Income and CO2 emissions: Evidence from panel unit root and cointegration tests

ARTICLE IN PRESS Energy Policy 37 (2009) 413–423

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Income and CO2 emissions: Evidence from panel unit root and cointegration tests Chien-Chiang Lee , Jun-De Lee Department of Applied Economics, National Chung Hsing University, Taichung 402, Taiwan

a r t i c l e in fo

abstract

Article history: Received 12 May 2008 Accepted 4 September 2008 Available online 4 November 2008

This paper re-investigates the stationarity properties of per capita carbon dioxide (CO2) emissions and real Gross Domestic Product (GDP) per capita for 109 countries within seven regional panel sets covering 1971–2003. We apply the recent unit-root test of the panel seemingly unrelated regressions augmented Dickey–Fuller (SURADF) test developed by Breuer et al. [2001. Misleading inferences from panel unit-root tests with an illustration from purchasing power parity. Review of International Economics 9, 482–493; 2002. Series-specific unit-root tests with panel data, Oxford Bulletin of Economics and Statistics 64, 527–546]. The panel SURADF test accounts for the presence of crosscountry correlations in the data, and the parameters in the panel specification vary across countries. More importantly, this test allows us to identify how many and which members of the panel contain a unit root. Overall, our empirical results illustrate that real GDP and CO2 emissions in these countries are a mixture of I(0) and I(1) processes, and that the traditional panel unit-root tests could lead to misleading inferences as well as the conduct of cointegration analysis being perhaps inappropriate. The results of our analysis carry critical implications for the modeling of CO2 emissions and GDP because of the different orders of integration for the two variables. & 2008 Elsevier Ltd. All rights reserved.

Keywords: Panel seemingly unrelated regressions augmented Dickey–Fuller test Carbon dioxide emissions Real Gross Domestic Product

1. Introduction Previous studies have concluded that real Gross Domestic Product (GDP) and carbon dioxide (CO2) emissions in a panel data framework are best characterized as a unit root, thus supporting the use of a panel cointegration structure for the estimation of the long-run nexus between income and emissions (Dinda and Coondoo, 2006). However, most studies neglect cross-sectional dependence in a non-stationary panel context. The unit root and cointegration models engaged so far are all designed for crosssectional independent panels. Although such methods for crosssectional independent panels are easy to use, mainly due to their increased availability in software packages, hardly any panel of economic data satisfies the cross-sectional independence assumption. This assumption, which requires GDP and the emissions series to be independent across countries, is of course highly restrictive and unlikely to hold. Thus, the problems mentioned above lead us to question a large part of the existing literature. In this paper, the results of an analysis of the relationship between per capita real GDP (hereafter PCGDP) and per capita CO2 emissions (hereafter PCCO2) obtained by applying non-stationary

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panel data techniques to a cross-country panel data set for the two variables have been analyzed.1 Previous studies lack a diagnostic analysis of the order of integration of the variables entering the long-run relationship as implied by the environmental Kuznets curve (EKC hereafter),2 which could lead to spurious regression bias. The presence of a unit root in the income and emission levels have crucial implications for the economic modeling of the income–emissions nexus.3 Granger and Newbold (1974) and Phillips (1986), among others, suggest that a traditional estimation of systems involving non-stationary variables leads to spurious results, because the test statistics no longer follow standard distributions. As a result, if the variables entering the system are non-stationary —I(1)— and non-cointegrated, then the best 1 For convenience of exposition, henceforth the two variables will be referred to as income and emissions, respectively. 2 The EKC is named after Kuznets (1955), who hypothesized that income inequality first rises and then falls as economic development proceeds. Panayotou (1993) referred to this phenomenon as the EKC according to the concept of Kuznets (1955), which illustrates an inverted U-shaped relationship between income and income inequality. 3 The permanent vs. transitory nature of shocks is relevant for theoritical models that aim at being consistent with the actual data generating process of macroeconomic time series (Aksoy and Leon-Ledesma, 2008). From an empirical perspective, the order of integration of the variables has important implications for the appropriate modeling of time-series data. From an economic viewpoint, if the series contain a unit root, business cycle theories describing output fluctuations as temporary deviations from long-run growth trends lose their empirical support (Narayan, 2004).

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modeling strategy to circumvent the problems of spurious results is a vector auto-regression (VAR) in first differences. However, if the variables are I(1) and cointegrated, then a vector errorcorrection model (VECM) is suitable for modeling the system. The main purpose of this paper is to re-investigate the stationarity properties of per capita carbon dioxide emissions and real GDP per capita for 109 countries within seven regional panel sets covering 1971–2003. We apply the panel seemingly unrelated regressions augmented Dickey–Fuller (Panel SURADF) test developed by Breuer et al. (2001), which allows us to account for possible cross-sectional effects and to identify how many and which countries within the panel contain a unit root. Once we confirm the stationary behavior for both series, we can then investigate the relationships between the series based on the different order of integration. That is, for countries in which the two series are I(1), we use the panel cointegration method to evaluate whether GDP and carbon emissions are cointegrated. In addition, we identify the countries for which the two series have different properties, i.e., where one is I(1) and the other is I(0). Under these conditions, GDP and carbon emissions cannot the related to each other in the long run and the conduct of panel cointegration analysis is inappropriate. Finally, we identify countries in which both series are I(0) and investigate whether there is a relationship between GDP and carbon emissions. We will therefore show that the results of the panel SURADF test help us to understand the relationship between GDP and carbon emissions. Our econometric methodology proceeds in five steps. First, we implement the SURADF panel unit-root test to ascertain the order of integration for both of our variables. Second, the sample countries can be classified into three groups—I(0), I(1) and mixed according to the results of the SURADF test. Third, conditional upon finding that both variables are integrated of order one, we further test for panel cointegration using the approach suggested by Pedroni (1999). Fourth, conditional upon finding a cointegration relationship, we calculate panel fully modified ordinary least squares (FMOLS) estimates of the coefficients of real GDP per capita. Fifth, conditional upon finding that both variables are integrated of order zero, we investigate the relationship between GDP and CO2 emissions using the dynamic panel approach suggested by Arellano and Bond (1991) and Arellano and Bover (1995). More than 80% of the world’s current primary energy demand is nowadays met by fossil fuels (see Energy Information Administration, 2004; EIA hereafter). Their use yields CO2 as a joint product, which once released into the atmosphere contributes to climate change with potentially irreversible negative impacts on the world economy. This issue is closely related to the EKC discussion, which investigates the quantitative relationship between emissions of some pollutants and economic activity. A large body of literature has developed in recent years that investigates the relationship between income and pollution (environmental pressure). Early papers by Shafik (1994) and Grossman and Krueger (1995) found evidence of an inverted U-shaped relationship between income and pollution, which has since become known as the EKC. While several studies examine the EKC hypothesis using many different pollutants as proxies for environmental degradation and by utilizing different econometric techniques (Ekins, 1997), these empirical tests of the EKC hypothesis generate mixed results. Harbaugh et al. (2002) point out that the empirical findings in relation to the EKC are more fragile than the corresponding theoretical results. Since panel data provide us with much more information and increase the power of unit-root tests,4 we employ the SURADF

4 It is well known that time series unit root tests have low power, especially in small samples; see Campbell and Perron (1991) and DeJong et al. (1992).

panel unit-root test as it allows us to better characterize the order of integration of income and emission levels, thereby shedding some light on whether the lack of robustness in previous findings has to do with the differing order of integration of the variables, which could lead to spurious regression results. The remainder of this paper is organized as follows. Section 2 provides a brief summary of the literature. Section 3 describes the econometric methodology. Section 4 explains the data. Section 5 reports the empirical results. Finally, in Section 6 we summarize our findings and attempt to draw some policy implications.

