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Journal of Geographical Sciences >

2016 , Vol. 26 >Issue 2: 131 - 152

DOI: https://doi.org/10.1007/s11442-016-1259-2

Orginal Article

Urbanization, economic growth, and carbon dioxide emissions in China: A panel cointegration and causality analysis

  • LIU Yansui , 1, 2, 3 ,
  • YAN Bin 1, 2, 4 ,
  • *ZHOU Yang , 1, 2, 3
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  • 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. Key Laboratory of Regional Sustainable Development Modeling, CAS, Beijing 100101, China
  • 3. College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China
  • 4. University of Chinese Academy of Sciences, Beijing 100049, China

Author: Liu Yansui (1965-), Professor, specialized in land sciences, sustainable agriculture and rural development. E-mail:

*Corresponding author: Zhou Yang (1984-), PhD and Assistant Professor, E-mail:

Received date: 2015-08-20

  Accepted date: 2015-09-30

  Online published: 2016-02-25

Supported by

National Natural Science Foundation of China, No.41130748, No.41471143

Major Program of National Social Science Foundation of China, No.15ZDA021

Copyright

Journal of Geographical Sciences, All Rights Reserved
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Abstract

Elucidating the complex mechanism between urbanization, economic growth, carbon dioxide emissions is fundamental necessary to inform effective strategies on energy saving and emission reduction in China. Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study empirically examines the relationships among urbanization, economic growth and carbon dioxide (CO2) emissions at the national and regional levels using panel cointegration and vector error correction model and Granger causality tests. Results showed that urbanization, economic growth and CO2 emissions are integrated of order one. Urbanization contributes to economic growth, both of which increase CO2 emissions in China and its eastern, central and western regions. The impact of urbanization on CO2 emissions in the western region was larger than that in the eastern and central regions. But economic growth had a larger impact on CO2 emissions in the eastern region than that in the central and western regions. Panel causality analysis revealed a bidirectional long-run causal relationship among urbanization, economic growth and CO2 emissions, indicating that in the long run, urbanization does have a causal effect on economic growth in China, both of which have causal effect on CO2 emissions. At the regional level, we also found a bidirectional long-run causality between land urbanization and economic growth in eastern and central China. These results demonstrated that it might be difficult for China to pursue carbon emissions reduction policy and to control urban expansion without impeding economic growth in the long run. In the short-run, we observed a unidirectional causation running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern and central regions. Further investigations revealed an inverted N-shaped relationship between CO2 emissions and economic growth in China, not supporting the environmental Kuznets curve (EKC) hypothesis. Our empirical findings have an important reference value for policy-makers in formulating effective energy saving and emission reduction strategies for China.

Key words： urbanization; economic growth; CO2 emissions; panel cointegration; Granger causality

Cite this article

LIU Yansui , YAN Bin , *ZHOU Yang . Urbanization, economic growth, and carbon dioxide emissions in China: A panel cointegration and causality analysis[J]. Journal of Geographical Sciences, 2016 , 26(2) : 131 -152 . DOI: 10.1007/s11442-016-1259-2

