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Convergence behavior of carbon dioxide emissions in China
Economic Modelling 43 (2014) 75–80
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Economic Modelling journal homepage: www.elsevier.com/locate/ecmod
Convergence behavior of carbon dioxide emissions in China Yiming Wang a, Pei Zhang a, Dake Huang b,⁎, Changda Cai a a b
Department of Public Economics, School of Economics, Xiamen University, China Institute for Quantitative Economics, Huaqiao University, China
a r t i c l e
i n f o
Article history: Accepted 30 July 2014 Available online xxxx Keywords: Carbon dioxide emissions Club convergence Log t test China
a b s t r a c t In view of global warming, the concept of a low carbon world economy has been brought to center stage. In this paper, a systematical empirical investigation of the convergence behavior of carbon dioxide emissions in China is conducted based on provincial data for the period of 1995–2011. Using the log t test developed by Phillips and Sul (2007), evident divergence at the country level and convergence to three steady state equilibriums at provincial level was identified. Furthermore, estimates from the ordered logit model uncover important determinants underlying the formation of clubs, including the per capita GDP, energy consumption structure, energy intensity, and initial levels of economic development. The results from this study contribute to a more in-depth understanding of the carbon dioxide emissions status quo in China and serves as reference when launching regionbased emissions mitigation policies. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Over the past century, the average surface temperature of the earth has increased by 0.74%, according to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report released in 2007.1 The report further points out that the use of fossil fuels and changes in land utilization are to blame for the worldwide rising concentrations of carbon dioxide. Given the immense detrimental effects of human activities on the natural environment, some global warming scientists advocate imposing necessary control over the emissions of greenhouse gases to avoid further climate changes (Boyd et al., 1995; Nordhaus, 1994). Carbon dioxide emissions are frequently subject to criticism in relation to their link to global warming. Some researchers have been motivated to uncover the relationship between economic development and greenhouse gas pollution (Wang et al., 2005; Zhuang, 2008). In particular, an inverted U shaped relationship between environmental pollution and economic development, known as the Environmental Kuznets Curve (EKC), has been observed (Dijkgraaf and Vollebergh, 2005; Dinda, 2004; Grossman and Krueger, 1996). The idea underlying the EKC is to leave the environmental degradation problems to the market, since they tend to be eradicated as the economy develops. Refuting
⁎ Corresponding author at: Institute for Quantitative Economics, Huaqiao University, Jimei Road No. 668, Xiamen, Fujian 361021, China. Tel.: +86 13860491888. E-mail address: [email protected] (D. Huang). 1 Available at http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_ assessment_report_synthesis_report.htm.
http://dx.doi.org/10.1016/j.econmod.2014.07.040 0264-9993/© 2014 Elsevier B.V. All rights reserved.
the idea of the EKC relationship, Moomaw and Unruh (1997) discovered an N shaped curve, pointing out that controlling carbon dioxide emissions at the expense of economic development seems irrational, because oil shocks alone are enough for reducing emissions. Recently, there have been growing concerns over the relationship between carbon dioxide emissions and energy efficiencies (Kim and Worrell, 2002; Persson et al., 2007). Given that fossil fuels constitute the primary source of greenhouse gases, historical levels of carbon dioxide emissions are dominated by the economic structure and efficiency of energy utilization. More specifically, energy efficiencies are investigated by testing the relative convergence between energy consumption and output levels. Persson et al. (2007) found evidence of converging carbon dioxide emissions among industries in twelve countries and concluded that higher energy efficiencies are accompanied by higher energy prices. The case of China has received much attention from current researchers and government authorities (Du et al., 2012; Zhuang, 2008). Du et al. (2012) applies static and dynamic panel models, but fails to detect the existence of EKC. Predictions are made that carbon dioxide emissions on an aggregate and per capita basis will keep increasing in China. Thus, there is substantial potentiality for carbon dioxide emissions. In response to this issue, actions have been taken by Chinese governments. For instance, the State Council has released The Twelfth Five-Year Work Plan for Controlling Greenhouse Gas Emissions, which requires a 17% cut in emissions by 2015 on the 2010 basis and a 40% reduction by 2020 on the 2005 basis. Combating carbon dioxide emissions entails an in-depth understanding of the long term trends and corresponding determinants. However, prior literature presents mixed results regarding convergence
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Y. Wang et al. / Economic Modelling 43 (2014) 75–80
behavior.2 The ambiguous and even conflicting empirical evidences may be attributed to the noteworthy variations in the methodologies applied, including non-parametric convergence tests (Ezcurra, 2007; McKibbin and Stegman, 2005; Van, 2005), the Bayesian shrinkage estimation (Jobert et al., 2010), and augmented Solow growth models (Brock and Taylor, 2004). This paper differs from the previous studies in two primary aspects. The first difference is that this study uses a panel estimation technique recently developed by Phillips and Sul (2007, 2009). The advantage of this approach is that it takes into account the heterogeneity of the Chinese regions in a non-linear time-varying framework. It outperforms other methods used in the relevant literature for an economy in transition, such as China, that has undergone a significant transformation over the period under consideration (Herrerias and Ordoñez, 2012). A relevant application of this method to cross-country panel data can be found in Panopoulou and Pantelidis (2009). The second difference is that this study adopts a carbon intensity based on specific considerations instead of measuring carbon dioxide emissions on per capita basis (Panopoulou and Pantelidis, 2009; Romero-Ávila, 2008; Strazicich and List, 2003). Currently in China, economic development comes first. Given the close relationship between GDP growth and carbon dioxide emissions, a reduction in the absolute term usually does not emerge; instead, a decrease in relative terms emerges (Zhuang, 2008). More importantly, the reduction targets are based on the carbon intensity in China.3 To our best knowledge, this practice remains rarely explored in studies related to China. The remainder of the paper is organized as follows. Section 2 describes the data and establishes the non-linear time-varying framework by introducing the log t test and clustering algorithm. Section 3 presents the empirical results. Section 4 concludes. 2. Empirical model and estimation methodology 2.1. Estimation methodology: the log t tests In this section, a regression-based convergence test, developed by Phillips and Sul (2007), referred to as the log t test,4 is presented. The novel aspect of this approach is that it does not rely on any particular assumption and solves the issue of unit roots and cointegration when dealing with convergence in a time series panel framework (Ghosh et al., 2013). It begins with a parsimonious factor model defined as: X i;t ¼ δi;t μ t
ð1Þ
where i = 1, …, N and t = 1, …, T. Xi,t is the log value of the target variable, the carbon intensity. δi,t is an idiosyncratic element that measures the distance between Xi,t and the common factor μ t. A semi-parametric form of δi,t is given by: −1 −α
δi;t ¼ δi þ σ i ζ i;t Lðt Þ
t
ð2Þ
where δi is fixed, ζi,t is iid(0, 1) across i with weak serial dependence, and L(t) is a slow varying function of time, such as log(t). When
2 Strazicich and List (2003) examined the carbon dioxide emissions among twenty-one OECD countries and confirmed β convergence. However, they refuted the existence of stochastic convergence, in stark contrast to Aldy (2006). Romero-Ávila (2008) presumed structural breaks and found evident stochastic and deterministic convergence. Westerlund and Basher (2007) employed a factor model to account for cross-sectional dependence. The rejected null hypothesis under the panel unit root-test confirms the converging emissions among 28 countries. 3 See the official central government website: http://www.gov.cn/jrzg/2009-12/06/ content_1481261.htm (in Chinese). 4 For a more in-depth understanding of this method, please see Phillips and Sul (2007, 2009).