2. Literature review Tables 1 and 2 summarize the corresponding mixed results of various unit-root tests for CO2 emissions and income in different countries based on annual data. However, there are sometimes conflicting findings across different countries. Recent studies have questioned the empirical validity of the EKC for various pollutants on the basis of a lack of theoretical grounding behind the reducedform relationship. Moreover, the estimated results are sensitive to slight variations in the specification and control set, as well as to a number of econometric problems. For example, Stern and Common (2001) estimate an EKC for SO2 emissions for 74 countries over the period 1960–1990, finding fragile results caused by omitted variable bias. Their results also reveal that the sample selection affects the point estimates of specifications in levels, while models in first differences behave more robustly. This may be an indication that specifications in log-levels may constitute spurious regressions caused by the differing order of integration or the non-cointegration of the variables, although these issues are not formally addressed in their analysis. Focacci (2005) examines the EKC hypothesis for three developing countries, namely, Brazil, China and India, and finds that it does not hold for such countries. Paudel et al. (2005) utilize semiparametric and parametric models to investigate the EKC for three types of water pollution, namely, nitrogen, phosphorus and dissolved oxygen (DO), and indicate that the EKC for nitrogen is significantly found, but not for phosphorus or DO. Kanjilal and Ghosh (2002) also test for cointegration and Granger causality between industrial CO2 emissions and GDP for India, and the ADF tests reveal that both series are non-stationary and are individually integrated. Coondoo and Dinda (2002) investigate the direction of causality in the emission–income relationship for a sample of 88 countries over the period 1960–1990 using data for first differences. By only addressing short-run causality, they circumvent the issues of stochastic trends and spurious regression. Most studies also lack a diagnostic analysis upon the order of integration of the variables entering the long-run relationship implied by the EKC, which could lead to spurious regression bias. Stern (2004) pays more attention to those studies dealing with stochastic trends and related issues such as a non-linear transformation of non-stationary variables, cross-sectional dependence, and finite-sample bias. He outlines those studies dealing with the issues of heteroskedasticity, simultaneity and omitted variable bias, data poolability, and heterogeneity in the sample selected. Perman and Stern (2003) were the first to raise serious concerns regarding the issue of panel non-stationarity in income and emissions. By using the panel unit-root tests of Levin et al. (2002, LLC) and Im et al. (2003, IPS), they found that the three series—sulphur dioxide (SO2) emissions, GDP per capita, and its square all expressed in natural logs—are non-stationary for a panel of 74 countries spanning the period 1960–1990. However, unlike the panel SURADF test, the LLC and IPS tests fail to allow for

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Table 1 Comparison of previous empirical results from various unit-root tests for CO2 emissions Author(s)

Sample

Method

Results

Aldy (2006) Chang and Lee (2008)

88 countries 21 OECD countries

Elliott et al. (1996) DF-GLS test Lee and Strazicich (2003, 2004)

Lee and Chang (2008) Dinda and Coondoo (2006) Friedl and Getzner (2003) Kanjilal and Ghosh (2002) McKitrick and Strazicich (2005)

21 OECD countries 12 country-groups Austria India 121 countries

Sen (2003) Panel IPS (2003) test ADF (1979) test ADF (1979) test Two-break LM test

Nguyen-Van (2005)

100 countries

Non-parametric methods

Perman and Stern (2003) Slottje et al. (2001) Strazicich and List (2003) Sun and Wang (1996)

74 countries US 21 industrial countries Global CO2 emissions

Panel LLC (2002) and IPS (2003) tests Phillips and Perron (1988) test Panel IPS (2003) test ADF (1979) test

Only 13 of 88 countries are stationary Most per capita CO2 emissions and average series are stationary. Stationary Non-stationary Non-stationary Non-stationary Individual: mixed Panel: stationary Industrial countries show a convergence pattern, but little evidence for the developing countries. Non-stationary Non-stationary Stationary Non-stationary

Note: ADF denotes Dickey and Fuller (1979) test; LLC denotes Levin et al. (2002) test; IPS denotes Im et al. (2003) test.

Table 2 Comparison of previous empirical results from various unit-root tests for Income Author(s)

Sample

Method

Results

Abras et al. (2004) Bru¨ggemann and Trenkler (2007)

Brazilian Czech Republic, Hungary, Poland, Greece, Portugal, Spain

Lee and Strazicich (2003) the 2-break LM unit-root test Lee and Strazicich (2003) the two-break LM unit-root test

Galva˜o and Gomes, (2007) Johnson (2000) Lee and Chang (2007) Lee and Strazicich (2003) Narayan (2008)

19 Latin American countries 48 US states 22 developed and 18 developing countries US 24 Chinese provinces

Smyth and Inder (2004)

25 Chinese provinces

Strazicich et al. (2004)

OECD countries

Lee and Strazicich (2003) the 2-break LM unit-root test Non-parametric Carrion-i-Silvestre et al. (2005) multiple structural breaks test Lee and Strazicich (2003) the 2-break LM unit-root test Carrion-i-Silvestre et al. (2005) multiple structural breaks test Zivot and Andrews, (1992) and Lumsdaine and Papell (1997) unit-root tests Lee and Strazicich (2003) 2-break LM unit root test

Non-stationary Stationary: Czech Republic, Hungary, Poland, Greece, Spain, Non-stationary: Poland Stationary Convergence Stationary

both parameters in the panel specification to vary across countries and the presence of cross-country correlations in the data. This is likely to lead to misleading inferences in regard to the order of integration of the variables under consideration. Nguyen-Van (2005) applies non-parametric methods to examine the convergence in CO2 emissions per capita for a sample of 100 countries covering the period from 1966 to 1996. He reports that industrialized countries exhibit a convergence pattern, but find little evidence of convergence for the whole sample. Dinda and Coondoo (2006) investigate the long-run relationship and the direction of causality in the relationship between income and CO2 emissions for a panel of 88 countries. For different country-groups over 1960–1990, they find that the IPS test renders overwhelming evidence of non-stationarity in both per capita real GDP and CO2 emission levels.5 Richmond and Kaufmann (2006) estimate EKCs for a sample of 36 countries over the period 1973–1997 using data on fuel mix. Evidence from IPS and LLC tests point to CO2 emissions, fuel mix, and GDP per capita being non-stationary. Romero-A´vila (2008) investigates the time series properties of per capita CO2 emissions and per capita GDP levels for a sample of 86 countries for 1960–2000. He employs a state-of-the-art panel stationarity test

5 McAleer and Chan (2006) apply the generalized autoregressive conditional heteroscedasticity (GARCH) model and its asymmetric variations to examine the trend and volatility in atmospheric carbon dioxide concentration levels.

