 1 f 4j DC t j þ Xp 1 y 4j DE t j þ Xp 1 c 4j DG t j þ Xp 1 d 4j DG 2 t j þ 4t, ð4:1þ ð4:2þ ð4:3þ ð4:4þ where ECT t 1 ¼ C t 1 þðb 21 =b 11 ÞE t 1 þðb 31 =b 11 ÞG t 1 þ ðb 41 =b 11 ÞG 2 t 1 is the normalized cointegrated equation. There are two sources of causation, i.e., through the ECT, if aa0, or through the lagged dynamic terms. The ECT measures the long-run equilibrium relationship while the coefficients on lagged difference terms indicate the short-run dynamics. The statistical significance of the coefficients associated with ECT provides evidence of an error correction mechanism that drives the variables back to their long-run relationship. Given the two different sources of causality, we can perform two different causality tests, i.e., short-run Granger non-causality test and long-run weak exogeneity test. In Eq. (4.1), to test DG t and DG 2 t do not Granger cause DC t in the short run, we examine the significance of the lagged dynamic terms by testing the null H 0 : all c 1j ¼ all d 1j ¼ 0 using the Wald test. Rejection of the null implies output growth Granger causes pollution growth in the short run. To test the null that pollution growth does not Granger cause output growth, a different procedure is necessary since there are two variables which indicate output growth in the system, i.e., DG t and DG 2 t. Using Eqs. (4.3) and (4.4), we formulate an unrestricted VECM by imposing the restrictions all f 3j ¼ 0 and all f 4j ¼ 0.

7 4776 ARTICLE IN PRESS J.B. Ang / Energy Policy 35 (2007) Similar to the conventional F-test, a likelihood ratio test can be performed on the determinants of residual variance of these two models. Rejection of the null implies pollution growth Granger causes output growth in the short run. The weak exogeneity test, which is a notion of long-run noncausality test, requires satisfying the null H 0 : a 11 ¼ 0 for non-causality from long-run equilibrium deviation to DC t. For non-causality from long-run equilibrium deviation to DG t and DG 2 t, this requires non-rejection of the null H 0 : a 31 ¼ a 41 ¼ 0. The hypothesis testing is based on a likelihood ratio test which follows a w 2 distribution. 4. Empirical findings Three unit root tests are implemented: ADF, PP, and ERS tests. The results reported in Table 1 shows that all unit root tests yield remarkably similar results, i.e., C t, E t, G t and G 2 t are non-stationary in their levels but become stationary after taking the first difference. Hence, we conclude that all series are I(1) at the 5% level of significance. Given that C t, E t, G t and G 2 t share common integration properties, we can now proceed to test for the presence of a common trend, or equivalently, a long-run cointegrating relationship between the variables. Given the sample size, we have considered a maximum lag length of five and tested the model downwards. The optimal lag length is found to be five based on the AIC model selection criterion and likelihood ratio test. This lag structure is used for the remaining analyses. The Johansen cointegration tests are performed for the VARs at levels. In panel A of Table 2, the results of the Johansen maximum eigenvalue tests, based on both Johansen (1988) and the modified version suggested by Reinsel and Ahn (1992), point to the conclusion that there is one cointegrated relationship, at the 1% level of significance. As a sensitivity check, cointegration tests are also performed using the ARDL bounds test of Pesaran et al. (2001). The results reported in panel B indicate that the null hypothesis of cointegration is rejected at the 5% significance level. Given the small sample used in the present study, the F-statistic is also Table 1 Unit root tests ADF PP ERS Level 1st difference Level 1st difference Level 1st difference compared against the critical values provided by Narayan (2005), which are based on small samples. We obtain the same conclusion that no evidence against cointegration is found. Table 3 presents the cointegrating equation and speed of adjustment coefficient of this cointegrating vector. Lagrange Multiplier (LM) tests suggest no evidence of serial correlation in the residuals up to the second order. By normalizing the coefficient of C t to one, the cointegrated equation shows that all coefficients are statistically significant at the 1% level. All coefficients in the