Convergence in per capita energy use among OECD countries

Energy Economics 36 (2013) 536–545

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Convergence in per capita energy use among OECD countries Ming Meng a, James E. Payne b,⁎, Junsoo Lee a a b

Department of Economics, Finance and Legal Studies, University of Alabama, Box 870224, Tuscaloosa, AL 35487, United States Department of Economics and Finance, University of New Orleans, New Orleans, LA 70148, United States

a r t i c l e

i n f o

Article history: Received 31 May 2012 Received in revised form 18 October 2012 Accepted 2 November 2012 Available online 9 November 2012 JEL classification: C22

a b s t r a c t Unlike previous studies which mainly focus on the integration properties of energy consumption and production, this study examines the convergence of per capita energy use among 25 OECD countries over the period 1960 to 2010. In particular, newly developed LM and RALS-LM unit root tests with allowance for two endogenously determined structural breaks are employed. The results indicate significant support for per capita energy use convergence among OECD countries. © 2012 Elsevier B.V. All rights reserved.

Keywords: Per capita energy use Convergence Unit roots Trend-shifts

1. Introduction Energy usage is a vital input in the production of goods and services and the functioning of economies worldwide. However, as economies become more industrialized and the energy sector develops over time, energy use patterns change. Technological advancements in the energy sector and further modernization of the energy infrastructure have resulted in improvements in energy efficiency as shown by declining energy intensities (energy–GDP ratio) for many industrialized economies. As noted by Jakob et al. (2012, p. 95), “For industrialized countries, we find that economic growth is partially decoupled from energy consumption and that above average rates of economic growth were accompanied by larger improvements in energy efficiency”. Jakob et al. (2012, p. 101) further note that “for wealthy countries, which are close to their steady state, growth is largely driven by gains in total factor productivity and increases in economic activity can be counterbalanced by energy efficiency. This can result in slowly increasing, constant, or even decreasing total energy use depending on the growth rates of total factor productivity and energy efficiency, respectively.” It is the time series behavior of per capita energy use which we focus upon in this study, in particular, the convergence of per capita energy use among OECD countries. To examine the convergence of per capita energy use among OECD countries, we examine stochastic conditional convergence by employing newly developed LM unit root tests with trend-breaks suggested by Meng and Lee (2012) and Lee et ⁎ Corresponding author. E-mail addresses: [email protected] (M. Meng), [email protected] (J. Lee). 0140-9883/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.eneco.2012.11.002

al. (2012). The importance of allowing for structural breaks has been well documented in the literature of unit root tests. While the convergence hypothesis within the energy economics literature has largely focused on carbon dioxide emissions and energy intensity with more recent studies adopting unit root tests that allow for structural changes, our testing procedure differs from previous studies in several important aspects. 1 First, most importantly, we pay attention to the apparent trendbreaks that can be observed in the data of per capita energy use, and adopt relevant tests that resolve the issue properly. Although multiple trend-breaks may exist in the data, they have been ignored or downplayed in many of the existing studies. One crucial reason for such mis-treatments is the dependency of the unit root test statistic on the nuisance parameter indicating the location of breaks. Several popular unit root tests attempt to eliminate the dependency on the nuisance parameter by assuming that breaks are absent under the null hypothesis. However, Nunes et al. (1997) and Lee and Strazicich (2001, 2003) point out that assuming away the nuisance parameter leads to a serious size distortion, and spurious rejections will occur under the null hypothesis in such tests. A recent survey study by Perron (2006) also recognizes this critical drawback and provides a warning in this

1 List (1999), Strazicich and List (2003), Lee and Chang (2008), Aldy (2007), and Barassi et al. (2008), among others, investigate convergence in carbon dioxide emissions. Studies by Miketa and Mulder (2005), Markandya et al. (2006), Mulder and de Groot (2007, 2012), and Liddle (2009, 2010), among others, examine convergence in energy intensity.

M. Meng et al. / Energy Economics 36 (2013) 536–545

regard. Our transformed LM tests do not depend on the nuisance parameter and allow for trend-breaks under the null hypothesis. Second, we adopt the new LM tests based on the RALS (residual augmented least squares) regression. These new tests utilize the information of non-normal errors that have been ignored in the literature of unit root tests. Our approach makes a sharp contrast with nonlinear unit root tests which attempt to capture neglected nonlinearity. Given that conventional linear unit root tests will lose power in the presence of nonlinearity, various nonlinear unit root tests have been suggested to address this issue. However, a recent study by Lee et al. (2011) notes that these nonlinear unit root tests assume a particular nonlinear function with normal errors, and that they suffer from loss of power in the presence of non-normal errors, contrary to linear unit root tests. Our new RALS-LM tests are free of this problem and actually gain power in the presence of non-normal errors when we adopt the linear based testing procedure. To the best of our knowledge, this is the first empirical study that examines the behavior of energy use by utilizing the information of non-normal errors in the framework of trend-shift models. The rest of the study proceeds as follows. Section 2 provides an overview of the literature. Section 3 discusses the methodology. Section 4 presents the data and empirical results with concluding remarks given in Section 5.