Stationary Stationary Half of the provinces are stationary Stationary

which incorporates multiple shifts in level and slope, thereby controlling for cross-sectional dependence through bootstrap methods. His analysis provides clear-cut evidence that per capita GDP levels are non-stationary for the world as a whole, while per capita CO2 is found to be regime-wise trend stationary. However, it must be kept in mind that all of these previous studies—in the testing for a unit root—are joint tests of a unit root for all members of the panel and are incapable of determining the mix of I(0) and I(1) series in a panel setting. Our test provides information regarding the number and identity of the panel members that reject or do not reject the null hypothesis of a unit root. When a series is non-stationary, testing for the presence of cointegration among the variables may take place, although the traditional regression model may still be used.

3. Methodology It has been suggested that one feasible way to increase power when testing for a unit root is to use panel data. Perhaps one of the most notable works is that of Breuer et al. (2001, 2002) who show that the recent methodological refinements to the LLC test fail to fully address the ‘‘all-or-nothing’’ nature of the test. Therefore, their rejection signifies that at least one panel member is stationary, with no information about how many series are stationary. Given that they are joint tests of the null hypothesis, they are not informative when it comes to the number of series

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that are stationary processes when the null hypothesis is rejected. In expanding this issue, Breuer et al. (2001, 2002) develop a panel unit-root test that is based on the ADF regressions estimation in a seemingly unrelated regressions (SUR) framework and then test for an individual unit root within the panel members. This procedure has several advantages as follows. First, these multivariate tests use the information content in the variance– covariance matrix, so that the unrealistic assumption of crosssection independence made in the panel tests can be avoided. Second, conventional univariate unit-root tests not only fail to consider information across regions but are also restricted regarding the problem of a small sample, thereby leading to less efficient estimations (see Chiu, 2002). For this reason, we implement multivariate ADF-type unit-root tests that have better power properties than their univariate counterparts. Exploiting the information from the error covariance and allowing for an autoregressive process will produce more efficient estimators than the single-equation methods. Third, the estimation tests also allow for an important degree of heterogeneity in the lag structure across the panel members, in that the lag order of the augmented test can vary among the individuals and the autoregressive parameter can also differ for every cross-section. Fourth, the panel SURADF unit-root test allows us to identify how many and which members of the panel contain a unit root.6 However, a common feature of the panel tests is that they maintain the null hypothesis of a unit root in all panel members. Therefore, their rejection indicates that at least one panel member is stationary, although there is no information regarding which ones are stationary (Breuer et al., 2001, 2002; Camarero and Tamarit, 2004). Moreover, Sarno and Taylor (1998) point out that the conventional types of panel unit-root tests are biased towards being stationary, if only one series is strongly stationary. The unit-root test of the panel SURADF for N series and T time periods is based on the system of ADF equations, which can be represented as

DZ 1;t ¼ a1 þ b1 Z 1;t1 þ Zt þ

k1 P j¼1

DZ 2;t ¼ a2 þ b2 Z 2;t1 þ Zt þ

k2 P j¼1

j1;j DZ 1;tj þ e1;t

t ¼ 1; 2; . . . ; T

j2;j DZ 2;tj þ e2;t

t ¼ 1; 2; . . . ; T

DZ N;t ¼ aN þ bN Z N;t1 þ Zt þ

kN P j¼1

H10 : b1 ¼ 0; H1A

: b1 o0

H20 : b2 ¼ 0; H2A ...

: b2 o0 ...

N HN 0 : bN ¼ 0; H A

: bN o0

(2)

with the test statistics being computed from SUR estimates of system (1), while the critical values are generated by Monte Carlo simulations. The major difference lies in the formulation of the null hypothesis between the panel SURADF and other panel unit tests, such as the multivariate augmented Dickey–Fuller (MADF) test of Taylor and Sarno (1998), and the LLC and IPS tests. While the other tests are joint tests of a unit root for all members of a panel, the panel SURADF tests investigate a separate unit-root null hypothesis for each individual panel member and therefore identify precisely how many and exactly which ones among the series in the panel are stationary processes (see footnote 7). Breuer et al. (2002) document the power of the SURADF test in various environments regardless of whether the series are I(0) or not I(0). Meanwhile, the advantage of the panel SURADF is that it analyzes the variables without imposing uniformity across the panel under either the null or alternative hypotheses in accordance with the SURs. More importantly, this testing procedure enables us to handle heterogeneous serial correlation across panel observations, especially when residual cross-correlations are high. In other words, we demonstrate that the panel SURADF test is more powerful than the single-equation (Dickey-Fuller, 1979; ADF hereafter) tests used here. In addition, although the panel SURADF test is needed to simulate critical values specific to each empirical environment, such simulations generally increase the power of the test for hypotheses such as those in the other panel tests. This is why we can find that the experiment to test the power of the SURADF exceeds that of the ADF test in Breuer et al. (2002).

4. Data .. .

.. .

cross-sectional unit in the panel. The flexibility to test for a unit root within each cross-sectional unit is especially beneficial for applied work where mixed stationary and non-stationary series are likely. This system is estimated by the SUR procedure, and we test the N null (Hi0 ) and alternative hypotheses (HiA ) individually as

jN;j ZX N;tj þ eN;t t ¼ 1; 2; . . . ; T; (1)

where Z denotes CO2 per capita (PCCO2) or real GDP per capita (PCGDP),7 and ei,t (i ¼ 1,2, y, N) is an error term. Coefficient ai is the heterogeneous constant term, bi ¼ ri1, and ri is the autoregressive coefficient for the ith cross-sectional member of the series, while t denotes the deterministic time trend. Eq. (1) tests the null hypothesis of a unit root against the trend stationarity. The model allows for heterogeneous fixed-effects, heterogeneous trend effects, and heterogeneous lags for each 6 Mark (2001) indicates that one potential pitfall with the panel test is that the rejection of the non-stationarity hypothesis does not mean that all series are stationary. It is possible that out of N time-series, only one is stationary and (N–1) have a unit-root process. 7 Given that the panels are stationary without structural breaks, introducing multiple breaks will reduce the power of the panel data unit-root tests (Narayan and Smyth, 2007). Although Romero-A´vila (2008) has proposed a panel stationarity test with structural breaks, a similar problem that arises is that related to the null of stationarity in all panel members. Therefore, their rejection indicates that at least one panel member is non-stationary, with no information being provided as to how many series and which ones are stationary.