long-run relationship have the expected signs. That is, C t are positively related to E t and G t but negatively related to G 2 t. The results provide some support for our argument to examine these variables under an integrated framework. The long-run elasticity of C t with respect to E t is found to be 2.25, suggesting that a 1% point increase in E t is associated with 2.25% points increase in C t. The results that E t has a positive influence on C t are consistent with our predictions. The elasticity of pollution with respect to income in the long run is found to be G t.the C t *** *** *** E t *** *** *** G t ** ** ** G 2 t ** ** ** Notes: For ADF, AIC is used to select the lag length. The maximum number of lags is set to be five. For PP, Barlett Kernel is used as the spectral estimation method. The bandwidth is selected using the Newey West method. For ERS, AR spectral OLS is used as the spectral estimation method. The optimal lag length is chosen using AIC. *, ** and *** indicate 10%, 5% and 1% level of significance, respectively. statistical significance of G 2 t rules out the case where output increases monotonically with the level of pollution. The results provide some support for the EKC hypothesis that the level of environmental pollution first increases with income, and then stabilizes and declines. The results imply that the turning point of the EKC occurs at an income level of 9.31 (in logarithms), which is rather close to the actual level of 9.55 (in logarithms). Our findings are broadly consistent with Shafik and Bandyopadhyay (1992), Selden and Song (1994) and Grossman and Krueger (1995), who have also reported an inverted U-shaped relationship between pollution and output. The loading factor, which measures the speed of adjustment back to the long-run equilibrium value, is statistically significant and correctly signed (negative). This implies that the long-run equilibrium deviation has a significant impact on the growth of CO 2 emissions. The speed of adjustment at 77% a year is considered relatively high. In other words, it takes less than 1.5 years to achieve long-run equilibrium whenever there is a deviation from the long-run steady state. The results are not surprising given that there is little control on the growth of CO 2 emissions. Cointegration implies the existence of causality, at least in one direction. However, it does not indicate the direction of the causal relationship. Hence, to shed light on the direction of causality, we perform the ECM-based causality tests. The results reported in Table 4 show a unidirectional causality running from output growth to growth of pollutant emissions in the long run. When examining the causal relationship between economic growth and growth of energy use, we find evidence of causality running from the former to the latter in the long run, but a reverse causality is observed in the short run. The results also suggest that degradation of the environment does not have a causal impact on economic

8 ARTICLE IN PRESS J.B. Ang / Energy Policy 35 (2007) Table 2 Cointegration test results Hypothesized no. of cointegrating equations Eigenvalue Maximum eigenvalue statistics Modified Maximum eigenvalue statistics A. Johansen cointegration tests r ¼ *** *** r ¼ r ¼ r ¼ Critical bounds of the F-statistic: unrestricted intercept and no trend 1% level 5% level 10% level growth. Instead, expansion of the economy exerts a causal effect on CO 2 emissions. The results call for more environmental protection since environmental pollution may cause a negative externality to the economy through affecting human health and thereby reduce productivity. However, one must be mindful that policies that impose a I(0) I(1) I(0) I(1) I(0) I(1) B. ARDL Bounds test Pesaran et al. (2001) Narayan (2005), N ¼ Narayan (2005), N ¼ Calculated F-statistic ** Notes: The modified maximum eigenvalue statistics were obtained by multiplying the Johansen maximum eigenvalue statistic with the scale factor (N pk)/ N, where N is the number of observation, and p and k are the order of the VAR and the dimensions, respectively. For the ARDL bounds test, the optimal lag length was found to be four using AIC. The critical values provided by Narayan (2005) are based on small samples. They are calculated using stochastic simulations specific to the sample size based on 40,000 