1 Introduction

Along with rapid economic growth, China has undergone an unprecedented process of urbanization since the 1978 economic reform (Liu and Yang, 2012; Long et al., 2013; Liu et al., 2015). China’s urbanization rate has even developed far ahead of its economic growth since 2004 (Yang, 2013; Chen et al., 2013). Urbanization and economic development have long been regarded as interconnected processes. The rapid growth of China’s extensive economy in recent decades has inevitably caused fast growth of CO2 emissions. Furthermore, the construction of urban infrastructures and household consumption in the process of rapid urbanization has also led to an increase in CO2 emissions (Peters et al., 2007; Raupach et al., 2007). Although urbanization has played a vital role in stimulating economic growth in China, it has created serious environmental problems, posing tremendous challenges to sustainable development (Liu and Diamond, 2005; Peters et al., 2007; Liu et al., 2008; Liu Yansui et al., 2014).
As the world’s largest developing country, China is now facing the international restriction of reducing greenhouse gas emissions. The Chinese government set binding target in the “Twelfth Five-Year Plan” to reduce the unit GDP energy consumption and CO2 emissions by 16% and 17% (down from 20% in the last plan) by the end of 2015, respectively. The government needs to explore new development paths to achieve the targets of energy saving and emission reduction while ensuring its economic development (Wang et al., 2011). Thus, the relationship between CO2 emissions and economic growth is of particular research concern, especially China has promised to reduce its carbon emissions by 40%-50% in 2020.
Urbanization is closely linked to economic development, both of which inevitably increase energy consumption and CO2 emissions (Zhang and Lin, 2012; Bai et al., 2014; Wang et al., 2014). Previous studies on urbanization-economy-environment nexus in China may be divided into three lines of research (Wang et al., 2011; Ma et al., 2011; Cheng et al., 2013; Sun et al., 2013; Liu et al., 2015; Zhou et al., 2015). The first strand of study has primarily focused on the relationship between economic growth and urbanization. Based on a positive correlation between economic growth and urbanization, some researchers argued that economic growth stimulates urban land expansion or vice versa. However, correlation does not imply causation (Bai et al., 2012). Some studies examined the causality between urbanization and economic growth. For instance, Li and Cheng (2006) uncovered that economic growth was the long-run Granger cause of China’s urbanization. Wang et al. (2011) investigated the Granger causality between CO2 emissions, energy consumption and economic output for China 28 provinces. Bai et al. (2012) examined the causal relationship between urbanization and economic growth in Chinese cities and provinces in recent decades and found that a long-run bidirectional causality exists between urbanization and economic growth. Recently, Chen et al. (2014) detected that the relationship between urbanization and economic development is similar to the Matthew effect in China. The second strand of study has largely discussed the driving forces behind the growth in CO2 emission in China. Using the decomposition method, Zhang et al. (2009) identified economic activity was one of the driving forces influencing CO2 emissions. A similar conclusion was obtained by Lin and Liu (2010). Feng et al. (2012) found that urbanization and associated income and lifestyle changes were important driving forces for the growth of CO2 emissions in most parts of China. Using an improved STIRPAT (stochastic impacts by regression on population, affluence and technology) model, Zhang and Lin (2012) argued that urbanization increases energy consumption and CO2 emissions in China. At the local level, Wang et al. (2012a) found that urbanization was the main contributors to CO2 emissions in Beijing, China.
The third strand of study has mainly concentrated on the relationship between economic growth and environmental quality with a particular focus on validation of the EKC hypothesis. Song et al. (2008) examined the relationship between three industrial pollutants (i.e., waste water, exhaust gases and solid waste) and economic growth in China based on the EKC hypothesis. Using the time series data of 1975-2005 in China, Jalil and Mahumd (2009) found the evidence supporting the EKC hypothesis for CO2 emissions. Wang et al. (2011) observed the existence of a U-shaped curve between economic growth and CO2 emissions. Most studies have confirmed the existence of a close relationship between economic growth and CO2 emissions in China. However, some recent studies have observed mixed findings. For example, Wang et al. (2012a) argued that there was no evidence supporting the EKC hypothesis for CO2 emissions in Beijing city, China. Using a panel data of 29 provinces in China from 1995 to 2009, Du et al. (2012) found that economic development was one of the most import factors affecting CO2 emissions, but the EKC hypothesis was not strongly supported. So far, the existing findings on the relationship between economic growth and CO2 emissions in China remain controversial.
Given urbanization may continue to be an important engine of China’s economic growth, understanding the relationship between urbanization, economic growth and CO2 emissions is essential for policymakers in formulating effective energy saving and emission reduction policies. Most of the existing studies only focus on the relationship between urbanization, economic growth and CO2 emissions in China rather than on the linkages among those variables without consideration of regional differences as well. Thus, the relationships between urbanization, economic growth and CO2 emissions in China still need further investigation. A main objective of this study was to investigate the relationship between urbanization, economic growth and CO2 emissions at the national and regional levels using a balanced panel data of China’s 31 provinces for the period 1997-2010. This is a period in China’s history characterized by rapid economic development and a correspondingly large increase in urbanization rate and CO2 emissions. In addition, China’s road to urbanization has been thought of as unique for it is neither identical to the parallel-urbanization experience of developed countries nor does it duplicate the path of developing countries (Chen et al., 2012; Liu et al., 2013). Investigating the complex relationship between economic growth, urbanization and CO2 emissions in China would provide a beneficial reference for other countries in formulating the energy saving and emission reduction strategies.