2. Overview of the literature Though the energy economics literature has not examined the convergence of energy consumption within the context of stochastic conditional convergence using unit root tests with trend-shifts, there have been a number of studies examining the integration properties of energy consumption and production in the last 5 years (Smyth, 2012). 2,3,4 Narayan and Smyth (2007) examine the stationarity of per capita energy consumption using the augmented Dickey–Fuller (Dickey and Fuller, 1979) unit root test for 182 countries to find only 31% of the countries reject the null hypothesis of a unit root, while the results using Im et al. (2003) panel unit root tests reveal stationarity. Chen and Lee (2007) allow for multiple structural breaks using the Carrion-i-Silvestre et al. (2005) panel stationarity test in the case of per capita energy consumption for 104 countries broken down into seven regional country panels to show stationarity in each of the regional country panels. Hsu et al. (2008) employ Breuer et al. (2001) panel SURADF unit root test of energy consumption across five regional country panels to find non-stationary behavior. Mishra et al. (2009) utilize Carrion-i-Silvestre et al. (2005) panel stationarity tests with endogenously determined structural breaks to examine the stationarity of per capita energy consumption for 13 Pacific Island countries. Their results support stationarity of per capita energy consumption for roughly 60% of

2 Examination of the integration properties of energy consumption and production focuses on whether shocks have a permanent or transitory impact for several reasons. First, the distinction between the permanent and transitory nature of shocks is relevant to determining whether energy-related policies will have a permanent impact. Second, to the extent that energy is integrated with the overall economy, shocks to energy consumption and production that are permanent may very well be transmitted to other sectors of the economy. Third, determination of the presence of a unit root in energy consumption and production has implications for the modeling and forecasting of future energy consumption and production. 3 Maza and Villaverde (2008) utilize nonparametric techniques applied to residential per capita electricity consumption for a sample of 98 countries to find a weak process of electricity consumption convergence. Mohammadi and Ram (2012) apply quantile regression analysis to 108 countries to find that global convergence in energy usage is weak whereas the evidence of convergence is much stronger with respect to electricity usage. 4 Other studies in the energy literature have investigated the integration properties associated with energy prices and energy efficiency, see Maslyuk and Smyth (2008), Lee and Lee (2009), Rao and Rao (2009), and Barros and Gil-Alana (forthcoming), among others.

537

the countries while per capita energy consumption for the entire panel of countries is stationary. Narayan et al. (2010) use the Lee and Strazicich (2003) unit root tests with endogenously determined structural breaks for sectoral energy consumption in Australia and its six states. Their results show rejection of the null hypothesis of a unit root for most sectors except in the electricity sector of Tasmania and the transportation sector of South Australia. With the exception of aggregate energy consumption in South Australia, the rest of the states and Australia aggregate energy consumption is stationary. Aslan and Kum (2011) employ linear and non-linear unit root tests for energy consumption in Turkey across seven sectors. For the sectors in which linearity is not rejected, the Lee and Strazicich (2003) unit root tests with endogenously determined structural breaks yield stationarity. For the remaining sectors, Kruse (2011) non-linear unit root tests yield non-stationarity. Hasanov and Telatar (2011) examine the stationarity of per capita primary energy consumption for 178 countries using linear and nonlinear unit root tests. The linear augmented Dickey–Fuller unit root test (Dickey and Fuller, 1979) renders per capita primary energy consumption stationary in 55 countries; the nonlinear unit root test by Kapetanios et al. (2003) based on an exponential smooth transition model reveals per capita primary energy consumption is stationary in 71 countries; and the nonlinear unit root test of Sollis (2004) which combines both smooth transition and asymmetric threshold models yields per capita primary energy consumption is stationary in 121 countries. Ozturk and Aslan (2011) investigate per capita energy consumption by sector in Turkey using the Lee and Strazicich (2003) unit root test with endogenously determined structural breaks to show per capita energy consumption is stationary. Kula et al. (2012) examine the presence of a unit root in per capita electricity consumption with allowance for endogenously determined structural breaks using the Lee and Strazicich (2003) unit root test for 23 OECD countries. Their results indicate that per capita electricity consumption is stationary for 21 of the 23 OECD countries. Other studies have examined the integration properties of specific types of energy consumption. Lean and Smyth (2009) explore the long memory behavior of U.S. disaggregated petroleum consumption using Nielsen (2005) fractional integration approach to find that petroleum consumption in the commercial and industrial sectors are fractionally integrated whereas petroleum consumption in the residential sector is stationary. Apergis et al. (2010a) investigate whether natural gas consumption for the 50 U.S. states contains a unit root. Levin et al. (2002), Im et al. (2003), Maddala and Wu (1999), and Hadri (2000) panel unit root and stationarity tests show that natural gas consumption contains a unit root. However, allowing for endogenously determined structural breaks, the Carrion-i-Silvestre et al. (2005), Im et al. (2005), and Westerlund (2005) panel unit root and stationarity tests reveal that natural gas consumption is stationary. Apergis et al. (2010b) utilize Im et al. (2005), Westerlund (2005), and Carrion-i-Silvestre et al. (2005) panel unit root and stationarity tests with endogenously determined structural breaks in the case of coal consumption for the 50 U.S. states to find that coal consumption is stationary. Apergis and Payne (2010) use the Lee and Strazicich (2003) and Narayan and Popp (2010) unit root tests with endogenously determined structural breaks to show that the null hypothesis of a unit root in petroleum consumption is rejected for a majority of U.S. states. Aslan (2011) estimate linear (Lee and Strazicich, 2003) and nonlinear (Kruse, 2011) unit root tests for natural gas consumption for the 50 U.S. states to find that roughly 60% of the states exhibit nonlinear behavior with 27 states showing non-stationarity while 23 states are stationary. Barros et al. (2012) employ fractional integration techniques to examine the persistence and long memory behavior of U.S. renewable energy consumption to show the degree of integration is above 0.5, but significantly less than 1.0 indicative of nonstationarity with mean reverting behavior. Gil-Alana et al. (2010) use fractional integration techniques to examine the level of persistence of shocks to each energy source consumed by the U.S. electric power