replications. The sample size N of the present study is 41. *, ** and *** indicate 10%, 5% and 1% level of significance, respectively. Table 3 Cointegrating vector Cointegrated equation a 11 C t ¼ 161:38 þ 2:25E t þ 31:11G t 1:67G 2 t ð4:30 Þð3:92 Þð 4:32 Þ w 2 SERIAL ð1þ ¼17:29ð0:37Þ; w2 SERIALð2Þ ¼12:03ð0:74Þ ( 5.68**) 0.77 Notes: The normalized variable is C t, figures in parentheses indicate t-statistics; w 2 SERIAL ð1þ and w2 SERIALð2Þ are the Lagrange multiplier test statistics for no first and second serial correlation, respectively; and *, ** and *** indicate 10%, 5% and 1% level of significance, respectively. Table 4 Causality tests Hypothesis Short-run Granger noncausality test Long-run weak exogeneity test H 0 :DG,DG 2 QDC *** H 0 :DCQDG,DG H 0 :DG,DG 2 QDE *** H 0 :DEQDG,DG *** 0.07 Notes: *, ** and *** indicate 10%, 5% and 1% level of significance, respectively. higher cost such as environmental taxes or greater command-and-control efforts may result in input material substitution, which may reduce or increase the pollution levels. The finding of a uni-directional causality running from output growth to growth of energy use in the long run implies that France is an energy-independent economy, in line with its economic policy to achieve energy independence in the long run. The results seem to suggest that the implementation of energy conservation policies has not inversely affected the long-term economic performance of France. Hence, the results imply that the economy of France may be less vulnerable to energy shocks, which could adversely affect GDP growth. In the short run, more energy use is required to fuel economic development. Economic growth is the outcome of growth in inputs and increases in the productivity of the inputs. Therefore, economic expansion requires higher or more efficient consumption of energy products. However, there is much scope for the development of energy conservation strategies given that over-consumption of resources can have negative impacts on the environment. 5. Summary and conclusions In this paper, we examine the dynamic relationship between CO 2 emissions, energy consumption, and output for France over the period using a multivariate vector error-correction model. The empirical results for the case of France suggest the existence of a robust long-run relationship between the variables. The results indicate that more energy use results in more CO 2 emissions, and CO 2 emissions and output have a quadratic relationship in the

9 4778 ARTICLE IN PRESS J.B. Ang / Energy Policy 35 (2007) long run. To complement the findings of cointegration analysis, we also perform two causality tests to throw light on the causal links of output energy and output pollution. The results suggest that output growth causes CO 2 emissions and energy consumption in the long run. A uni-directional causality running from growth of energy use to output growth is observed in the short run. Acknowledgements Useful suggestions and comments from Warwick McKibbin, Sjak Smulders, Russell Smyth and a referee of this journal are greatly appreciated. References Abosedra, S., Baghestani, H., New evidence on the causal relationship between United States energy consumption and Gross National Product. Journal of Energy and Development 14, Akarca, A.T., Long, T.V., On the relationship between energy and GNP: a reexamination. Journal of Energy and Development 5, Ang, J.B., Are saving and investment cointegrated? The case of Malaysia ( ). Applied Economics, forthcoming. Antweiler, W., Brian, C.R., Scott, T., Is free trade good for the environment? American Economic Review 91, Asafu-Adjaye, J., The relationship between energy consumption, energy prices and economic growth: time series evidence from Asian developing countries. Energy Economics 22, Cheng, B.S., Lai, T.W., An investigation of co-integration and causality between energy consumption and economic activity in Taiwan. Energy Economics 19, Cheung, Y.