The primary data employed in this paper are the annual CO2 emissions measured in metric tons per capita and GDP per capita is constant at year 2000 prices and is expressed in US dollars.8 In using panel data, different countries are collectively treated as one entity and not as separate units. To partially resolve the homogeneity problem, we classify the panel data into seven sub-panels. We consider the regional effects to be determined by seven geographical groups made up of 109 world countries, which we divide in accordance with the World Development Indicators (WDI, 2006). We use the time period 1971–2003, because that is the period for which the empirical data are available. There are 14 countries in East Asia and the Pacific, 22 in Europe and Central Asia, 24 in Latin America and the Caribbean, 10 in the Middle East and North Africa, two in North America, five in South Asia, and 32 in Sub-Saharan Africa. All of the data for the variables are in natural logarithms and are taken from the WDI (2006). As shown in Figs. 1 and 2, even though North America and Australia have high levels of real GDP per capita, they both face a more serious CO2 emissions problem. There are low levels of income and CO2 emissions per capita in the Middle East and North 8 Per capita numbers give the emissions the same units for large and small countries, and so they control the scale of the economy (Lanne and Liski, 2004).

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Fig. 1. The geographic distribution of CO2 emissions per capita in the world (Source: WDI, 2006).

Fig. 2. The geographic distribution of GDP per capita (constant 2000 US$) in the world (Source: WDI, 2006).

Africa. By contrast, there are high levels of CO2 emissions per capita in North America. At first sight, the distributions of CO2 and real GDP per capita appear to be a regional feature. According to this view, we consider the country classification criteria of the World Bank based on the geographic contiguity and use seven regional-based panel datasets. The objective is to regard the ‘‘regional effects’’ as being determined by geographical groups in 109 countries divided into seven regions. It is expected that the empirical results will lead to different policy implications and strategies for all seven regions. One thing worth bearing in mind is that using panel data creates another problem in which different countries as a whole are treated as an entity and not as separate units. As a result, we cannot identify the differences in the EKC hypothesis among countries. However, the environmental quality of countries may

be influenced by their neighboring countries (Ansuategi, 2003; Helland and Whitford, 2003; Gray and Shadbegian, 2004; Maddison, 2006; Sigman, 2002). Coondoo and Dinda (2002) also show that there are different causal relationships for different levels of income. Stern and Common (2001) indicate that using mixed data for OECD and non-OECD countries to examine the global EKC hypothesis is inappropriate.

5. Empirical results 5.1. The results of the SURADF test In order to obtain meaningful regression results from a regression containing integrated variables, it is necessary for

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these variables to be cointegrated. Therefore, the first step in the analysis is to test for a unit-root type of non-stationarity and, if this is confirmed, a panel cointegration analysis will be the next step. To provide the number of unit roots or stationary crosssection elements, we utilize the Breuer et al. (2001, 2002) test that individually tests the null of non-stationarity in the SUR grid. The critical values are obtained by simulation and are different for each of the cross-sections. As reported in Tables 3 and 4, the estimated 1%, 5%, and 10% critical values are obtained from finite sample simulations of 10,000 replications using the lag and covariance structure from the panel of per capita CO2 emissions and real GDP per capita. As Breuer et al. (2001) show that the imposition of an identical lag structure across panel members could bias the test statistics, we select the lag structures for each equation based on the method of Perron (1989).9 Our results reveal that the two variables are a mixture of I(0) and I(1) processes. Table 3 indicates that South Asia, East Asia and the Pacific, and the Middle East and North Africa are unable to reject the null hypothesis of a non-stationary series at the 10% level except for Singapore and Saudi Arabia. In this strand of the literature we find that Wagner and Mu¨ller-Fu¨rstenberger (2004), Dinda and Coondoo (2006), and Richmond and Kaufmann (2006) employ panel unit-root tests. Similar results hold for Stern (2002) and Perman and Stern (2003) for the case of SO2 emissions. For roughly half of Europe and Central Asia, Latin America and the Caribbean, and Sub-Saharan Africa, the null hypothesis of a non-stationary series is rejected. Therefore, how to control the equilibrium value or trend path in the long run is a critical target for rejecting the null hypothesis countries rather than a temporary reduction in the short run. As shown in Table 4 for South Asia, North America, East Asia and the Pacific, Europe and Central Asia, and the Middle East and North Africa, we cannot reject the null hypothesis except for the Solomon Islands, Iceland, Italy, Latvia, Iran, and Malta. Similar results are obtained by Abras et al. (2004) and Chang et al. (2006). Generally speaking, the null hypothesis of non-stationarity cannot be rejected for half of Latin America and the Caribbean, and SubSaharan Africa at the 10% significance level. We find that different reasons for stationarity exist between regions. The empirical results provide two views which could explain why CO2 per capita emissions and real GDP per capita are or are not stationary. First, the European Union (EU) considered imposing a carbon tax covering its member states prior to starting its emissions trading scheme (ETS) in 2005. The UK has unilaterally introduced a range of carbon taxes and levies to accompany the EU ETS trading regime. Thus, most European countries and the UK have contributed towards stationarity. Second, the countries with less CO2 per capita emissions and less real GDP per capita, such as Sub-Saharan Africa, seem to be closely connected to the characteristic of stationarity. Third, it is easier for Latin America and the Caribbean with an abundance of energy resources to be characterized by stationarity. 5.2. The estimation of income–emissions Through our SURADF test results, we summarize the results of the panel unit-root tests for GDP and CO2 emissions in Table 5. Among them, both series are I(1)/I(0) in 54/44 countries, but since 9

The lag parameters are selected based on the recursive t-statistic as suggested by Perron (1989). The maximum lag length for the general to specific methodology is set at 8. Monte Carlo simulations suggest that this approach has better properties in terms of size and power than information-based methods such as the Akaike Information Criterion and Schwartz Bayesian Criterion (Ng and Perron, 1995).

Table 3 Panel SURADF tests and critical values for CO2 emissions per capita Country

Test statistics

Critical values 1%

5%

10%

Part A: East Asia and the Pacific Australia China Hong Kong, China Indonesia Japan Kiribati Korea, Dem. Rep. Malaysia New Zealand Papua New Guinea Philippines Singapore Solomon Islands Thailand

1.869 1.411 2.952 2.244 1.216 3.713b 1.358 0.512 0.746 1.823 1.299 4.734c 2.850 0.480

3.760 4.296 4.216 4.273 4.128 3.824 4.684 4.131 3.928 4.285 3.945 4.385 4.469 4.505

3.206 3.621 3.613 3.679 3.533 3.254 4.005 3.504 3.347 3.637 3.383 3.733 3.798 3.856

2.901 3.263 3.277 3.312 3.194 2.959 3.624 3.208 3.010 3.291 3.049 3.364 3.471 3.511

Part B: Europe and Central Asia Austria Belgium Denmark Finland France Georgia Germany Greece Hungary Iceland Ireland Italy Latvia Luxembourg Netherlands Norway Portugal Spain Sweden Switzerland Turkey United Kingdom

7.682c 2.995 7.188c 4.260c 4.326b 2.558 0.195 0.922 0.848 6.880c 0.457 2.966 2.636 4.011 5.269c 8.222c 2.242 1.595 3.714 5.464c 2.033 4.952c

5.574 5.723 5.834 4.173 5.113 5.328 5.804 4.466 5.218 4.863 4.276 5.229 5.067 5.963 5.067 4.976 4.816 5.759 6.294 5.346 5.219 4.620