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section to find a high degree of persistence and long memory behavior. Apergis and Tsoumas (2011) explore the long memory behavior of U.S. solar, geothermal, and biomass energy consumption across sectors to find that the degree of fractional integration varies across energy sources and sectors; however, the order of integration is less than one, suggesting shocks are transitory in nature. Apergis and Tsoumas (2012) examine the long memory properties of fossil fuel, coal, and electricity consumption in the U.S. by sector using the Gil-Alana (2008) fractional integration approach with structural breaks to reveal heterogeneity in the order of integration across energy sources and sectors; however, the order of integration is below one, suggesting that shocks are transitory. With respect to energy production, Narayan et al. (2008) explore the integration properties of crude oil and NGL production for 60 countries using panel unit root and stationarity tests by Maddala and Wu (1999), Breitung (2000), Hadri (2000), Levin et al. (2002), and Im et al. (2003) to find mixed evidence of stationarity. However, accounting for structural breaks using the panel unit root test by Im et al. (2005), the results reject the null hypothesis of a unit root in crude oil and NGL production. Maslyuk and Smyth (2009) utilize the threshold unit root tests of Caner and Hansen (2001) to investigate the unit root properties of crude oil production for 17 OPEC and non-OPEC countries. The results suggest the presence of threshold effects in crude oil production over two regimes and in the case of 11 countries the presence of a unit root in both regimes while in the remaining countries presence of a partial unit root in either the first or second regime. Barros et al. (2011) examine oil production for OPEC countries using a fractional integration framework with allowance for structural breaks to show mean reverting persistence in oil production with breaks for 10 out of the 13 countries examined. In light of the studies mentioned, this study differs in explicitly testing for stochastic conditional convergence in per capita energy use via unit root testing. Earlier works on the convergence hypothesis adopted cross-sectional studies and test the notion of β convergence; see Baumol (1986), Barro and Sala-i-Martin (1991, 1992) for example. However, several important studies reported pitfalls of testing for β convergence and advocate time series approaches for stochastic conditional convergence based on unit root tests; see Evans (1996), Evans and Karras (1996), and Quah (1996), among others. Indeed, given the different endowments of each country, the Solow (1956) model implies conditional convergence rather than absolute convergence. The methodology section to follow presents the new LM and RALS-LM tests with allowance for endogenously determined structural breaks to test for stochastic conditional convergence in per capita energy use. 3. Methodology One may consider the following data generating process (DGP) based on the unobserved component representation: ′

yt ¼ δ Z t þ et ; et ¼ βet−1 þ t ;

ð1Þ

where Zt contains exogenous variables. To consider multiple breaks, we let TBi stand for the time period of each break and consider multiple dummy variables such that:
  ′ Z t ¼ 1; t; D1t ; …; DRt ; DT 1t ; …DT Rt ;

ð2Þ

where Dit* = 1 for t ≥ TBi + 1, i = 1,…R, and zero otherwise, and DTit* = t − TBi for t ≥ TBi + 1 and zero otherwise. Following the LM (score) principle, we impose the null restriction β = 1 and consider in the first step the following regression in differences: ′

Δyt ¼ δ ΔZ t þ ut ;

ð3Þ

h i′ where δ ¼ δ1 ; δ2 ; δ′ 3i ; δ′ 4i , i = 1,…R. Then, we denote the estimated ˜ The unit root test statistics are then obtained from coefficient as δ. the following regression: ′

Δyt ¼ δ ΔZ t þ ϕS˜ t−1 þ et ;

ð4Þ

where S˜ t denotes the de-trended series ˜ ˜ S˜ t ¼ yt −Ψ−Z t δ:

ð5Þ

˜ ¼ y1 −Z 1δ. ˜ It is important to note that in the de-trending Here, Ψ procedure (5), the de-trending parameters are estimated in the first step regression (3) in differences. Through this channel the dependency on nuisance parameters is removed in the crash model. However, the dependency on nuisance parameters is not removed with this de-trending procedure in the model with trend breaks. In particular, Lee et al. (2012) show that the LM unit root test statistic tϕ for the model with trend-shifts will depend on λ*,i which denotes the ∗ fraction of sub-samples in each regime such that λ*1= TB1/T, λi = ∗ (TBi − TBi − 1)/T, i = 2,…,R, and λ R + 1 = (T − TBR)/T. However, Lee et al. (2012) show that the dependency on the nuisance parameter can be removed with the following transformation: 8 T ˜ > > S > > > T B1 t > > > T > < S˜ t  S˜ t ¼ T B2 −T B1 > > > > ⋮ > > T ˜ > > > S : T−T BR t

for t ≤ T B1 for T B1 b t ≤ T B2 for

:

ð6Þ

T BR b t ≤ T

 We then replace S˜ t−1 in the testing regression (4) with S˜ t−1 as follows:

′



Δyt ¼ δ ΔZ t þ ϕS˜ t−1 þ

k X

dj ΔS˜ t−j þ et :

ð7Þ

j¼1

We denote τ ˜ LM as the t-statistic for ϕ = 0 from Eq. (7). 5 Following the transformation, the asymptotic distribution of τ ˜ LM depends only on the number of trend breaks, since the distribution is given as the sum of R + 1 independent stochastic terms. With one trend-break (R = 1), the distribution of τ ˜ LM is the same as that of the untransformed test using λ = 1/2, regardless of the initial location of the break(s). Similarly, with two trend-breaks (R=2), the distribution of τ ˜ LM is the same as that of the untransformed test using λ1 =1/3 and λ2 =2/3. In general, for the case of R multiple breaks, the same analogy holds: the distribution of τ ˜ LM is the same as that of the untransformed test using λi =i/(R+1), i=1, …, R. Therefore, we do not need to simulate new critical values at all possible break point combinations. The critical values of τ ˜ LM are reported in Lee et al. (2012). The LM tests are generally more powerful than the usual DF type tests. However, to further improve the power of the LM statistic τ ˜ LM , we adopt the procedure to utilize the information on non-normal errors. We adopt the “residual augmented least squares” (RALS) method suggested by Meng and Lee (2012) and Im et al. (2010). The RALS pro^ t , to the testing regression (7). cedure augments the following term, w ^ ^ t ¼ hðe^t Þ−K^ −e^t D ð8Þ w 2 h i′ T ^ ¼ 1 ∑T h′ ðe^ Þ. To where hðe^t Þ ¼ e^2t ; e^3t , K^ ¼ T1 ∑t¼1 hðe^t Þ, and D t¼1 2 t T 5 Then, the asymptotic distributions of the test statistic τ˜ LM will be invariant to the nuisance parameter λ∗i.



τ ˜ →−

⌈

Rþ1 1 X 1 2 ∫ V ðr Þ dr 2 i¼1 0 i

⌉

−1=2

where V i ðr Þ is defined in Proposition 1 of Lee et al. (2012).

M. Meng et al. / Energy Economics 36 (2013) 536–545

h i′ capture the information of non-normal errors, we let hðe^t Þ ¼ e^2t ; e^3t , which involves the second and third moments of e^t . Then, letting j ^ j ¼ T −1 ∑Tt¼1 e^t , the augmented term can be given as m h i′ ^ 2 ; e^3t −m ^ 3 −3m ^ 2 e^t : ^ t ¼ e^2t −m w

ð9Þ

These terms are obtained from the redundancy condition in that knowledge of higher moments mj + 1 are uninformative if mj + 1 = jσ 2mj − 1. The normal distribution is the only distribution that satisfies the redundancy condition. However, if the distribution of the error term is not normal, the condition is not satisfied. In such cases, one may increase efficiency by augmenting the testing regression (7) ^ t . Then the transformed RALS-LM test statistic is obtained with w from the regression ′



Δyt ¼ δ ΔZ t þ ϕS˜ t−1 þ

p X

′

^ t γ þ ut : dj ΔS˜ t−j þ w

ð10Þ

j¼1

The RALS-LM estimator is obtained through the usual least square estimation applied to regression (10). We denote the corresponding t-statistic for ϕ = 0 as τ*RALS − LM. 6 Note that the asymptotic distribution of τ*RALS − LM no longer depends on the break location parameter λj, hence there is no need to simulate new critical values for all the possible break location combinations. The critical values of the RALS-LM statistics are provided in Meng and Lee (2012), which shows improved power of the LM tests when the errors are not normal, but exhibit asymmetry or the patterns of fat-tailed distributions. 4. Data and empirical results We utilize the World Development Indicators (WDI) data provided on annual per capita energy use over the period 1960–2010 to examine the convergence property in 25 OECD countries.7 The countries examined are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Japan, Luxembourg, Netherlands, New Zealand, Norway, Poland, Portugal, Spain, Sweden, Switzerland, Turkey, United Kingdom (UK) and the United States (US). For each country i, we examine the natural logarithm of the ratio of per capita energy use (PCEU) relative to the average of all OECD countries in the sample as follows: yit ¼ lnðPCEU it =average PCEU t Þ:

ð11Þ

We utilize the two-step LM and three-step RALS-LM unit root tests developed by Lee et al. (2012) and Meng and Lee (2012), respectively, to examine the log of relative per capita energy use in (11). Throughout, we consider a model with at most two level and trend breaks. 8 The two-step LM test can be summarized as follows: in the first step, we set a maximum structural break number R (in this study R = 2) and apply the maxF test to identify the break locations, test the significance of each break, and simultaneously determine the optimal lags with the corresponding number and location of breaks. If the null hypothesis of 6

Then it can be shown that the asymptotic distribution of τ*RALS−LM is given as follows: qffiffiffiffiffiffiffiffiffiffiffiffiffi   τ RALS−LM →ρ˜τ LM þ 1−ρ2 Z where ρ reflects the relative ratio of the variances of two error terms. It is interesting to see that the limiting distribution is similar to the distribution of the unit root tests with stationary covariates of Hansen (1995). 7 Energy use refers to use of primary energy (kg of oil equivalent per capita) before transformation to other end-use fuels, which is equal to indigenous production plus imports and stock changes, minus exports and fuels supplied to ships and aircraft engaged in international transport. 8 Eq. (11) implies that shocks of the same percentage common to all countries would leave relative energy use unchanged. Therefore, the structural breaks identified by our tests would be country-specific. Throughout, we require that each break be significant at the 10% level.

539

no trend break is not rejected or if the null hypothesis of no trend break is rejected but one of the break dummy variables is not significant based on the standard t-test, we move to the beginning of the first step with the structural break number equal to R− 1. This procedure continues until the break number becomes zero or all the identified break dummy variables are significant. If the first step yields a break number equal to zero, we use the usual no-break LM unit root test of Schmidt and Phillips (1992); if one or more breaks are found, we use one break (or R breaks) LM unit root test of Amsler and Lee (1995) and Lee and Strazicich (2003) with the break number and location as well as the corresponding optimal lags determined in the first step. Then, we obtain the LM test statistic, denoted as τ*LM.9 The first two steps of the three-step RALS-LM test are the same as the two-step LM test. We use the higher moment information obtained from the second step and augment it to the regression of the two-step LM test as the third step of the RALS-LM test denoted as τ*RALS−LM. When looking for the optimal number of breaks, we use the grid search within 0.10–0.90 intervals of the whole sample period so that each subsample before and after breaks will have enough observations to perform a valid test. The corresponding number of lags is chosen using a general to specific method with the maximum lags equal to eight. The results of employing the two-break LM and two-break RALS-LM unit root tests for the sample period 1960–2010 are shown in Table 1. The null hypothesis of a unit root in relative per capita energy use is rejected at the 10% significance level in nineteen countries using the LM test and in seventeen countries with the RALS-LM test. Further examination reveals that two structural breaks in the trend (Djt) are significant (t-value significant at 10%) in thirteen countries, while one structural break is significant in the twelve remaining countries.10 For Australia, Belgium, Finland, France, Germany, Greece, Ireland, Italy, Japan, Norway, Switzerland and United Kingdom, one of the breaks identified for each country is not significant at the 10% level, and thus a one-break unit root test appears more appropriate. To examine the effect of including two breaks instead of one, we perform additional one-break tests for all countries and the results are shown in Table 2. Compared to nineteen and seventeen rejections for the unit root tests with two breaks, eight and twelve countries reject the null hypothesis of a unit root for LM and RALS-LM tests, respectively. To examine further the effect of structural breaks when testing for a unit root in relative per capita energy use the ADF, LM and RALS-LM tests with no-break are performed. The results for the no-break tests are presented in Table 3. Seven countries reject the unit root null hypothesis with the ADF test, and the rejection number for the no-break LM and RALS-LM test are three and eight, respectively. This provides evidence to support the notion that failure to allow for structural breaks reduces the ability to reject a false unit root null hypothesis. Among the twelve countries with one-break not significant with the two-break test, five countries reject the unit root null hypothesis with the one-break LM test and six countries reject the unit root null hypothesis with the one-break RALS-LM test. It is interesting to note that per capita energy use in Italy cannot reject the unit root null hypothesis with the two-break LM test, but reject the unit root null hypothesis with one-break. Per capita energy use in Greece and Ireland yield a similar result with the RALS-LM test. As such, the increase in the number of structural breaks may not lead to more rejections of a unit root. Overall, our empirical findings provide significant support for per capita energy use convergence among OECD countries. Since all breaks identified with one-break tests are significant at the 10% level, we can conclude that all twenty five OECD countries examined in this study contain one/two break(s). With the appropriate number of structural

9 Exogenous critical values are used here because the maxF test can identify the break 100% accurate when the break is relatively large. Refer to Lee et al. (2012) for a detailed discussion of this issue. 10 Here we only consider the significance of the trend breaks.