2 Data and methods

2.1 Data source

Urban population and built-up area are generally used as the proxy indicators of demographic urbanization (Bloom et al., 2008) and land (landscape) urbanization (Bai et al., 2012), respectively. We also chose built-up area (BA) and urban population (UP) as the proxy indices of land urbanization and demographic urbanization, respectively, and used real GDP per capita (pGDP) as economic indicator, and used CO2 emissions as the environmental indicator (Table 1). All provincial data covering the period 1997-2010 are collected from the China Statistical Yearbooks (CSY) and China City Statistical Yearbook (CCSY). The CO2 data over the period from 1997 to 2010 are obtained from the results calculated by Guan et al. (2012), in which the data on CO2 emissions for all provinces are estimated from the publicly available energy statistics from Chinese authorities following the Intergovernmental Panel on Climate Change (IPCC) emission accounting approach. In the present study, all the variables are expressed in natural logarithms so that they may be considered elasticity of the relevant variables. The GDP is calculated at a constant price (1997 prices) and GDP per capita is calculated from GDP divided by the year-end population. The data for China do not include data for Hong Kong, Macao and Taiwan, China.
Table 1 Data description and sources
Indicators Unit Abbreviation Meaning of indicators Sources
Built-up area km2 BA Land urbanization CCSY
Urban population persons UP Demographic urbanization CSY
GDP per capita yuan RMB pGDP Economic growth CSY
CO2 emissions million tons CO2 Pollutant emissions Guan et al., 2012