t → ∞, L(t) → ∞. Thus, the function L(t) guarantees that convergence still holds in the case of α = 0. In principle, the null hypothesis of convergence will be accepted if δi,t → δi for all α ≥ 0. To test the convergence in the panel data, a relative transition parameter hi,t is defined as: hi;t ¼
1 N
X i;t XN
X i¼1 i;t
¼
1 N
δi;t XN
δ i¼1 i;t
:
ð3Þ
This transition parameter measures the loading coefficient δi,t in relation to its panel average at time point t. A clear understanding of hi,t will make it much easier to grasp the following characteristics: (1) the cross-sectional mean of hi,t equaling one; and (2) if δi,t converges to δ, then hi,t converges to 1 and the cross-sectional variance, denoted as Ht, converges to zero. That is, if t → ∞, then: Ht ¼
1 N
2 XN hi;t −1 →0: i¼1
ð4Þ
As a crucial step, Phillips and Sul (2007) constructed the crosssectional variance ratio H1/Ht to establish the log t regression model, used to test the presence of club convergence: log
H1 ^ logt þ u ^t −2 logLðt Þ ¼ ^c þ b Ht
ð5Þ
where t = [rT], [rT] + 1, …, T, r N 0. A value of r = 0.3 is suggested by Phillips and Sul (2009), based on Monte Carlo simulations. More specif^ ¼ 2a ^. a ^ is an estimate of α in Eq. (2). ically, L(t) = log(t + 1) and b Based on the results, the null hypothesis is accepted when the autocorrelation heteroskedasticity robust one-tail t statistic is above the critical value −1.65 at the 5% significance level. Otherwise, it is rejected. The novel aspect of this approach is that convergence patterns within groups can be examined using log t regressions. The dynamics of carbon emissions are driven by both common and idiosyncratic factors. The log t test differs from other methods in that it enables us to distinguish the effect of common factors from that of idiosyncratic factors. In particular, the log t test establishes a variable to measure the difference between the cross-sections and the crosssectional mean, using it to describe the varying convergence behavior of the cross-sections. Compared with other traditional methods, the log t test is preferred in this particular situation because: 1) it can be viewed as a conditional σ convergence test that controls for the common factors; and 2) it can be exercised repeatedly to gather together all of the individuals that share the same convergence trend into one club, without exogenously assuming any convergence pattern in advance; traditional methods can only examine the panel convergence behavior. As such, the log t test is the most appropriate test to apply in this study. 2.2. Club convergence: a clustering algorithm As for the process to identify each jurisdiction's membership in a specific club, Phillips and Sul (2009) suggest a four-step clustering procedure depicted as follows: • Step one (cross-section ordering) Order the panels according to the values in the final period of each province. It could also be done based on a time series average of the final observations. • Step two (core group selection) Identify k individuals with the largest values to formulate a core group Gk, N N k ≥ 2. Next, compute the t-statistic of sequential log t tests for Gk, and choose the core group size k∗ according to the following criterion:
k ¼ arg maxk ft k g
subject to
minft k gNc
ð6Þ
Y. Wang et al. / Economic Modelling 43 (2014) 75–80
where c∗ can be set to the asymptotic 5% critical value of − 1.65, as in this case, or zero in extreme conservative testing. A min{tk} N −1.65 guarantees that the null hypothesis of convergence holds for each k. k ¼ arg max ft k g minimizes the probability of a Type II error k
and guarantees that Gk is a converging core group. • Step three (screening for membership) Pick one individual outside Gk and add it into Gk to perform the log t test again. If the t-statistic is greater than c∗, then this individual is considered a club member of Gk and added into Gk. Otherwise, it is excluded from Gk. Repeat the same procedure for each of the remaining individuals and arrive at a club that contains all converging individuals. • Step four (recursion and stopping) Perform the log t test for all the rest of the individuals. This clustering procedure stops if the t-statistic is greater than the critical value c∗; as a result, the null hypothesis of convergence cannot be rejected. Otherwise, there is divergence among the rest of the members. In this case, repeat Steps one through three again for the remaining individuals until the second club is formulated. The recursion shall not stop until all clubs are exhausted. In the case of no core group being identified, the remaining individuals display a divergent behavior. The key mechanism of the log t test is that, over time, extremely large deviations from the cross-sectional mean are identified and the corresponding data points are excluded from the group. In this way, as is emphasized in this paper, the log t test examines the relative convergence, rather than the level convergence. 2.3. Data Following Fan et al. (2007), the carbon intensity is defined as the carbon dioxide emissions (in tons) per unit of GDP value (set to be 10,000 RMB). More specifically, we account for the carbon dioxide emissions from eight primary sources, namely coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas. According to a practice suggested by the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and the Kyoto Protocol established in 1997, the total amount of carbon dioxide is the sum of the emissions from these sources, which is defined as:
CO2 ¼
n X i¼1
CO2;i ¼
n X i¼1
Ei NCVi CEFi COFi
44 12
ð7Þ
where CO2 denotes carbon dioxide emissions measured in tons. i = 1, …, n, stands for the categories of energy. Ei denotes the amount of energy consumed. The remaining parameters are all constants: the Net Calorific Value (NCV), Carbon Oxidation Factor (COF), Carbon Emission Factor (CEF), and molecular weight ratio of carbon dioxide over carbon. Each type of energy consumed is converted into a corresponding amount of coal based on standard coal coefficients. These constants and coefficients are all readily available in China Energy Statistical Yearbook 2011. The data sources include various issues of the China Statistical Yearbook and China Energy Statistical Yearbook (issue years include 1995 through 2012), and the China Compendium of Statistics 1949– 2008. GDP data was adjusted by Consumer Price Index (CPI). Tibet (Xizang) is not considered for a severe lack of data; the Chongqing was merged into Sichuan, because it remained attached to Sichuan until 1997. Finally, we have one extra “reference member” added to the panel data, structured as the country average, to determine the position of the country average in the clubs. Overall, there are thirty panels, spanning from 1995 to 2011.
77
3. Empirical results and analysis 3.1. Overall test results An application of the log t test to the null hypothesis of overall convergence generates the following results:
log
H1 −2 logLðt Þ ¼ 0:051−1:059 logt Ht ð4:30Þð−20:89Þ:
ð8Þ
The t-statistic of − 20.89 is far below the critical value of − 1.65, strongly rejecting the null hypothesis. This is not surprising, given the immense inter-regional differences across Chinese provinces (Herrerias and Ordoñez, 2012; Lin et al., 2013). In sharp contrast, Huang and Meng (2013) take spatial dynamics into account and find evidence of overall convergence using per capita carbon dioxide emissions from 1985 to 2008 data across Chinese provinces.