4.882 5.067 5.105 3.575 4.401 4.651 5.017 3.817 4.505 4.166 3.628 4.507 4.362 5.203 4.316 4.276 4.105 5.058 5.582 4.676 4.473 3.956

4.504 4.663 4.703 3.226 4.038 4.278 4.626 3.466 4.126 3.805 3.265 4.140 3.985 4.785 3.934 3.896 3.742 4.652 5.172 4.266 4.076 3.597

Part C: Latin America and the Caribbean Argentina 9.094c Belize 0.162 Bolivia 3.268 Brazil 2.640 Chile 1.413 Colombia 4.434a Costa Rica 2.651 Dominican Republic 1.305 Ecuador 9.322c El Salvador 1.518 Guatemala 1.594 Guyana 5.269b Haiti 9.103c Honduras 1.600 Jamaica 1.821 Mexico 5.330c Nicaragua 6.932c Panama 3.308 Paraguay 4.112 Peru 2.556 St. Vincent and the Grenadines 1.136 Trinidad and Tobago 5.150c Uruguay 5.660b Venezuela, RB 2.693

5.587 4.973 6.067 4.195 5.650 5.289 5.699 5.853 5.938 5.683 4.473 5.338 6.105 5.453 4.837 4.914 6.003 4.748 5.718 4.943 5.509 4.787 5.675 4.977

4.867 4.265 5.345 3.560 4.891 4.567 4.995 5.076 5.235 4.941 3.840 4.592 5.409 4.652 4.115 4.129 5.183 4.042 4.993 4.255 4.788 4.117 5.000 4.365

4.490 3.857 4.951 3.239 4.490 4.208 4.599 4.695 4.831 4.605 3.473 4.246 5.014 4.271 3.753 3.784 4.773 3.671 4.589 3.881 4.390 3.735 4.611 4.002

Part D: The Middle East and North Africa Algeria 1.531 Egypt, Arab Rep. 1.612 Iran, Islamic Rep. 1.576 Israel 0.388 Malta 0.893 Morocco 2.893 Oman 2.109

3.661 3.916 3.901 4.072 3.759 4.096 3.868

3.103 3.332 3.320 3.439 3.219 3.470 3.272

2.809 3.035 3.013 3.116 2.895 3.129 2.939

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Table 4 Panel SURADF tests and critical values for real GDP per capita

Table 3 (continued ) Country

Test statistics

Critical values Country 1%

5%

10%

Saudi Arabia Syrian Arab Republic Tunisia

3.868c 2.605 2.433

3.816 4.066 4.115

3.257 3.464 3.483

2.945 3.151 3.162

Part E: North America Canada United States

1.376 3.665c

3.539 3.596

3.027 3.012

2.724 2.713

Part F: South Asia Bangladesh India Nepal Pakistan Sri Lanka

0.519 1.970 0.731 1.854 0.304

3.554 3.492 3.502 3.441 3.489

2.993 2.956 2.876 2.857 2.909

2.686 2.657 2.602 2.556 2.613

1.458 10.047c 2.746 2.915 6.441c 3.757b 0.403 0.561 6.946c 5.773c 1.726 10.210c 1.367 2.261 3.793a 3.843a 4.598c 2.332 2.354 3.990b 5.292c 1.544 4.144c 5.584c 1.164 2.608 2.126 5.750c 4.878c 3.875b 1.121 0.468

5.203 4.626 3.998 4.022 4.314 3.802 3.778 4.664 4.739 4.368 4.584 4.349 4.248 4.476 4.521 4.593 4.545 4.286 4.339 4.619 4.450 4.429 4.087 4.459 3.983 4.097 3.803 4.530 4.428 3.979 4.164 4.280

4.545 3.999 3.351 3.370 3.651 3.246 3.204 3.993 4.044 3.763 3.983 3.680 3.585 3.865 3.896 3.940 3.860 3.647 3.697 3.943 3.805 3.735 3.431 3.851 3.434 3.525 3.216 3.837 3.781 3.388 3.536 3.587

4.197 3.649 3.023 3.041 3.305 2.922 2.896 3.647 3.668 3.444 3.634 3.351 3.235 3.522 3.574 3.562 3.523 3.286 3.358 3.599 3.453 3.377 3.122 3.490 3.102 3.179 2.891 3.490 3.480 3.073 3.223 3.285

Part G: Sub-Saharan Africa Benin Botswana Burkina Faso Burundi Cameroon Central African Republic Chad Congo, Dem. Rep. Congo, Rep. Cote d’Ivoire Gabon Gambia, The Ghana Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Niger Nigeria Rwanda Senegal Seychelles Sierra Leone South Africa Sudan Swaziland Togo Zambia Zimbabwe

419

Notes: Critical values are calculated by Monte Carlo simulation with 10,000 draws and fitted to the present sample size. a 10% significant. b 5% significant. c 1% significant.

the remainder is order-different, we thus conduct panel cointegration tests in those countries with I(1) series. We follow Dinda and Coondoo (2006) and proceed to test the GDP and CO2 emissions for cointegration in order to determine if there is a long-run relationship to control for in the econometric specification. We first implement the following equations: LPCCO2it ¼ ai þ di t þ bi LPCGDP it þ it

(3)

where ln(real GDP per capita) stands for LPCGDP; and ln(per capita CO2 emissions) stands for LPCCO2, t ¼ 1, y, T time periods, and i ¼ 1, y, N countries of the panel. All of these allow for cointegrated vectors of differing magnitudes between countries, as well as for country (ai) and trend-effects (dit). These trendeffects are intended to capture any disturbances that are common across different members of the panel, such as global disturbances and international business cycles. All the variables are expressed

Test statistics

Critical values 1%

5%

10%

Part A: East Asia and the Pacific Australia China Hong Kong, China Indonesia Japan Kiribati Korea, Dem. Rep. Malaysia New Zealand Papua New Guinea Philippines Singapore Solomon Islands Thailand

1.293 0.798 3.192 2.749 1.453 2.708 2.249 1.666 0.816 2.912 1.628 2.771 3.070a 2.167

3.865 4.327 4.555 4.572 4.515 4.550 5.082 4.922 4.031 4.350 4.027 4.699 3.849 4.904

3.257 3.662 3.913 3.957 3.804 3.917 4.391 4.292 3.432 3.708 3.388 4.062 3.197 4.252

2.930 3.301 3.564 3.612 3.471 3.557 4.043 3.947 3.107 3.357 3.062 3.700 2.878 3.919

Part B: Europe and Central Asia Austria Belgium Denmark Finland France Georgia Germany Greece Hungary Iceland Ireland Italy Latvia Luxembourg Netherlands Norway Portugal Spain Sweden Switzerland Turkey United Kingdom

3.498 3.462 1.890 2.352 4.454 3.019 3.606 1.287 2.778 3.770a 0.664 5.549a 4.672a 1.561 2.684 3.278 1.587 1.461 1.528 3.105 1.488 0.248

5.531 6.726 5.780 7.027 6.428 5.127 6.890 5.695 5.710 4.535 4.736 6.327 5.795 6.093 6.249 6.333 6.023 6.393 5.631 6.848 4.180 6.141