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Table 1 Results using two-break LM and RALS-LM unit root tests. Country

Australia Austria Belgium Canada Denmark Finland France Germany Greece Iceland Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Poland Portugal Spain Sweden Switzerland Turkey United Kingdom United States

LM

Table 3 Results using ADF test and no-break LM unit root tests. k^

T^ B

RALS-LM

τ*LM

τ*RALS−LM

^ ρ

−5.362*** −5.250*** −5.692*** −3.720 −5.899*** −6.137*** −4.991** −3.731 −2.540 −4.204* −3.426 −3.074 −4.094* −6.440*** −4.820** −4.119* −4.939** −4.091* −4.906** −3.230 −4.855** −5.364*** −5.578*** −4.419** −6.179***

−5.303*** −4.921** −6.388*** −3.858 −6.625*** −6.317*** −5.895*** −4.021*** −2.065 −3.888 −3.363 −3.101 −3.433 −5.320*** −6.095*** −4.184* −5.000*** −3.657 −5.290*** −3.861 −5.754*** −5.124*** −5.432*** −4.109* −8.306***

0.901 1.043 0.849 0.954 0.864 0.963 0.790 0.181 0.836 1.028 0.577 0.866 0.999 0.946 0.627 0.849 0.951 1.040 0.936 0.860 0.806 0.881 1.040 0.941 0.678

1978 1974 1981 1988 1971 1986 1976 1978 1976 1972 1970 1969 1973 1973 1975 1978 1987 1988 1987 1979 1979 1974 1979 1980 1990

n n

n

n

1994 n 1993 1997 n 1991 1997 2003 n 1995 1997 2004 n 2004 1977 1998 n 1993 n 1986 1985 1996 2000 1991 2001 2004 1987 1985 n 1999 1989 n 2001

7 4 1 0 3 1 3 8 4 2 8 0 2 8 1 3 8 4 4 7 0 0 6 5 1

Notes: since our LM test and RALS-LM test share the same procedure when searching for the break points and the corresponding optimal lags, we only report one time to save the space. k^ is the optimal number of lagged first-differenced terms. T^ B denotes the estimated break point. n denotes that the identified break point was not significant at the 10% level. τ*LM and τ*RALS−LM denote the test statistics for the LM and RALS-LM tests, respectively (since we use transformed test, the test statistics are invariant to the location of trend breaks). *, ** and *** denote the test statistic is significant at 10%, 5% and 1% levels, respectively.

Table 2 Results using one-break LM and RALS-LM unit root tests. Country

Australia Austria Belgium Canada Denmark Finland France Germany Greece Iceland Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Poland Portugal Spain Sweden Switzerland Turkey United Kingdom United States

LM

Country

2

RALS-LM

T^ B

k^

1978 2004 2002 2004 1971 2003 1995 1984 1989 2003 2001 1980 1973 1998 1971 1978 1992 1987 2004 2004 1994 1990 1973 1980 1990

7 6 0 0 3 7 0 0 6 2 8 8 2 4 0 8 3 4 0 7 3 0 0 6 1

2

τ*LM

τ*RALS−LM

ρ^

−3.898** −1.879 −2.867 −2.866 −4.383** −2.761 −3.516* −2.725 −3.315 −2.255 −2.235 −3.878** −2.694 −2.854 −2.849 −3.023 −5.177*** −3.367 −2.571 −1.881 −4.157** −4.194** −3.187 −2.915 −4.093**

−3.760* −1.694 −3.041 −2.776 −4.786*** −2.722 −3.413* −0.753 −3.324* −2.517 −3.634** −3.425 −2.264 −2.790 −3.420* −3.576* −6.633*** −2.478 −2.552 −2.093 −4.229** −4.952*** −3.422* −3.078 −4.554***

1.039 0.799 0.996 0.927 0.938 0.989 0.871 0.209 0.729 0.891 0.715 1.016 0.891 0.808 0.774 0.843 0.678 0.987 0.936 0.839 0.935 0.814 0.733 0.903 0.687

Notes: k^ is the optimal number of lagged first-differenced terms. T^ B denotes the estimated break point. n denotes that the identified break point was not significant at the 10% level. τ*LM and τ*RALS−LM denote the test statistics for the LM and RALS-LM tests, respectively. *, ** and *** denote the test statistic is significant at the 10%, 5% and 1% levels, respectively.