3.2. Club convergence test results The clustering algorithm introduced in Section 2.2 uncovered three convergence clubs.5 To understand the spatial distribution of these groups, the geographical locations of each club is plotted (Fig. 1). A simple glance over the geographical distribution of the clubs unveils the following discoveries. Firstly, clubs seem to be spatially concentrated. This result is similar to Quah's (1996) finding. A possible explanation for this is that the economic activity of bordering regions should influence a given region's economy, and therefore, have an impact on its convergence process. Secondly, the clubs seem to be distributed in sequence from west to east and inland area to coastal regions. This observation echoes Weeks and Yao (2003). Given the conventional partition of China into the eastern, central and western regions,6 it is noteworthy that Clubs 1 and 2 approximate the middle and western areas geographically, while Club 3 primarily corresponds to the more developed eastern region. Finally, the spatial agglomeration pattern of the clubs seems to contradict the distribution of the economic power across regions. According to the calculations based on our data, the average carbon intensity of Clubs 1 and 2 is 1.654, while that of Club 3 is 1.638. Overall, the figures seem to challenge our common sense by illustrating that less developed regions contribute more carbon dioxide emissions than their wealthy counterparts. This seemingly paradoxical relationship may be attributed to the fact that both regions are less developed and their GDP sizes are naturally smaller than that of the eastern region. By definition, the average carbon intensity of Clubs 1 and 2 ends up with higher carbon intensity. Table 1 presents the detailed results of the club convergence tests. It is noteworthy that the reference member, country average, is located in Club 1, showing a vast inter-regional imbalance in terms of carbon intensity. The positive t-statistic for each group is significantly higher than − 1.65, thus rendering strong support for the club classification. This is even more pronounced in Club 3. 5 To be specific, the first club includes: Ningxia, Xinjiang, Shǎnxi, and the country average. The second club includes Guangdong, Sichuan, Guizhou, Yunnan, Gansu, and Qinghai. The third club includes Beijing, Tianjin, Hebei, Shānxi, Neimenggu, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangxi, and Hainan. 6 Following Bian and Yang (2010), the eastern region includes Beijing, Tianjin, Shanghai, Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan. The middle region includes Heilongjiang, Jilin, Inner Mongolia, Henan, Shanxi, Anhui, Hubei, Hunan, Jiangxi, and Guangxi. The western region includes Gansu, Guizhou, Ningxia, Qinghai, Shaanxi, Tibet, Yunnan, Xinjiang, Sichuan, and Chongqing.
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Club1 Club2 Club3 N.A.
Fig. 1. Geographical distribution of converging clubs in China.
3.3. Robustness check: tests of club merging Fig. 2 displays the relative transitory paths for the detected clubs to provide a visual perception of the significance of the convergence behavior. As shown below, Club 3 stays around the mean value of 1, while Clubs 1 and 2 seem to diverge in opposite directions during the sample period. The discrepancy between them remains ambiguous, and thus, necessitates further examination. To assess the robustness of our club classification more accurately, in this section, tests of club merging are conducted. Log t tests are employed to determine if neighboring clubs can merge into one group. More specifically, examinations of whether the λ1 fraction of the members with lower carbon intensity in the upper club and the λ2 fraction of the members with higher carbon intensity in the lower club converge are conducted. Consequently, if the t-statistic is greater than −1.65, then the adjacent clubs should be merged together. Phillips and Sul (2009) suggest setting λ1 = λ2 = 0.5. The test results are summarized in Table 2. As is illustrated, both t-statistic values for club merging tests, − 27.22 and − 16.88, are significantly smaller than − 1.65, strongly rejecting the null hypothesis of convergence. Hence, the tests of club merging confirm our initial classification of the three convergence clubs. 3.4. Analysis of determinants: ordered logit model In this sector, we explore the determinants underlying the formation of clubs by screening through important determinants commonly
Table 1 Convergence test results. Category Club 1 Club 2 Club 3
Number of provinces a
3+1 6 20
^ b
t-Statistic
0.189 0.098 3.718
1.129 0.869 4.054
Note: a Includes one extra “reference member”, the country average.
suggested in current works and using the ordinal club ranking as the dependent variable. This is conducted by applying the ordered logit model introduced by McKelvey and Zavoina (1975). According to club theory, convergence to a common steady state only emerges when there are similar structures or characteristics, including: • Initial conditions Prior studies have shown that initial conditions play a crucial role in determining the transitory path of an economy (Azariadis and Drazen, 1990; Shaver et al., 2000). Following conventional practice (Auffhammer and Carson, 2008; Ezcurra, 2007; Lee and Lee, 2009), per capita GDP and energy resource endowments in the initial period are employed as the conditioning variables for two reasons: 1) the close relationship between economic development and carbon emissions attracted a significant amount of attention (Moomaw and Unruh, 1997; Roberts and Grimes, 1997) after Grossman and Krueger (1991) proposed the Environment Kuznets Curve and proved it to be significant; and 2) many researchers, like Friedrichs and Inderwildi (2013), confirmed that important interactions between initial energy resource endowments and carbon emissions exist from four aspects, based on the Resource Curse Theory proposed by Auty (1993). • Structural characteristics Holding initial conditions constant, the structural characteristics dominate the dynamics of economic development. A large body literature has been motivated to unearth the important deriving forces (Houghton, 2007; Jobert et al., 2010; Wang et al., 2005; Zhuang, 2008), such as the size of the secondary industry, public investments, energy intensity, and energy consumption structure. These are also included in our explanatory variable list for two reasons: 1) according to Grossman and Krueger (1994), the economic structure is one of the three principal factors affecting the environment; the economic structure can be depicted by the size of the secondary industry. The energy intensity and energy consumption structure are also frequently used as important determinants of carbon emissions; 2) our club convergence results illustrate an obvious spatially correlated pattern; this pattern seems to coincide with the distribution of local economic development and further suggests that infrastructure construction may perform well
Y. Wang et al. / Economic Modelling 43 (2014) 75–80
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Fig. 2. Relative transitory paths of convergence clubs.
Table 2 Convergence club classification. Initial classification ^ (SE of b) ^ b Club 1 [3 + 1]a Club 2 Club 3
0.189 (0.167) 0.098 (0.113) 3.718 (0.917)
Tests of club merging ^ (SE of b) ^ b
Final classification ^ (SE of b) ^ b
Club 1 + 2 −1.580⁎ (0.058)
Club 1 Club 2 + 3 −0.989⁎ (0.047)
Club 2 Club 3
0.189 (0.167) 0.098 (0.113) 3.718 (0.917)
Notes: ⁎ Reject the null hypothesis of growth convergence at the 5% level. The numbers in brackets stand for the number of provinces in a group. a Club 1 includes the country average.
in explaining the grouping results. In view of this, we used public investments to capture the effects of infrastructure construction. All indicators used are fairly standard. Energy resource endowments are measured by per capita fossil energy, including coal, oil, and natural gas. Energy intensity is measured by the amount of energy consumed for each dollar of GDP. The energy consumption structure is depicted by the weight of coal consumption in overall energy consumption. For a robustness consideration, public investments are measured in two ways: (1) highway mileage within a province as a percentage of its land area, and (2) fiscal expenditures as a percentage of GDP. All data was obtained from the CEIC database. To begin with, the empirical model takes the form:
yi ¼ X i β þ εi
ð9Þ
where the dependent variable y∗i takes on ordinal values from 1 to 3; Xi is the explanatory variable set and i = 1, …, 30 indicates the provinces. The column vector β corresponds to the regression coefficients. Once the ordered logit regression is employed, the coefficient in a logit regression is defined as the log value of the odds ratio.7 A positive coefficient suggests that greater values help explain the membership in a specific club. In contrast, a negative coefficient suggests that lower values help identify the membership in that club. According to Table 3, the initial conditions, energy intensity, energy consumption structure, and public investment indicators are significant to varying degrees, while the industry structure seems irrelevant. To be more specific, the positive signs on per capita GDP, the energy resource endowments, and the energy consumption structure indicate that larger values help to explain the formation of the club classification. This coincides with the pattern of the spatial distribution of the clubs, in that the eastern provinces with higher values are more stable in Club 3 than the mid-to-western provinces that are classified into 7 It measures the effect of a single variable on the odds of membership in a specific club. See Introduction to SAS. UCLA: Statistical Consulting Group. from http://www.ats.ucla. edu/stat/mult_pkg/faq/general/odds_ratio.htm.