4.786 6.079 5.054 6.360 5.694 4.414 6.165 4.960 4.942 3.909 3.993 5.608 5.024 5.331 5.441 5.633 5.257 5.708 4.897 6.088 3.534 5.428

4.397 5.729 4.669 6.006 5.307 4.060 5.781 4.572 4.558 3.553 3.631 5.227 4.631 4.931 5.021 5.265 4.855 5.311 4.536 5.680 3.169 5.042

Part C: Latin America and the Caribbean Argentina 4.365a Belize 1.553 Bolivia 5.880c Brazil 5.874c Chile 0.732 Colombia 4.104a Costa Rica 1.962 Dominican Republic 0.001 Ecuador 7.007c El Salvador 7.136c Guatemala 5.116 Guyana 2.439 Haiti 0.230 Honduras 5.865c Jamaica 2.305 Mexico 3.411 Nicaragua 3.721 Panama 2.980 Paraguay 5.130c Peru 1.554 St. Vincent and the Grenadines 0.291 Trinidad and Tobago 4.836 Uruguay 5.838b Venezuela, RB 0.192

5.340 5.254 5.621 5.557 5.811 5.150 5.578 5.353 5.056 6.054 6.327 4.939 4.280 5.584 4.473 5.276 5.683 5.060 4.930 4.593 5.474 6.399 5.847 4.511

4.589 4.480 4.839 4.830 5.162 4.428 4.832 4.631 4.360 5.273 5.589 4.271 3.623 4.890 3.837 4.607 5.011 4.360 4.221 3.979 4.740 5.717 5.101 3.829

4.216 4.131 4.436 4.418 4.795 4.059 4.440 4.228 3.966 4.900 5.197 3.897 3.294 4.520 3.465 4.200 4.600 3.961 3.852 3.628 4.373 5.338 4.743 3.463

Part D: The Middle East and North Africa Algeria 1.582 Egypt, Arab Rep. 2.734 Iran, Islamic Rep. 3.853b Israel 1.205 Malta 4.184c Morocco 0.574 Oman 1.053

4.033 3.859 3.875 3.904 3.607 3.799 3.968

3.419 3.263 3.272 3.348 3.026 3.232 3.350

3.096 2.950 2.961 3.049 2.729 2.930 3.011

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Table 5 List of selected countries by different properties of both variables

Table 4 (continued ) Country

Test statistics

Critical values 1%

5%

10%

Saudi Arabia Syrian Arab Republic Tunisia

1.348 2.768 0.013

3.693 4.029 4.015

3.091 3.432 3.385

2.796 3.100 3.025

Part E: North America Canada United States

0.775 0.889

3.823 3.873

3.239 3.272

2.954 2.942

Part F: South Asia Bangladesh India Nepal Pakistan Sri Lanka

1.355 1.978 1.219 1.739 0.179

3.789 3.954 3.468 3.633 3.890

3.200 3.355 2.978 3.079 3.272

2.861 3.049 2.664 2.787 2.980

Part G: Sub-Saharan Africa Benin Botswana Burkina Faso Burundi Cameroon Central African Republic Chad Congo, Dem. Rep. Congo, Rep. Cote d’Ivoire Gabon Gambia, The Ghana Guinea-Bissau Kenya Liberia Madagascar Malawi Mali Mauritania Niger Nigeria Rwanda Senegal Seychelles Sierra Leone South Africa Sudan Swaziland Togo Zambia Zimbabwe

2.530 1.339 2.329 1.097 3.372a 1.251 4.180c 1.469 3.415a 0.769 3.681a 3.995b 4.497b 4.943c 3.381b 1.061 2.125 4.285c 1.640 8.795c 3.458 2.571 5.021c 5.098c 2.548 1.567 3.450 0.696 2.300 1.570 4.389b 3.816b

4.248 4.218 4.056 4.127 4.234 3.977 4.020 4.259 4.140 4.657 4.606 4.347 4.658 4.716 4.244 4.279 4.561 4.143 4.273 5.207 4.115 4.004 4.548 4.257 4.062 4.460 4.814 4.908 4.200 4.318 4.704 4.349

3.641 3.575 3.408 3.497 3.610 3.350 3.368 3.692 3.509 3.961 3.911 3.708 3.989 4.030 3.644 3.658 3.867 3.512 3.652 4.528 3.517 3.403 3.831 3.609 3.409 3.778 4.139 4.214 3.593 3.708 4.073 3.684

3.309 3.241 3.085 3.167 3.251 3.039 3.050 3.340 3.205 3.606 3.565 3.358 3.638 3.656 3.323 3.289 3.511 3.184 3.323 4.148 3.187 3.093 3.492 3.267 3.084 3.429 3.782 3.835 3.273 3.377 3.713 3.325

Notes: Critical values are calculated by Monte Carlo simulation with 10,000 draws and fitted to the present sample size. a 10% significant. b 5% significant. c 1% significant.

in natural logarithms such that the elasticities can also be interpreted, and b is the parameter of the model to be estimated, and eit is the residual. Having ascertained that the two variables in a panel sense are non-stationary for all the countries, the aim of this section is to test for panel cointegration. We use the Pedroni (1999) panel cointegration test. The results from a total of seven different panel test statistics suggested by Pedroni (1999) are reported in Table 6. For our panel of 54 countries, we use a cointegration test which, when compared with the cross-section approach, is more powerful and allows us to increase the degrees of freedom. We then use the fully modified OLS (FMOLS henceforth) technique to estimate the cointegration vector for heterogeneous cointegrated panels. This enables us to correct the standard OLS for bias induced by endogeneity and the serial correlation of the regressors.

Two series are I(1): 54 countries

Two series are I(0): 44 countries

Two series have different properties: 11 countries

Australia China Hong Kong, China Indonesia Japan Korea, Dem. Rep. Malaysia New Zealand Papua New Guinea Philippines Thailand Belgium Georgia Germany Greece Hungary Ireland Luxembourg Turkey Portugal Spain Sweden Belize

El Salvador Paraguay Mexico Nicaragua Malta Trinidad and Tobago Saudi Arabia Sudan Swaziland Togo Zambia Zimbabwe Singapore Solomon Islands Kiribati Austria Denmark Finland France Netherlands Norway United States Botswana Central African Republic Chad Italy Latvia Switzerland United Kingdom Guyana Haiti Honduras

Ecuador Colombia Uruguay Iceland Cameroon Congo, Rep. Kenya Mauritania Argentina Rwanda Senegal

Chile Costa Rica Dominican Republic Guatemala Jamaica Panama Peru St. Vincent and the Grenadines Venezuela, RB Algeria Egypt, Arab Rep. Israel Morocco Oman Syrian Arab Republic Tunisia Canada Mali Bangladesh India Nepal Pakistan Sri Lanka Benin Burkina Faso Burundi Congo, Dem. Rep. Seychelles Sierra Leone South Africa Nigeria

Bolivia Brazil Cote d’Ivoire Gabon Gambia, The Ghana Guinea-Bissau Liberia Madagascar Malawi Niger Iran, Islamic Rep.