2.2 Econometric analysis

This study utilized panel unit root, cointegration and causality analysis to investigate the relationship between urbanization level, economic growth and CO2 emissions. The empirical modeling framework in the present study consists of four stages. First, the presence of unit roots in the all variables was tested. The LLC and IPS methods were used to test the presence of the panel unit root (Levin et al., 2002; Im et al., 2003). In addition to these methods, Maddala and Wu (1999) and Choi (2001) provided two nonparametric unit root tests, the Fisher-ADF and the Fisher-PP statistics.
Second, because each of the variables contained a panel unit root, the heterogeneous panel cointegration test developed first by Pedroni (2004) was performed to examine whether there was a long-term equilibrium relationship between the variables. The Pedroni panel cointegration tests included two types, one was the panel cointegration test and the other was the group mean panel cointegration test. The former was based on the within dimension approach, which included the following statistics: Panel-v, Panel-rho, Panel-ADF and Panel-PP. The latter was based on the between dimension approach, which included three statistics: Group-rho, Group-PP and Group-ADF (Pedroni, 2004; Mahadevan and Asafu-Adjaye, 2007). It is generally accepted that the Panel-ADF and the Group-ADF statistics had better small sample properties than the other statistics, which made them more reliable.
Third, two techniques, i.e. the fully modified ordinary least squares (FMOLS) estimator and the dynamic ordinary least squares (DOLS) estimator were used to further estimate the long-run equilibrium relationships among the variables. The FMOLS estimator is believed to eliminate endogeneity in the regressor and serial correlation in the errors (Pedroni, 2000). The DOLS estimators had a normal asymptotic distribution and their standard deviations provided a valid test for the statistical significance of the variables (McCoskey and Kao, 1999). In general, the DOLS technique is more reliable than the panel OLS estimation for panel data (McCoskey and Kao, 1999; Pedroni, 2000). The FMOLS estimator can be calculated in the following:
where wi,t and xi,t are cointegrated with slope βi, and βi may or may not be homogeneous across i. -Ki and Ki are leads and lags.
where is the long-run covariance for this vector process which can be decomposed into where is the contemporaneous covariance and is a weighted sum of auto-covariance.
The panel DOLS estimator is defined as:
where is vector of regressors, and
Lastly, confirming the existence of cointegration between the variables, the next step was to examine both short- and long-run causality by performing Granger causality test based on vector correction model (VECM). The empirical models are specified as follows:
where Δ denotes the difference of the variable; K is the lag length; is the error correction term with lag 1; εi,t is the residuals of the mode; μ is the coefficient of the error correction term βi,t-1. The significance of causality results is determined by Wald F-test. In the short-run, the x does not Granger cause y where H0: θ2k=0 for all i and k, while the long-run causality can be established if μ=0.
Furthermore, we employed a heterogeneous panel Granger causality analysis, recently proposed by Dumitrescu and Hurlin (2012), to further verify the short-term Granger casual relationship between urbanization, economic growth and CO2 emissions. The testing procedure is superior to former panel Granger causality tests since it can give efficient results even in panels with small sample sizes (Tugcu, 2014). The Dumitrescu and Hurlin (2012) test of Granger non-causality for heterogeneous panel is based on the stationary fixed-effects panel model:
where x and y are two stationary variables observed for N provinces in T periods. βi(k) denote the autoregressive parameters, and δi(k) are the regression coefficient’s slopes; δi=(δi(1),…, δi(k))’; individual effects θi are assumed to be fixed; K is the lag length. By definition, x causes y if and only if the past values of the variable x observed on the i-th province improves the forecasts of the variable y for this province i only. The test is based on the null hypothesis of homogeneous non-causality (HNC), there is no causal relationship from x to y for all the provinces of the panel Under the alternative hypothesis, there exists a causal relationship from x to y for at least one province of the sample. The test statistic is given by the cross-sectional average of individual Wald statistics defined for the Granger non-causality hypothesis for each province:
where Wi,T is the individual Wald statistic for the ith cross-section unit. Under the null hypothesis of non-causality, each individual Wald statistic converges to a chi-squared distribution with K degrees of freedom for T→∞.
The standardized test statistic for fixed T samples is as follows (Dumitrescu and Hurlin (2002):

2.3 The EKC model

According to the EKC hypothesis, the long-term relationship between economic growth and CO2 emissions can be expressed as a logarithmic cubic function of the income (Grossman and Krueger, 1995). The simplified EKC model is given by:
where i-province (i=1, 2,…31), t-year (t=1, 2,…T); α0 represents cross-section effect; εit is random disturbance; β1, β2, β3 and β4 are the estimated coefficients; pGDP is real GDP per capita; BA is built-up area. Eq. (6) allows us to test the various forms of environmental- economic linkages: β1>0, β2<0 and β3>0 indicating an N-shaped relationship; β1<0, β2>0 and β3<0 indicating an inverse N-shaped relationship; β1<0, β2>0 and β3=0 indicating a U-shaped relationship β1>0; β1>0, β2<0 and β3=0 indicating an inverse U-shaped relationship, representing the EKC hypothesis, the turning point of the EKC is computed by ω=exp(-0.5β1/β2); β1>0, β2=0 and β3=0 indicating a monotonically increasing linear relationship; β1<0, β2=0 and β3=0 indicating a monotonically decreasing linear relationship; β1=β2=β3=0 indicating a level relationship.