Clubs 1 and 2. Intuitively, the eastern provinces with a higher per capita GDP, energy resource endowments, and energy consumption structure are more developed and can afford to consider the quality of economic growth, as compared with the central-to-western provinces. However, the public investment variables, including highway mileage within a province as a percentage of its land area and fiscal expenditures as a percentage of GDP, bear negative signs. That is, smaller values consolidate the grouping results. Again, this concurs with our observation in that the highway mileage within a province as a percentage of its land area is 24% for the eastern provinces, and 30% and 46% for the central and western provinces, respectively. Fiscal expenditures as a percentage of GDP are 28% for the eastern provinces, 40% for the central provinces, and 32% for the western provinces. Consequently, the eastern region seems to be converging more than the central-to-western region.
4. Concluding remarks Pollution from greenhouse gases has attracted considerable attention from many researchers and governmental officers. In most studies, per capita carbon dioxide emissions are used (Huang and Meng, 2013; Persson et al., 2007; Zhuang, 2008). However, there remains much to be explored about China's carbon dioxide emissions. In studying the carbon dioxide emissions of China, this paper differs from the previous literature in two aspects. First, carbon intensity is used to measure emissions instead of per capita basis. This conforms to the fact that GDP growth takes precedence in China. As a result, reducing emissions is achieved more on relative basis than in absolute terms. As such, the reduction targets are set based on the carbon intensity in China. Therefore, using the carbon intensity is of a more practical sense. Next, we apply the log t tests and clustering algorithm developed by Phillips and Sul (2007, 2009) to detect club convergence. This method allows for inter-regional heterogeneity within a time-varying framework. Moreover, this not only indicates a divergence or convergence, but also that the club convergence converges to a different steady state equilibrium.
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Table 3 Ordered logit regression results. Variable
Coefficient
z-Statistics
Variable
Coefficient
z-Statistics
Per capita GDP Per capita endowment Energy structurea Energy intensity
0.000018⁎⁎ 0.256687⁎ 1.77241⁎⁎⁎ −0.253157⁎⁎
2.07 1.52 4.61 −2.11
Industrial structure Highway mileage Fiscal expenditure
−1.951098 −14.39167⁎⁎⁎ −0.499966⁎⁎⁎
−1.2 −4.26 −3.3
Note: ⁎⁎⁎ Significant at the 1% level. ⁎⁎ Significant at the 5% level. ⁎ Significant at the 15% level. a Here, energy structure is short for the energy consumption structure.
The empirical results based on provincial data spanning from 1995 to 2011 provide strong evidence of a divergence of the country as a whole and further suggests three convergence clubs in terms of carbon intensity. Tests of club merging confirm the groupings. The estimates of the ordered logit regression reveal that the initial economic conditions, energy intensity, energy consumption structure, and public investments play a role in explaining the formation of clubs, while the industry structure remains irrelevant. The policy implications of this paper is relevant in terms of setting regional emissions mitigation policies to fulfill declared objectives, as it provides a clear picture of the carbon dioxide emissions status quo in China. As pointed out by Wang (2013), joint efforts may work better in controlling environmental pollution. Hence, members within the same club may consider potential cooperation opportunities in reducing carbon dioxide emissions. In this way, the overall convergence among provinces in China may be achieved in the long run.
Acknowledgment This research is supported by the National Natural Science Foundation of China (No. 71373217), the Fundamental Research Funds for the Central Universities of China (No. 2013221010), Key Projects of National Social Science Fund (No. 14AZD018), and the 2012 Scholarship for Excellent Doctoral Student by Ministry of Education and Academic Degree Commission of the State Council of China.
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Grossman, G.M., Krueger, A.B., 1991. Environmental impacts of a North American Free Trade Agreement. National Bureau of Economic Research Working Paper 3914. NBER, Cambridge (MA). Grossman, Gene M., Krueger, Alan B., 1994. Economic growth and the environment. NBER Working Papers, 4634. Grossman, Gene M., Krueger, Alan B., 1996. The inverted-U: what does it mean? Environ. Dev. Econ. 1 (1), 119–122. Herrerias, M.J., Ordoñez, J., 2012. New evidence on the role of regional clusters and convergence in China (1952–2008). China Econ. Rev. 23 (4), 1120–1133. Houghton, David D., 2007. Global Climate Change Atmospheric & Oceanic Sciences. Huang, Bo, Meng, Lina, 2013. Convergence of per capita carbon dioxide emissions in urban China: a spatio-temporal perspective. Appl. Geogr. 40, 21–29. Jobert, Thomas, Karanfil, Fatih, Tykhonenko, Anna, 2010. Convergence of per capita carbon dioxide emissions in the EU: legend or reality? Energy Econ. 32 (6), 1364–1373. Kim, Yeonbae, Worrell, Ernst, 2002. International comparison of CO2 emission trends in the iron and steel industry. Energy Policy 30 (10), 827–838. Lee, Chien-Chiang, Lee, Jun-De, 2009. Income and CO2 emissions: evidence from panel unit root and cointegration tests. Energy Policy 37 (2), 413–423. Lin, Pei-Chien, Lin, Chun-Hung, Ho, I. Ling, 2013. Regional convergence or divergence in China? Evidence from unit root tests with breaks. Ann. Reg. Sci. 50 (1), 223–243. McKelvey, Richard D., Zavoina, William, 1975. A statistical model for the analysis of ordinal level dependent variables. J. Math. Sociol. 4 (1), 103–120. McKibbin, Warwick J., Stegman, Wlison, 2005. Convergence and per capita carbon emission. CAMA Working Paper Series 4. Moomaw, William R., Unruh, Gregory C., 1997. Are environmental Kuznets curves misleading us? Environ. Dev. 2 (4). Nordhaus, Willian D., 1994. Locational competition and the environment: should countries harmonize their environmental policies? 1079. Cowles Foundation for Research in Economics, pp. 261–287. Panopoulou, Ekaterini, Pantelidis, Theologos, 2009. Club convergence in carbon dioxide emissions. Environ. Resour. Econ. 44 (1), 47–70. Persson, Tobias A., Colpier, Ulrika Claeson, Azar, Christian, 2007. Adoption of carbon dioxide efficient technologies and practices: an analysis of sector-specific convergence trends among 12 nations. Energy Policy 35 (5), 2869–2878. Phillips, Peter C.B., Sul, Donggyu, 2007. Transition modeling and econometric convergence tests. Econometrica 75 (6), 1771–1855. Phillips, Peter C.B., Sul, Donggyu, 2009. Economic transition and growth. J. Appl. Econ. 24 (7), 1153–1185. Quah, Danny T., 1996. Empirics for economic growth and convergence. Eur. Econ. Rev. 40 (6), 1353–1375. Roberts, J. Timmons, Grimes, Peter E., 1997. Carbon intensity and economic development 1962–1991: a brief exploration of the environmental Kuznets curve. World Dev. 25 (2), 191–198. Romero-Ávila, Diego, 2008. Convergence in carbon dioxide emissions among industrialised countries revisited. Energy Econ. 30 (5), 2265–2282. Shaver, Gaius R., Canadell III, Josep, Chapin, F.S., Gurevitch, Jessica, Harte, John, Henry, Greg, Rustad, Lindsey, 2000. Global warming and terrestrial ecosystems: a conceptual framework for analysis. Bioscience 50 (10), 871–882. Strazicich, Mark C., List, John A., 2003. Are CO2 emission levels converging among industrial countries? Environ. Resour. Econ. 24 (3), 263–271. Van, Phu Nguyen, 2005. Distribution dynamics of CO2 emissions. Environ. Resour. Econ. 32 (4), 495–508. Wang, Yiming, 2013. A time-consistent model for cooperation in international pollution control. Econ. Model. 33, 500–506. Wang, Can, Chen, Jining, Zou, Ji, 2005. Decomposition of energy-related CO2 emission in China: 1957–2000. Energy 30 (1), 73–83. Weeks, Melvyn, Yao, James Yudong, 2003. Provincial conditional income convergence in China, 1953–1997: a panel data approach. Econ. Rev. 22 (1), 59–77. Westerlund, Joakim, Basher, Syed A., 2007. Testing for convergence in carbon dioxide emissions using a century of panel data. Environ. Resour. Econ. 40 (1), 109–120. Zhuang, Guiyang, 2008. How will China move towards becoming a low carbon economy? China World Economy 16 (3), 93–105.