Table 6 presents the panel cointegration estimated results. Note that the dependent variable is CO2 emissions (LPCCO2). Except for the panel r (no time effects) and the group r statistics, all other statistics significantly reject the null of no cointegration. Although this is not the case with all tests, we generally obtain strong evidence of integration among these series.10 Thus, it can be predicted that CO2 emissions and GDP move together in the

10 Pedroni (1999) shows that the panel-ADF and group-ADF tests have better small-sample properties than the other tests, and hence they are more reliable.

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Table 6 Pedroni’s (1999) panel cointegration test results

Panel variance ratio Panel r Panel PP Panel ADF Group r Group PP Group ADF

421

Table 8 The generalized method of moments model: dependent variable is CO2

No time effects

Fixed time effects

Regressors

Panel (without time dummies)

Panel (with time dummies)

1.786** 0.683 1.435* 1.498* 1.059 3.329** 5.223**

2.141** 1.404* 3.929** 6.381** 0.694 3.906** 5.962**

GDP

0.212** (6.283) 0.248

0.197** (5.881) 0.113

Sargn test

Note: The Sargan test: the null hypothesis is that the instruments used are not correlated with the residuals. All equations include time dummies as regressors and instruments. ** indicates significance at the 5% level. Instruments: lagged levels for differences, lagged differences for levels.

Note: Statistics are asymptotically distributed as normal. The variance ratio test is right sided, while the others are left sided. ** and * denote that rejects the null of no cointegration at the 5% and 10% level, respectively. For the formulas used in the panel cointegration test statistics, readers are referred to Pedroni (1999).

6. Conclusions and policy implications Table 7 Fully modified OLS estimates: dependent variable is CO2 Regressors

Panel (without time dummies)

Panel (with time dummies)

GDP

0.88** (56.57)

0.58** (23.83)

Note: t-values are in parentheses. ** indicates statistical significance at the 5% level.

long run for the 54 selected countries after allowing for the country-specific effects. The next step consists of the long-run equations, which are estimated using the FMOLS estimation technique for heterogeneous cointegrated panels. Table 7 provides the results of the panel FMOLS tests where the dependent variable is CO2 emissions. All of the coefficients of GDP are statistically significant at the 5% level regardless of whether or not time dummies are included in our model, and the effect is positive. Implicit here is that a 1% increase in GDP increases CO2 emissions by around 0.6–0.9% in our 54 selected samples. We obtain similar panel cointegraton results to those of Dinda and Coondoo (2006), but our estimations give rise to higher coefficients. We provide reasonable explanations as follows. Dinda and Coondoo (2006) use the IPS (2003) unit-root test, which is unable to determine whether the series are I(0) or I(1), which leads us to doubt whether the mixed sample of countries will give rise to different test results. In addition, Taylor and Sarno (1998) and Karlson and Lothgren (2000) have suggested that the rejection of the null hypothesis of joint non-stationarity using panel unit-root tests may be due to as few as one of the crosssectional units being stationary. We are able to reject the unit-root null hypothesis of the two variables for 44 out of 109 countries, representing 40% of the countries in the sample. For 44 countries with I(0) series, we therefore apply the dynamic panel generalized method of moments (DPM) technique of Arellano and Bond (1991) and Arellano and Bover (1995) to obtain efficient estimators of the relationship between GDP and CO2 emissions. The generalized method of moments (GMM) model resolves the possible simultaneity between the level of income and pollutants, and concentrates on the causal effect of the exogenous component of income and CO2 emissions. Table 8 shows the estimation results for the dynamic panel regression and the Sargan test, which indicates that the null hypothesis of valid over-identifying restrictions cannot be rejected. The instrumental variables estimated by the GMM are satisfactory. When the dependent variable is CO2 emissions in Table 8, the panel parameter is around 0.2 which is significant at the 5% level. By making comparisons with Table 7, we discover that the impact of GDP is that it influences CO2 emissions more significantly in our estimated results of the panel cointegration.

The panel SURADF is much more efficient as it accounts for the contemporaneous cross-correlation information obtained from the SUR estimates. Indeed, Breuer et al. (2001, 2002) show that the panel SURADF has anywhere from double to triple the power of the ADF test in rejecting a false null hypothesis. Therefore, this paper adopts the series-specific panel unit-root test of Breuer et al. (2001, 2002) to test 109 countries’ CO2 emissions and real GDP within seven regional panel sets covering 1971–2003. More specifically, this paper contributes to the debate regarding the validity of the empirical basis of GDP and CO2 emissions. Previous studies have concluded that GDP and CO2 emissions are best characterized as a unit root, thus supporting the use of a panel cointegration framework for the estimation of the long-run relationship. However, by using the panel SURADF test, we can categorize the behavior of GDP and CO2 emissions and then perform advanced analysis using the cointegration test or dynamic panel generalized method. We emphasize again that the low power of the traditional panel unit-root test similar to the IPS test cannot ensure that all the series are I(0) since it rejects the null in the test results. Thus it is worthwhile adopting the panel SURADF test while we attempt to conduct detailed investigations into the relationships between variables, regardless of whether they are cointegrated or casual relationships. Our results illustrate that the per capita CO2 emissions and real GDP per capita in the sample countries are a mixture of I(0) and I(1) processes and that the generally used panel root tests could lead to misleading inferences. The results of our analysis carry important and far-reaching implications for the statistical modeling of the EKC and more generally for any form of relationship between income and emission levels. Since the differences in the order of integration in both variables for North America, Europe & Central Asia, Latin America and the Caribbean, and Sub-Saharan Africa call into question the validity of panel cointegration techniques that assume that both variables are non-stationary and cointegrated with each other, cointegration techniques are not appropriate either for the cases of South Asia, East Asia and the Pacific, and the Middle East and North Africa, because they are characterized by per capita GDP and CO2 emissions being stationary. For the cases where the series exhibit a unit root, it would be appropriate to first difference the data in order to remove the stochastic trend. We also nearly fail to reject the null of non-stationarity for CO2 emissions and GDP per capita series in South Asia, East Asia and the Pacific, and the Middle East and North Africa. This implies that a stabilization policy would have some permanent effects. When CO2 emissions temporarily deviate from the trend path, the administrative policy of the government should adopt necessary targets. At such a time, policy actions are required to return CO2 emissions and GDP to their trend path.