Australia Austria Belgium Canada Denmark Finland France Germany Greece Iceland Ireland Italy Japan Luxembourg Netherlands New Zealand Norway Poland Portugal Spain Sweden Switzerland Turkey United Kingdom United States

LM

RALS-LM

τADF

ADF k^

τLM

τRALS−LM

^2 ρ

k^

−4.548*** −4.120** −2.071 −1.779 −3.714** −2.573 −0.324 −1.547 −1.042 −3.275* −2.890 −5.024*** −1.403 −1.190 −3.886** −2.092 −2.583 −1.586 −0.666 −0.640 −0.912 −1.799 −3.346* −2.896 −3.126

7 1 0 0 7 0 4 0 0 8 8 0 8 0 7 8 2 1 0 7 5 1 0 6 0

−1.560 −3.723*** −3.146** −2.343 −1.802 −2.728 −1.868 −1.183 −0.301 −3.305** −1.716 0.194 0.142 −2.228 −1.945 −1.765 −2.182 −1.462 −1.389 −1.117 −2.233 −1.153 −2.712 −2.617 −2.667

−2.729* −3.523** −3.742*** −2.444 −2.383 −2.539 −1.608 −0.275 0.347 −3.389** −1.357 0.476 0.119 −1.946 −1.514 −2.017 −3.865*** −1.091 −1.337 −1.015 −1.855 −1.687 −2.738* −2.965* −3.010**

0.730 0.710 0.698 1.026 0.562 1.030 1.014 0.206 0.659 1.022 0.989 0.847 1.036 0.770 0.968 1.001 0.692 0.820 0.939 1.007 0.909 0.666 0.685 0.936 0.649

7 0 6 0 0 0 0 0 6 8 8 6 8 4 7 8 2 1 0 7 0 8 0 6 0

Notes: k^ is the optimal number of lagged first-differenced terms. τADF represents the augmented Dickey–Fuller statistic while τLM and τRALS−LM denote the test statistics for the LM and RALS-LM tests, respectively. *, ** and *** denote the test statistic is significant at the 10%, 5% and 1% levels, respectively.

breaks, per capita energy use in sixteen and fifteen countries for LM and RALS-LM test, respectively are found to be stationary.11 To visualize our empirical findings, we superimpose the level and trend breaks identified by the two-break test in Table 1 and plot the log of per capita energy use for each country. Linear trends are then estimated using OLS to connect the break points. The results are displayed in Fig. 1. Upon examination, for most of the countries, it is apparent that per capita energy use appears stationary after allowing for structural breaks. Further examination of the break points in Tables 1 and 2 reveal some interesting observations. For the optimal breaks revealed in Tables 1 and 2, in six countries (Australia, Austria, Japan, Luxembourg, Netherland, and New Zealand) the structural breaks occur within 5 years after the 1973 oil crisis. In five countries (Italy, Spain, Sweden, Turkey, and UK), the structural breaks occur within 5 years after the 1979 oil crisis caused by the Iranian Revolution. In five countries (Austria, Canada, France, Poland and Switzerland), the structural breaks occur within 5 years after the 1990 oil price shock associated with the Gulf War. In seven countries (Belgium, Finland, Iceland, Ireland, Portugal, Spain, and US), the structural breaks occur within 5 years after the 2000 energy crisis. From the above, it is apparent that most structural breaks in relative per capita energy use (61%) occur within 5 years of major global events that eventually impact world energy markets. 5. Concluding remarks and policy implications As economies become more industrialized and the energy sector develops over time, energy use patterns change. The combination of technological advancements and modernization of the energy sector have resulted in improvements in energy efficiency for many industrialized countries. Such improvements in energy efficiency may have 11 The appropriate break numbers are selected with two breaks if both trend break coefficients are significant at 10% level; if one of the two trend break coefficients is not significant, but the trend break coefficient is significant with one break test, we select one break test; otherwise, the no break test is selected.

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541

-0.15

0.300 0.275

-0.20 0.250 -0.25

0.225 0.200

-0.30 0.175 -0.35

0.150 0.125

-0.40 0.100 0.075

-0.45 1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

1960

2010

- Australia

- Austria

0.22

0.70

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

0.20 0.65 0.18 0.60 0.16 0.55

0.14 0.12

0.50 0.10 0.45 0.08 0.40

0.06 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Belgium

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Canada

0.2

0.40 0.35

0.1 0.30 -0.0

0.25 0.20

-0.1 0.15 -0.2

0.10 0.05

-0.3 0.00 -0.4

-0.05 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

- Denmark

- Finland

-0.05

0.20

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

0.15 -0.10

0.10 0.05

-0.15 -0.00 -0.05 -0.20 -0.10 -0.15

-0.25

-0.20 -0.30

-0.25 1960

- France

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010 - Germany

Fig. 1. Log of per capita energy use relative to the mean.