Contents lists available at ScienceDirect
Economic Modelling journal homepage: www.elsevier.com/locate/ecmod
Convergence behavior of carbon dioxide emissions in China Yiming Wang a, Pei Zhang a, Dake Huang b,⁎, Changda Cai a a b
Department of Public Economics, School of Economics, Xiamen University, China Institute for Quantitative Economics, Huaqiao University, China
a r t i c l e
i n f o
Article history: Accepted 30 July 2014 Available online xxxx Keywords: Carbon dioxide emissions Club convergence Log t test China
a b s t r a c t In view of global warming, the concept of a low carbon world economy has been brought to center stage. In this paper, a systematical empirical investigation of the convergence behavior of carbon dioxide emissions in China is conducted based on provincial data for the period of 1995–2011. Using the log t test developed by Phillips and Sul (2007), evident divergence at the country level and convergence to three steady state equilibriums at provincial level was identified. Furthermore, estimates from the ordered logit model uncover important determinants underlying the formation of clubs, including the per capita GDP, energy consumption structure, energy intensity, and initial levels of economic development. The results from this study contribute to a more in-depth understanding of the carbon dioxide emissions status quo in China and serves as reference when launching regionbased emissions mitigation policies. © 2014 Elsevier B.V. All rights reserved.
1. Introduction Over the past century, the average surface temperature of the earth has increased by 0.74%, according to the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report released in 2007.1 The report further points out that the use of fossil fuels and changes in land utilization are to blame for the worldwide rising concentrations of carbon dioxide. Given the immense detrimental effects of human activities on the natural environment, some global warming scientists advocate imposing necessary control over the emissions of greenhouse gases to avoid further climate changes (Boyd et al., 1995; Nordhaus, 1994). Carbon dioxide emissions are frequently subject to criticism in relation to their link to global warming. Some researchers have been motivated to uncover the relationship between economic development and greenhouse gas pollution (Wang et al., 2005; Zhuang, 2008). In particular, an inverted U shaped relationship between environmental pollution and economic development, known as the Environmental Kuznets Curve (EKC), has been observed (Dijkgraaf and Vollebergh, 2005; Dinda, 2004; Grossman and Krueger, 1996). The idea underlying the EKC is to leave the environmental degradation problems to the market, since they tend to be eradicated as the economy develops. Refuting
⁎ Corresponding author at: Institute for Quantitative Economics, Huaqiao University, Jimei Road No. 668, Xiamen, Fujian 361021, China. Tel.: +86 13860491888. E-mail address: [email protected] (D. Huang). 1 Available at http://www.ipcc.ch/publications_and_data/publications_ipcc_fourth_ assessment_report_synthesis_report.htm.
http://dx.doi.org/10.1016/j.econmod.2014.07.040 0264-9993/© 2014 Elsevier B.V. All rights reserved.
the idea of the EKC relationship, Moomaw and Unruh (1997) discovered an N shaped curve, pointing out that controlling carbon dioxide emissions at the expense of economic development seems irrational, because oil shocks alone are enough for reducing emissions. Recently, there have been growing concerns over the relationship between carbon dioxide emissions and energy efficiencies (Kim and Worrell, 2002; Persson et al., 2007). Given that fossil fuels constitute the primary source of greenhouse gases, historical levels of carbon dioxide emissions are dominated by the economic structure and efficiency of energy utilization. More specifically, energy efficiencies are investigated by testing the relative convergence between energy consumption and output levels. Persson et al. (2007) found evidence of converging carbon dioxide emissions among industries in twelve countries and concluded that higher energy efficiencies are accompanied by higher energy prices. The case of China has received much attention from current researchers and government authorities (Du et al., 2012; Zhuang, 2008). Du et al. (2012) applies static and dynamic panel models, but fails to detect the existence of EKC. Predictions are made that carbon dioxide emissions on an aggregate and per capita basis will keep increasing in China. Thus, there is substantial potentiality for carbon dioxide emissions. In response to this issue, actions have been taken by Chinese governments. For instance, the State Council has released The Twelfth Five-Year Work Plan for Controlling Greenhouse Gas Emissions, which requires a 17% cut in emissions by 2015 on the 2010 basis and a 40% reduction by 2020 on the 2005 basis. Combating carbon dioxide emissions entails an in-depth understanding of the long term trends and corresponding determinants. However, prior literature presents mixed results regarding convergence
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Y. Wang et al. / Economic Modelling 43 (2014) 75–80
behavior.2 The ambiguous and even conflicting empirical evidences may be attributed to the noteworthy variations in the methodologies applied, including non-parametric convergence tests (Ezcurra, 2007; McKibbin and Stegman, 2005; Van, 2005), the Bayesian shrinkage estimation (Jobert et al., 2010), and augmented Solow growth models (Brock and Taylor, 2004). This paper differs from the previous studies in two primary aspects. The first difference is that this study uses a panel estimation technique recently developed by Phillips and Sul (2007, 2009). The advantage of this approach is that it takes into account the heterogeneity of the Chinese regions in a non-linear time-varying framework. It outperforms other methods used in the relevant literature for an economy in transition, such as China, that has undergone a significant transformation over the period under consideration (Herrerias and Ordoñez, 2012). A relevant application of this method to cross-country panel data can be found in Panopoulou and Pantelidis (2009). The second difference is that this study adopts a carbon intensity based on specific considerations instead of measuring carbon dioxide emissions on per capita basis (Panopoulou and Pantelidis, 2009; Romero-Ávila, 2008; Strazicich and List, 2003). Currently in China, economic development comes first. Given the close relationship between GDP growth and carbon dioxide emissions, a reduction in the absolute term usually does not emerge; instead, a decrease in relative terms emerges (Zhuang, 2008). More importantly, the reduction targets are based on the carbon intensity in China.3 To our best knowledge, this practice remains rarely explored in studies related to China. The remainder of the paper is organized as follows. Section 2 describes the data and establishes the non-linear time-varying framework by introducing the log t test and clustering algorithm. Section 3 presents the empirical results. Section 4 concludes. 2. Empirical model and estimation methodology 2.1. Estimation methodology: the log t tests In this section, a regression-based convergence test, developed by Phillips and Sul (2007), referred to as the log t test,4 is presented. The novel aspect of this approach is that it does not rely on any particular assumption and solves the issue of unit roots and cointegration when dealing with convergence in a time series panel framework (Ghosh et al., 2013). It begins with a parsimonious factor model defined as: X i;t ¼ δi;t μ t
ð1Þ
where i = 1, …, N and t = 1, …, T. Xi,t is the log value of the target variable, the carbon intensity. δi,t is an idiosyncratic element that measures the distance between Xi,t and the common factor μ t. A semi-parametric form of δi,t is given by: −1 −α