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Similar conclusions are reached by Romero-A´vila (2008) and Mu¨ller-Fu¨rstenberger and Wagner (2007). Their analyses also call into question the validity of applying panel cointegration techniques to investigate the existence of a long-run relationship between income and emissions for different country groups. Arguably, the fragility and lack of meaningful results surrounding the estimation of EKCs—and more generally for any form of a long-run relationship between income and emission levels—are likely to stem from a failure to properly characterize the time series properties of both variables, due to neglecting crosssectional dependence. The clear cointegration relationships are supported in the countries with I(1) series, so that long-run relationships are created between GDP and CO2 emissions, and the coefficients range from 0.6 to 0.9 when we consider the impact of GDP to CO2 emissions. However, an interesting finding is offered when we use dynamic panel GMM estimation in countries with I(0), for which influences as low as 0.2 from GDP to CO2 emissions can be found. A unit root in real output is tantamount to shocks having a permanent effect. This may be an upshot of real factors, such as technology shocks, contributions to economic fluctuations, or aggregate demand shocks having a permanent effect on real output levels (Narayan, 2004). The degree of integration among emissions and GDP levels does not coincide for the seven panels. When the impact exhibits temporary (I(0) properties), it might be possible to forecast future movements in the series based on past behavior. Governments should thus pay attention to long-run trends in the series and should not adopt excessive targets or interfere with them in the short run. We also can consider that once the stationarity can enable each government to estimate the EKC or the relationship between CO2 and emissions, this will then result in higher accuracy and will not yield spurious regressions. Thus, proper characterization of the unit-root properties of GDP and CO2 emissions are essential in econometric modeling. For instance, in testing for Granger causality or estimating EKCs between GDP and CO2 emissions, a precondition is that both variables need to be integrated of order one. Since CO2 emissions exhibit an I(1) series in some selected sample countries, from another point of view, some of the energyuse policies need to be significantly improved, including those related to the governmental regulatory inefficiency, heterogeneous preferences among consumers, and relative prices of energy being non-stationary. For the I(0) variable of income, our study implies that a fiscal and/or monetary stabilization policy could not possibly affect the real GDP per capita of the selected 44 countries that we investigate here. The stationary series suggest that the stabilization or regulatory policies of emissions may not be overimplemented for the 44 countries selected. If the data are erroneously treated as non-stationary for the 44 countries selected and the causality tests for real GDP per capita (or per capita CO2 emissions) and macroeconomics are applied to the first difference, then a spurious causality will result. The same holds for the 54 countries where income and emissions are best described as non-stationary. However, income and emissions in the 44 countries selected are described as stationary. Thus, our analysis calls into question the validity of applying panel cointegration techniques to examine the existence of long-run cointegation between GDP and emissions. Knowledge of the series properties of CO2 emissions is important for econometric analysis. Labson and Crompton (1993) and Ahrens and Sharma (1997) argued that traditional regression analysis and hypothesis testing cannot be correctly undertaken without first understanding the characteristics of the time series, otherwise the results obtained from estimating the regression models could be rendered invalid. Moreover, given that good policy-making typically depends on sound economic fore-

casts, appropriately modeling the nature of the series can be invaluable to forecasters. Our results proffer the above policy implications. Finally, future studies could extend this research by allowing for both endogenous breaks as well as for cross-sectional correlation. Acknowledgments We would like to thank the Editor, two anonymous referees, and Dr. Chun-Ping Chang for their highly constructive comments. The authors are grateful to Professor Breuer and Professor McNown for kindly making available the RATS program codes used in this paper. Chien-Chiang Lee is grateful to the National Science Council of Taiwan for financial support through Grant NSC 96-2415-H-005-003-MY2. This work is supported in part by the Ministry of Education, Taiwan, ROC under the ATU plan. References Abras, A.L.G., Borges, B.L., Sekkel, R.M., 2004. Breaking trend, Lagrange multiplier test statistic and the presence of a unit root in the Brazilian gross domestic product. Applied Economics Letters 11, 361–364. Ahrens, W., Sharma, V., 1997. Trends in natural resource commodity prices: deterministic or stochastic? Journal of Environmental Economics and Management 16, 184–192. Aksoy, Y., Leon-Ledesma, M.A., 2008. Non-linearities and unit roots in G7 macroeconomic variables. The B.E. Journal of Macroeconomics 8, 1 (Article 5). Aldy, J.E., 2006. Per capita carbon dioxide emissions: convergence or divergence? Environmental and Resource Economics 33, 533–555. Ansuategi, A., 2003. Economic growth and transboundary pollution in Europe: an empirical analysis. Environmental and Resource Economics 26, 305–328. Arellano, M., Bond, S., 1991. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies 58, 277–297. Arellano, M., Bover, O., 1995. Another look at the instrumental variable estimation of error-components models. Journal of Econometrics 68, 29–51. Breuer, J.B., McNown, R., Wallace, M.S., 2001. Misleading inferences from panel unit-root tests with an illustration from purchasing power parity. Review of International Economics 9, 482–493. Breuer, J.B., McNown, R., Wallace, M.S., 2002. Series-specific unit root tests with panel data. Oxford Bulletin of Economics and Statistics 64, 527–546. Bru¨ggemann, R., Trenkler, C., 2007. Are Eastern European countries catching up? Time series evidence for the Czech Republic, Hungary and Poland. Applied Economics Letters 14, 245–249. Camarero, M., Tamarit, C., 2004. Hysteresis vs. natural rate of unemployment: new evidence for OECD countries. Economics Letters 84, 413–417. Campbell, J.Y., Perron, P., 1991. Pitfalls and opportunities: what macroeconomists should know about unit roots. NBER Macroeconomics Annual 6, 141–201. Carrion-i-Silvestre, J.L., Del Barrio-Castro, T., Lopez-Bazo, E., 2005. Breaking the panels: an application to the GDP per capita. Econometrics Journal 8, 159–175. Chang, C.P., Lee, C.C., 2008. Are per capita carbon dioxide emissions converging among industrialized countries? New time series evidence with structural breaks. Environment and Development Economics 13 (4), 497–515. Chang, T.Y., Chang, H.L., Chu, H.P., Su, C.W., 2006. Is per capita real GDP stationary in African countries? Evidence from panel SURADF tests. Applied Economics Letters 13, 1003–1008. Chiu, R.L., 2002. Testing the purchasing power parity in panel data. International Review of Economics and Finance 11, 349–362. Coondoo, D., Dinda, S., 2002. Causality between income and emission: a country group-specific econometric analysis. Ecological Economics 40, 351–367. DeJong, D.N., Nankervis, J.C., Savin, N.E., Whiteman, C.H., 1992. The power problems of unit root tests in time series with autoregressive errors. Journal of Econometrics 53, 323–343. Dickey, D., Fuller, W., 1979. Distribution of the estimators for autoregressive time series with a unit root. Journal of the American Statistical Association 74, 427–431. Dinda, S., Coondoo, D., 2006. Income and emission: a panel data-based cointegration analysis. Ecological Economics 57, 167–181. Ekins, P., 1997. The Kuznets curve for the environment and economic growth: examining the evidence. Environment and Planning A 29, 805–830. Energy Information Administration, 2004. International Energy Annual. Elliott, G., Rothenberg, T.J., Stock, J.H., 1996. Efficient tests for an autoregressive unit root. Econometrica 64, 813–836. Focacci, A., 2005. Empirical analysis of the environmental and energy policies in some developing countries using widely employed macroeconomic indicators: the cases of Brazil, China and India. Energy Policy 33, 543–554. Friedl, B., Getzner, M., 2003. Determinants of CO2 emissions in a small open economy. Ecological Economics 45, 133–148. Galva˜o Jr., A.F., Gomes, F.A., 2007. Convergence or divergence in Latin America? A time series analysis. Applied Economics 39, 1353–1360.

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