3 China’s urbanization, economic growth and CO2 emissions

The variation in land urbanization, demographic urbanization, GDP and CO2 emissions for China between 1997 and 2010 are shown in Figure 1. The number of China’s inland cities had increased from 193 in 1978 to 657 in 2010, and its urban population increased from 172.45 million to 681.13 million with an annual average growth rate of 4.53% (NBSC, 2011; Figure 1a). According to the latest National New-type Urbanization Plan released by the central government, China’s urbanization level was projected to reach 60% by 2020. Urban population growth in China was characterized by rural-to-urban migration (Gong et al., 2012). To accommodate this massive influx of population onto cities, China’s urban area had expanded rapidly. Specifically, China’s urban built-up area has increased from 13,685 km2 in 1997 to 41,244 km2 in 2010 (Figure 1b). During the same period, China’s urban area had expanded by 201%, whereas the urban population had only increased by 105%, indicating that the rate of China’s land urbanization is almost twice as fast as population urbanization. China’s real GDP had rapidly increased from 7897 million RMB yuan in 1997 to 26,723 million yuan in 2010 with an average annual increase of 10.7% (Figure 1c). Large-scale industrialization and urbanization have also made China the largest CO2 emitter in the world (Minx et al., 2011; IEA, 2012). The annual CO2 emissions in China had been increasing from 2887 million tons in 1997 to 8175 million tons in 2010 (Figure 1d).
Figure 1 The changes in land urbanization (a), demographic urbanization (b), GDP (c) and CO2 emissions (d) for China between 1997 and 2010
Linear regressions were further used to examine the changes in each variable for China’s 31 provinces during the period 1997-2010. The slopes of the lines of best fit reflected the changes in the variables, and a greater slope indicated greater changes in the variables. Figure 2 displays the changes in urban population, built-up area, per capita GDP and CO2 emissions for 31 provinces between 1997 and 2010. The top 5 provinces with the highest growth rate of urbanization population were Guangdong, Jiangsu, Shandong, Henan and Hebei (Figure 2a). In contrast, Tibet, Qinghai, Heilongjiang, Ningxia and Hainan were the provinces with the lowest growth rate of urban population. Similar to the urban population, the growth rate of built-up area in Guangdong Province was the highest, followed by Shandong, Jiangsu, Zhejiang and Henan (Figure 2b). The growth rate in GDP per capita in eastern China was relatively rapid over the past 14 years (Figure 2c). For CO2 emissions, the growth rate in Shandong Province (53.25 million tons per year) was the highest, followed by Hebei (35.35 million tons per year), Inner Mongolia (31.45 million tons per year), Jiangsu (31.15 million tons per year) and Henan (28.54 million tons per year) (Figure 2d).
Figure 2 The growth rate in urban population (a), built-up area (b), GDP per capita (c) and CO2 emissions (d) for China’s 31 provinces

4 The relationships among urbanization, economic growth and CO2 emissions in China

4.1 Panel unit root tests

It is advisable to check the presence of unit roots in the all variables before proceeding to any econometric analysis because using the conventional ordinary least squares (OLS) estimator with non-stationary variables might result in spurious regressions. Table 2 provides the panel unit root test results with and without a trend term. Results show that many variables are non-stationary in their levels, but most of them become stationary at the 5% significance level after taking first differences. This results indicate that built-up area, urban population, real GDP per capita, built-up area and CO2 emissions in all panels were integrated at order one, suggesting a possible long-run cointegration relationship among these variables.
Table 2 Panel unit root test results
Levels
Variable LLC IPS Fisher-ADF Fisher-PP
Intercept BA -2.75*** 4.04 18.88 26.12
UP -4.68*** 1.19 61.71 112.52***
pGDP 5.863 12.1 9.23 4.24
CO2 1.65 7.18 8.4 6.45
Intercept and trend BA -1.50* -1.47* 47.28 52.87
UP -9.11*** -0.27 67.02 27.63
pGDP -10.31*** -1.68** 91.47*** 68.99
CO2 -6.39*** -2.32** 89.47** 74.63
First differences
Variable LLC IPS Fisher-ADF Fisher-PP
Intercept BA -6.39*** -4.65*** 115.01*** 224.74***
UP -11.32*** -6.40*** 149.70*** 220.52***
pGDP -4.51*** -0.29 55.29** 52.95***
CO2 -6.12*** -4.11*** 106.59*** 195.71***
Intercept and trend BA -5.54*** -1.65*** 75.35*** 202.62***
UP -18.90*** -9.45*** 203.53*** 309.78***
pGDP -2.16*** 0.42 59.99** 105.29***
CO2 -2.59*** 0.10** 53.40** 158.00***