δi;t ¼ δi þ σ i ζ i;t Lðt Þ
t
ð2Þ
where δi is fixed, ζi,t is iid(0, 1) across i with weak serial dependence, and L(t) is a slow varying function of time, such as log(t). When
2 Strazicich and List (2003) examined the carbon dioxide emissions among twenty-one OECD countries and confirmed β convergence. However, they refuted the existence of stochastic convergence, in stark contrast to Aldy (2006). Romero-Ávila (2008) presumed structural breaks and found evident stochastic and deterministic convergence. Westerlund and Basher (2007) employed a factor model to account for cross-sectional dependence. The rejected null hypothesis under the panel unit root-test confirms the converging emissions among 28 countries. 3 See the official central government website: http://www.gov.cn/jrzg/2009-12/06/ content_1481261.htm (in Chinese). 4 For a more in-depth understanding of this method, please see Phillips and Sul (2007, 2009).
t → ∞, L(t) → ∞. Thus, the function L(t) guarantees that convergence still holds in the case of α = 0. In principle, the null hypothesis of convergence will be accepted if δi,t → δi for all α ≥ 0. To test the convergence in the panel data, a relative transition parameter hi,t is defined as: hi;t ¼
1 N
X i;t XN
X i¼1 i;t
¼
1 N
δi;t XN
δ i¼1 i;t
:
ð3Þ
This transition parameter measures the loading coefficient δi,t in relation to its panel average at time point t. A clear understanding of hi,t will make it much easier to grasp the following characteristics: (1) the cross-sectional mean of hi,t equaling one; and (2) if δi,t converges to δ, then hi,t converges to 1 and the cross-sectional variance, denoted as Ht, converges to zero. That is, if t → ∞, then: Ht ¼
1 N
2 XN hi;t −1 →0: i¼1
ð4Þ
As a crucial step, Phillips and Sul (2007) constructed the crosssectional variance ratio H1/Ht to establish the log t regression model, used to test the presence of club convergence: log
H1 ^ logt þ u ^t −2 logLðt Þ ¼ ^c þ b Ht
ð5Þ
where t = [rT], [rT] + 1, …, T, r N 0. A value of r = 0.3 is suggested by Phillips and Sul (2009), based on Monte Carlo simulations. More specif^ ¼ 2a ^. a ^ is an estimate of α in Eq. (2). ically, L(t) = log(t + 1) and b Based on the results, the null hypothesis is accepted when the autocorrelation heteroskedasticity robust one-tail t statistic is above the critical value −1.65 at the 5% significance level. Otherwise, it is rejected. The novel aspect of this approach is that convergence patterns within groups can be examined using log t regressions. The dynamics of carbon emissions are driven by both common and idiosyncratic factors. The log t test differs from other methods in that it enables us to distinguish the effect of common factors from that of idiosyncratic factors. In particular, the log t test establishes a variable to measure the difference between the cross-sections and the crosssectional mean, using it to describe the varying convergence behavior of the cross-sections. Compared with other traditional methods, the log t test is preferred in this particular situation because: 1) it can be viewed as a conditional σ convergence test that controls for the common factors; and 2) it can be exercised repeatedly to gather together all of the individuals that share the same convergence trend into one club, without exogenously assuming any convergence pattern in advance; traditional methods can only examine the panel convergence behavior. As such, the log t test is the most appropriate test to apply in this study. 2.2. Club convergence: a clustering algorithm As for the process to identify each jurisdiction's membership in a specific club, Phillips and Sul (2009) suggest a four-step clustering procedure depicted as follows: • Step one (cross-section ordering) Order the panels according to the values in the final period of each province. It could also be done based on a time series average of the final observations. • Step two (core group selection) Identify k individuals with the largest values to formulate a core group Gk, N N k ≥ 2. Next, compute the t-statistic of sequential log t tests for Gk, and choose the core group size k∗ according to the following criterion:
k ¼ arg maxk ft k g
subject to
minft k gNc
ð6Þ
Y. Wang et al. / Economic Modelling 43 (2014) 75–80
where c∗ can be set to the asymptotic 5% critical value of − 1.65, as in this case, or zero in extreme conservative testing. A min{tk} N −1.65 guarantees that the null hypothesis of convergence holds for each k. k ¼ arg max ft k g minimizes the probability of a Type II error k
and guarantees that Gk is a converging core group. • Step three (screening for membership) Pick one individual outside Gk and add it into Gk to perform the log t test again. If the t-statistic is greater than c∗, then this individual is considered a club member of Gk and added into Gk. Otherwise, it is excluded from Gk. Repeat the same procedure for each of the remaining individuals and arrive at a club that contains all converging individuals. • Step four (recursion and stopping) Perform the log t test for all the rest of the individuals. This clustering procedure stops if the t-statistic is greater than the critical value c∗; as a result, the null hypothesis of convergence cannot be rejected. Otherwise, there is divergence among the rest of the members. In this case, repeat Steps one through three again for the remaining individuals until the second club is formulated. The recursion shall not stop until all clubs are exhausted. In the case of no core group being identified, the remaining individuals display a divergent behavior. The key mechanism of the log t test is that, over time, extremely large deviations from the cross-sectional mean are identified and the corresponding data points are excluded from the group. In this way, as is emphasized in this paper, the log t test examines the relative convergence, rather than the level convergence. 2.3. Data Following Fan et al. (2007), the carbon intensity is defined as the carbon dioxide emissions (in tons) per unit of GDP value (set to be 10,000 RMB). More specifically, we account for the carbon dioxide emissions from eight primary sources, namely coal, coke, crude oil, gasoline, kerosene, diesel oil, fuel oil, and natural gas. According to a practice suggested by the United Nations Framework Convention on Climate Change (UNFCCC) in 1992 and the Kyoto Protocol established in 1997, the total amount of carbon dioxide is the sum of the emissions from these sources, which is defined as:
CO2 ¼
n X i¼1
CO2;i ¼
n X i¼1
Ei NCVi CEFi COFi
44 12
ð7Þ
where CO2 denotes carbon dioxide emissions measured in tons. i = 1, …, n, stands for the categories of energy. Ei denotes the amount of energy consumed. The remaining parameters are all constants: the Net Calorific Value (NCV), Carbon Oxidation Factor (COF), Carbon Emission Factor (CEF), and molecular weight ratio of carbon dioxide over carbon. Each type of energy consumed is converted into a corresponding amount of coal based on standard coal coefficients. These constants and coefficients are all readily available in China Energy Statistical Yearbook 2011. The data sources include various issues of the China Statistical Yearbook and China Energy Statistical Yearbook (issue years include 1995 through 2012), and the China Compendium of Statistics 1949– 2008. GDP data was adjusted by Consumer Price Index (CPI). Tibet (Xizang) is not considered for a severe lack of data; the Chongqing was merged into Sichuan, because it remained attached to Sichuan until 1997. Finally, we have one extra “reference member” added to the panel data, structured as the country average, to determine the position of the country average in the clubs. Overall, there are thirty panels, spanning from 1995 to 2011.