542

M. Meng et al. / Energy Economics 36 (2013) 536–545

-0.50

1.4

-0.75

1.2

-1.00

1.0

-1.25

0.8

-1.50

0.6

-1.75

0.4

-2.00

0.2

-2.25

0.0 1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

- Greece

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Iceland

-0.4

-0.20 -0.25

-0.5

-0.30 -0.6 -0.35 -0.40

-0.7

-0.45

-0.8

-0.50 -0.9 -0.55 -1.0

-0.60

-1.1

-0.65 1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

- Ireland

- Italy

-0.1

1.6

-0.2 1.4 -0.3 -0.4

1.2

-0.5 1.0 -0.6 -0.7

0.8

-0.8 0.6 -0.9 0.4

-1.0 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Luxembourg

- Japan

0.20

-0.00

0.15

-0.05

0.10

-0.10

0.05 -0.15 -0.00 -0.20 -0.05 -0.25 -0.10 -0.30

-0.15

-0.35

-0.20 -0.25 1960 - Netherlands

-0.40 1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

1960 - New Zealand

Fig. 1 (continued).

1965

1970

1975

1980

1985

1990

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2000

2005

2010

M. Meng et al. / Energy Economics 36 (2013) 536–545 0.3

543

-0.1

-0.2 0.2 -0.3 0.1

-0.4

-0.5

0.0

-0.6 -0.1 -0.7

-0.8

-0.2 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Norway

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Poland

-0.6

-0.25

-0.8 -0.50 -1.0 -0.75

-1.2

-1.4

-1.00

-1.6 -1.25 -1.8

-2.0

-1.50

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Portugal

1960

1965

1970

1975

1980

1985

1990

1995

2000

2005

2010

1960 - Switzerland

1965

1970

1975

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1985

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2005

2010

- Spain

0.40

-0.15

0.35

-0.20

0.30

-0.25

0.25

-0.30

0.20

-0.35

0.15

-0.40

0.10

-0.45

0.05

-0.50

0.00

-0.55 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008

- Sweden

-1.2

0.3

-1.3

0.2

-1.4 0.1 -1.5 -0.0 -1.6 -0.1 -1.7 -0.2 -1.8 -0.3

-1.9

-0.4

-2.0 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - Turkey

1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - United Kingdom

Fig. 1 (continued).

544

M. Meng et al. / Energy Economics 36 (2013) 536–545

1.0

usage convergence within a specific country as well as across countries; (2) the convergence of carbon dioxide emissions; and (3) the convergence of energy intensity.

0.9

0.8

References

0.7

0.6

0.5

0.4

0.3 1960 1963 1966 1969 1972 1975 1978 1981 1984 1987 1990 1993 1996 1999 2002 2005 2008 - United States

Fig. 1 (continued).

contributed to the decoupling of the relationship between economic growth and energy consumption within industrialized countries. Indeed, as noted by Jakob et al. (2012), for industrialized countries near their steady state, energy use to support growth may be slowly increasing, constant, or decreasing depending on the growth rates of total factor productivity and energy efficiency. This study focuses on one aspect of this potential decoupling of the growth–energy relationship, namely, the stochastic conditional convergence behavior of per capita energy use among 25 OECD countries by employing newly developed LM unit root tests with trend-breaks suggested by Meng and Lee (2012) and Lee et al. (2012). The results of unit root tests of the log of per capita energy use relative to the average for OECD countries without allowing for structural breaks reveals that seven countries reject the null hypothesis of a unit root for the ADF test; three countries for the LM test; and eight countries for the RALS-LM test. However, the results of unit root tests with allowance of one structural break shows the null hypothesis of a unit root is rejected for eight countries using the LM test and twelve countries with the RALS-LM test. The results of unit root tests with allowance of two structural breaks indicate that under the LM test nineteen countries reject the null hypothesis of a unit root while under the RALS-LM test seventeen countries reject the null hypothesis. Thus, allowance for structural breaks renders the log of per capita energy use relative to the average for OECD countries stationary for many of the countries, which is supportive of the convergence of per capita energy use among countries. With the identification of the dates for the structural breaks along with the visual inspection of the respective countries' per capita energy use demonstrates that a majority of the structural breaks occur within five years of major global events that impact world energy markets. In light of the global shocks to world energy markets, as identified by the structural breaks, the response by many OECD countries has been to seriously consider improvements in energy efficiency and the adoption of new energy-saving technologies. As a result, the reduction in the disparities in per capita energy usage among OECD countries can be attributed to the increase in energy efficiency, the decrease in energy intensity, and greater public awareness of global energy issues and desire to mitigate carbon dioxide emissions which contributes to the convergence of per capita energy usage among these countries. As discussed by Maza and Villaverde (2008), policies oriented towards energy conservation may provide additional cost-savings in terms of energy usage. On the demand side, consumers paying the real price for energy services, subsidizing the use of energy-efficient technologies by consumers, and promotional campaigns to alter energy consumption habits can yield energy savings. With respect to the supply side, the diffusion and adoption of new energy-efficient technologies will also enhance energy savings. Future research can extend the methodological approach undertaken in this study along several fronts: (1) sector analysis of energy

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