-W., Lai, K.S., Finite-sample sizes of Johansen s likelihood ratio tests for cointegration. Oxford Bulletin of Economics and Statistics 55, Coxhead, I., Development and the environment in Asia. Asian Pacific Economic Literature 17, Cropper, M., Griffiths, C., The interaction of population growth and environmental quality. American Economic Review 84, Elliott, G., Thomas, R., James, S., Efficient tests for an autoregressive unit root. Econometrica 64, Engle, R.F., Granger, C.W.J., Co-integration and error correction: representation, estimation, and testing. Econometrica 55, Erol, U., Yu, E.S.H., On the causal relationship between energy and income for industrialized countries. Journal of Energy and Development 13, Fatai, K., Oxley, L., Scrimgeour, F.G., Modeling and forecasting the demand for electricity in New Zealand: a comparison of alternative approaches. Energy Journal 24, Glasure, Y.U., Lee, A.-R., Cointegration, error-correction, and the relationship between GDP and energy: the case of South Korea and Singapore. Resource and Energy Economics 20, Grossman, G.M., Krueger, A.B., Economic growth and the environment. Quarterly Journal of Economics 110, Hettige, H., Lucas, R.E.B., Wheeler, D., The toxic intensity of industrial production: global patterns, trends, and trade policy. American Economic Review 82, Holtz-Eakin, D., Selden, T.M., Stoking the fires? CO 2 emissions and economic growth. Journal of Public Economics 57, Hwang, D.B.K., Gum, B., The causal relationship between energy and GNP: the case of Taiwan. Journal of Energy and Development 16, Johansen, S., Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12, Jumbe, C.B.L., Cointegration and causality between electricity consumption and GDP: empirical evidence from Malawi. Energy Economics 26, Kraft, J., Kraft, A., On the relationship between energy and GNP. Journal of Energy and Development 3, Martinez-Zarzoso, I., Bengochea-Morancho, A., Pooled mean group estimation of an environmental Kuznets curve for CO 2. Economics Letters 82, Masih, A.M.M., Masih, R., Energy consumption, real income and temporal causality: results from a multi-country study based on cointegration and error-correction modelling techniques. Energy Economics 18, Narayan, P., The saving and investment nexus for China: evidence from cointegration tests. Applied Economics 37, Narayan, P., Narayan, S., Estimating income and price elasticities of imports for Fiji in a cointegration framework. Economic Modelling 22, Narayan, P., Singh, B., The electricity consumption and GDP nexus for the Fiji islands. Energy Economics, forthcoming. Narayan, P., Smyth, R., The residential demand for electricity in Australia: an application of the bounds testing approach to cointegration. Energy Policy 33, Narayan, P., Smyth, R., 2006a. Higher education, real income and real investment in China: evidence from Granger causality tests. Education Economics 14, Narayan, P., Smyth, R., 2006b. What determines migration flows from low-income to high-income countries? An empirical investigation of Fiji US migration Contemporary Economic Policy 24, Paul, S., Bhattacharya, R.N., Causality between energy consumption and economic growth in India: a note on conflicting results. Energy Economics 26, Pesaran, M.H., Shin, Y., An autoregressive distributed-lag modelling approach to cointegration analysis. In: Strom, S. (Ed.), Econometrics and Economic Theory in the Twentieth Century: The Ragnar Frisch Centennial Symposium. Cambridge University Press, Cambridge, MA, pp Pesaran, M.H., Shin, Y., Smith, R.J., Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16, Reinsel, G.C., Ahn, S.K., Vector autoregressive models with unit roots and reduced rank structure: estimation, likelihood ratio test, and forecasting. Journal of Time Series Analysis 13, Selden, T.M., Song, D., Environmental quality and development: is there a Kuznets curve for air pollution emissions? Journal of Environmental Economics and Management 27, Shafik, N., Economic development and environmental quality: an econometric analysis. Oxford Economic Papers 46, Shafik, N., Bandyopadhyay, S., Economic growth and environmental quality: time series and cross-country evidence. World Bank Policy Research Working Paper No Stern, D.I., A multivariate cointegration analysis of the role of energy in the US macroeconomy. Energy Economics 22, Wolde-Rufael, Y., Electricity consumption and economic growth: a time series experience for 17. African Countries Energy Policy 34, Yang, H.-Y., A note on the causal relationship between energy and GDP in Taiwan. Energy Economics 22, Yu, E.S.H., Choi, J.-Y., The causal relationship between energy and GNP: an international comparison. Journal of Energy and Development 10,