77
3. Empirical results and analysis 3.1. Overall test results An application of the log t test to the null hypothesis of overall convergence generates the following results:
log
H1 −2 logLðt Þ ¼ 0:051−1:059 logt Ht ð4:30Þð−20:89Þ:
ð8Þ
The t-statistic of − 20.89 is far below the critical value of − 1.65, strongly rejecting the null hypothesis. This is not surprising, given the immense inter-regional differences across Chinese provinces (Herrerias and Ordoñez, 2012; Lin et al., 2013). In sharp contrast, Huang and Meng (2013) take spatial dynamics into account and find evidence of overall convergence using per capita carbon dioxide emissions from 1985 to 2008 data across Chinese provinces.
3.2. Club convergence test results The clustering algorithm introduced in Section 2.2 uncovered three convergence clubs.5 To understand the spatial distribution of these groups, the geographical locations of each club is plotted (Fig. 1). A simple glance over the geographical distribution of the clubs unveils the following discoveries. Firstly, clubs seem to be spatially concentrated. This result is similar to Quah's (1996) finding. A possible explanation for this is that the economic activity of bordering regions should influence a given region's economy, and therefore, have an impact on its convergence process. Secondly, the clubs seem to be distributed in sequence from west to east and inland area to coastal regions. This observation echoes Weeks and Yao (2003). Given the conventional partition of China into the eastern, central and western regions,6 it is noteworthy that Clubs 1 and 2 approximate the middle and western areas geographically, while Club 3 primarily corresponds to the more developed eastern region. Finally, the spatial agglomeration pattern of the clubs seems to contradict the distribution of the economic power across regions. According to the calculations based on our data, the average carbon intensity of Clubs 1 and 2 is 1.654, while that of Club 3 is 1.638. Overall, the figures seem to challenge our common sense by illustrating that less developed regions contribute more carbon dioxide emissions than their wealthy counterparts. This seemingly paradoxical relationship may be attributed to the fact that both regions are less developed and their GDP sizes are naturally smaller than that of the eastern region. By definition, the average carbon intensity of Clubs 1 and 2 ends up with higher carbon intensity. Table 1 presents the detailed results of the club convergence tests. It is noteworthy that the reference member, country average, is located in Club 1, showing a vast inter-regional imbalance in terms of carbon intensity. The positive t-statistic for each group is significantly higher than − 1.65, thus rendering strong support for the club classification. This is even more pronounced in Club 3. 5 To be specific, the first club includes: Ningxia, Xinjiang, Shǎnxi, and the country average. The second club includes Guangdong, Sichuan, Guizhou, Yunnan, Gansu, and Qinghai. The third club includes Beijing, Tianjin, Hebei, Shānxi, Neimenggu, Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, Zhejiang, Anhui, Fujian, Jiangxi, Shandong, Henan, Hubei, Hunan, Guangxi, and Hainan. 6 Following Bian and Yang (2010), the eastern region includes Beijing, Tianjin, Shanghai, Liaoning, Hebei, Shandong, Jiangsu, Zhejiang, Fujian, Guangdong, and Hainan. The middle region includes Heilongjiang, Jilin, Inner Mongolia, Henan, Shanxi, Anhui, Hubei, Hunan, Jiangxi, and Guangxi. The western region includes Gansu, Guizhou, Ningxia, Qinghai, Shaanxi, Tibet, Yunnan, Xinjiang, Sichuan, and Chongqing.
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Y. Wang et al. / Economic Modelling 43 (2014) 75–80
Club1 Club2 Club3 N.A.
Fig. 1. Geographical distribution of converging clubs in China.
3.3. Robustness check: tests of club merging Fig. 2 displays the relative transitory paths for the detected clubs to provide a visual perception of the significance of the convergence behavior. As shown below, Club 3 stays around the mean value of 1, while Clubs 1 and 2 seem to diverge in opposite directions during the sample period. The discrepancy between them remains ambiguous, and thus, necessitates further examination. To assess the robustness of our club classification more accurately, in this section, tests of club merging are conducted. Log t tests are employed to determine if neighboring clubs can merge into one group. More specifically, examinations of whether the λ1 fraction of the members with lower carbon intensity in the upper club and the λ2 fraction of the members with higher carbon intensity in the lower club converge are conducted. Consequently, if the t-statistic is greater than −1.65, then the adjacent clubs should be merged together. Phillips and Sul (2009) suggest setting λ1 = λ2 = 0.5. The test results are summarized in Table 2. As is illustrated, both t-statistic values for club merging tests, − 27.22 and − 16.88, are significantly smaller than − 1.65, strongly rejecting the null hypothesis of convergence. Hence, the tests of club merging confirm our initial classification of the three convergence clubs. 3.4. Analysis of determinants: ordered logit model In this sector, we explore the determinants underlying the formation of clubs by screening through important determinants commonly
Table 1 Convergence test results. Category Club 1 Club 2 Club 3
Number of provinces a
3+1 6 20
^ b
t-Statistic
0.189 0.098 3.718
1.129 0.869 4.054
Note: a Includes one extra “reference member”, the country average.
suggested in current works and using the ordinal club ranking as the dependent variable. This is conducted by applying the ordered logit model introduced by McKelvey and Zavoina (1975). According to club theory, convergence to a common steady state only emerges when there are similar structures or characteristics, including: • Initial conditions Prior studies have shown that initial conditions play a crucial role in determining the transitory path of an economy (Azariadis and Drazen, 1990; Shaver et al., 2000). Following conventional practice (Auffhammer and Carson, 2008; Ezcurra, 2007; Lee and Lee, 2009), per capita GDP and energy resource endowments in the initial period are employed as the conditioning variables for two reasons: 1) the close relationship between economic development and carbon emissions attracted a significant amount of attention (Moomaw and Unruh, 1997; Roberts and Grimes, 1997) after Grossman and Krueger (1991) proposed the Environment Kuznets Curve and proved it to be significant; and 2) many researchers, like Friedrichs and Inderwildi (2013), confirmed that important interactions between initial energy resource endowments and carbon emissions exist from four aspects, based on the Resource Curse Theory proposed by Auty (1993). • Structural characteristics Holding initial conditions constant, the structural characteristics dominate the dynamics of economic development. A large body literature has been motivated to unearth the important deriving forces (Houghton, 2007; Jobert et al., 2010; Wang et al., 2005; Zhuang, 2008), such as the size of the secondary industry, public investments, energy intensity, and energy consumption structure. These are also included in our explanatory variable list for two reasons: 1) according to Grossman and Krueger (1994), the economic structure is one of the three principal factors affecting the environment; the economic structure can be depicted by the size of the secondary industry. The energy intensity and energy consumption structure are also frequently used as important determinants of carbon emissions; 2) our club convergence results illustrate an obvious spatially correlated pattern; this pattern seems to coincide with the distribution of local economic development and further suggests that infrastructure construction may perform well
Y. Wang et al. / Economic Modelling 43 (2014) 75–80
79
Fig. 2. Relative transitory paths of convergence clubs.
Table 2 Convergence club classification. Initial classification ^ (SE of b) ^ b Club 1 [3 + 1]a Club 2 Club 3
0.189 (0.167) 0.098 (0.113) 3.718 (0.917)
Tests of club merging ^ (SE of b) ^ b
Final classification ^ (SE of b) ^ b
Club 1 + 2 −1.580⁎ (0.058)
Club 1 Club 2 + 3 −0.989⁎ (0.047)
Club 2 Club 3
0.189 (0.167) 0.098 (0.113) 3.718 (0.917)
Notes: ⁎ Reject the null hypothesis of growth convergence at the 5% level. The numbers in brackets stand for the number of provinces in a group. a Club 1 includes the country average.
in explaining the grouping results. In view of this, we used public investments to capture the effects of infrastructure construction. All indicators used are fairly standard. Energy resource endowments are measured by per capita fossil energy, including coal, oil, and natural gas. Energy intensity is measured by the amount of energy consumed for each dollar of GDP. The energy consumption structure is depicted by the weight of coal consumption in overall energy consumption. For a robustness consideration, public investments are measured in two ways: (1) highway mileage within a province as a percentage of its land area, and (2) fiscal expenditures as a percentage of GDP. All data was obtained from the CEIC database. To begin with, the empirical model takes the form:
yi ¼ X i β þ εi
ð9Þ
where the dependent variable y∗i takes on ordinal values from 1 to 3; Xi is the explanatory variable set and i = 1, …, 30 indicates the provinces. The column vector β corresponds to the regression coefficients. Once the ordered logit regression is employed, the coefficient in a logit regression is defined as the log value of the odds ratio.7 A positive coefficient suggests that greater values help explain the membership in a specific club. In contrast, a negative coefficient suggests that lower values help identify the membership in that club. According to Table 3, the initial conditions, energy intensity, energy consumption structure, and public investment indicators are significant to varying degrees, while the industry structure seems irrelevant. To be more specific, the positive signs on per capita GDP, the energy resource endowments, and the energy consumption structure indicate that larger values help to explain the formation of the club classification. This coincides with the pattern of the spatial distribution of the clubs, in that the eastern provinces with higher values are more stable in Club 3 than the mid-to-western provinces that are classified into 7 It measures the effect of a single variable on the odds of membership in a specific club. See Introduction to SAS. UCLA: Statistical Consulting Group. from http://www.ats.ucla. edu/stat/mult_pkg/faq/general/odds_ratio.htm.
Clubs 1 and 2. Intuitively, the eastern provinces with a higher per capita GDP, energy resource endowments, and energy consumption structure are more developed and can afford to consider the quality of economic growth, as compared with the central-to-western provinces. However, the public investment variables, including highway mileage within a province as a percentage of its land area and fiscal expenditures as a percentage of GDP, bear negative signs. That is, smaller values consolidate the grouping results. Again, this concurs with our observation in that the highway mileage within a province as a percentage of its land area is 24% for the eastern provinces, and 30% and 46% for the central and western provinces, respectively. Fiscal expenditures as a percentage of GDP are 28% for the eastern provinces, 40% for the central provinces, and 32% for the western provinces. Consequently, the eastern region seems to be converging more than the central-to-western region.
4. Concluding remarks Pollution from greenhouse gases has attracted considerable attention from many researchers and governmental officers. In most studies, per capita carbon dioxide emissions are used (Huang and Meng, 2013; Persson et al., 2007; Zhuang, 2008). However, there remains much to be explored about China's carbon dioxide emissions. In studying the carbon dioxide emissions of China, this paper differs from the previous literature in two aspects. First, carbon intensity is used to measure emissions instead of per capita basis. This conforms to the fact that GDP growth takes precedence in China. As a result, reducing emissions is achieved more on relative basis than in absolute terms. As such, the reduction targets are set based on the carbon intensity in China. Therefore, using the carbon intensity is of a more practical sense. Next, we apply the log t tests and clustering algorithm developed by Phillips and Sul (2007, 2009) to detect club convergence. This method allows for inter-regional heterogeneity within a time-varying framework. Moreover, this not only indicates a divergence or convergence, but also that the club convergence converges to a different steady state equilibrium.
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Table 3 Ordered logit regression results. Variable
Coefficient
z-Statistics
Variable
Coefficient
z-Statistics
Per capita GDP Per capita endowment Energy structurea Energy intensity
0.000018⁎⁎ 0.256687⁎ 1.77241⁎⁎⁎ −0.253157⁎⁎
2.07 1.52 4.61 −2.11
Industrial structure Highway mileage Fiscal expenditure
−1.951098 −14.39167⁎⁎⁎ −0.499966⁎⁎⁎
−1.2 −4.26 −3.3
Note: ⁎⁎⁎ Significant at the 1% level. ⁎⁎ Significant at the 5% level. ⁎ Significant at the 15% level. a Here, energy structure is short for the energy consumption structure.
The empirical results based on provincial data spanning from 1995 to 2011 provide strong evidence of a divergence of the country as a whole and further suggests three convergence clubs in terms of carbon intensity. Tests of club merging confirm the groupings. The estimates of the ordered logit regression reveal that the initial economic conditions, energy intensity, energy consumption structure, and public investments play a role in explaining the formation of clubs, while the industry structure remains irrelevant. The policy implications of this paper is relevant in terms of setting regional emissions mitigation policies to fulfill declared objectives, as it provides a clear picture of the carbon dioxide emissions status quo in China. As pointed out by Wang (2013), joint efforts may work better in controlling environmental pollution. Hence, members within the same club may consider potential cooperation opportunities in reducing carbon dioxide emissions. In this way, the overall convergence among provinces in China may be achieved in the long run.
Acknowledgment This research is supported by the National Natural Science Foundation of China (No. 71373217), the Fundamental Research Funds for the Central Universities of China (No. 2013221010), Key Projects of National Social Science Fund (No. 14AZD018), and the 2012 Scholarship for Excellent Doctoral Student by Ministry of Education and Academic Degree Commission of the State Council of China.
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