Note: Newey-West bandwidth selection using Bartlett kernel. Automation selection of lags was based on SIC. Levin, Lin and Chu test (LLC) Null: unit root (assumes a common unit root process); Im, Pesaran and Shin W-stat test (IPSW), ADF-Fisher Chi-square test (ADF) and PP-Fisher Chi-square test (PP) Null: Unit root (assumes an individual unit root process). BA, UP, pGDP and CO2 are built-up area, urban population, and per capita GDP and CO2 emissions, respectively. The null hypothesis of the LLC, IPS, Fisher-ADF and Fisher PP tests examines non-stationary. ***, ** and * indicates statistical significance at the 1%, 5% and 10% significance level, respectively.

4.2 Panel cointegration tests

If the series are integrated of the same order one can proceed with the cointegration test. Table 3 presents the panel cointegration test results for all panel datasets. For Panel A, all seven panel cointegration tests rejected the null hypothesis of no cointegration at the 10% significance level except the Group rho-statistic, meaning a long-run equilibrium relationship between land urbanization and economic growth, which is in agreement with a previous study (Bai et al., 2012). For Panel B, most of the panel cointegration test statistics were statistically insignificant at the 5% level expect the Panel ADF and Group ADF, which indicates that no long-run stable relationship exists between demographic urbanization and economic growth. The Pedroni test results revealed the existence of cointegration between urbanization and CO2 emissions only in Panel C, suggesting a long-run equilibrium relationship between land urbanization and CO2 emissions. All the panel test statistics in Panel D were statistically insignificant at the 10% level, implying that no a long-run equilibrium linkage exists between demographic urbanization and economic growth. With the exception of the Group rho-statistics, the other six test statistics in Panel E reject the null hypothesis of no cointegration at the 5% significance level, suggesting a long-term cointegrating relationship between CO2 emissions and economic growth. It can be concluded that constant long-run equilibrium relationship exists between land urbanization, economic growth and CO2 emissions in China for the period 1997-2010.
Table 3 Panel cointegration test results
Statistics Panel A Panel B Panel C Panel D Panel E
Panel v 1.51* 1.23 3.22*** -0.03 3.70***
Panel rho -1.75** 0.10 -3.25*** 0.71 -1.93**
Panel PP -2.40*** -1.17 -7.18*** 0.24 -5.31***
Panel ADF -2.98*** -1.29* -8.12*** -1.19 -8.10***
Group rho 1.48 2.77 -0.35 2.67 0.13
Group PP -0.36* 0.97 -5.74*** 0.96 -5.63***
Group ADF -2.04** -1.06* -7.61*** -1.26 -8.80***

Note: Panel A (built-up area---pGDP); Panel B (urban population---pGDP); Panel C (built-up area---CO2); Panel D (urban population---CO2); Panel E (pGDP---CO2). Statistics are asymptotically distributed as normal. All tests contain only the intercept and not the trend term. The variance ratio test is right-side, which the others are left-sided. The null hypothesis is that the variables are not cointegrated. Lag length selected based on AIC automatically with a max lag of 2. ***, ** and * reject the null of no cointegration at the 1%, 5% and 10% significance level, respectively.

4.3 Panel cointegration estimation

Once the cointegration relationship is established, the next step is to estimate the long-run parameters. Table 4 provides the results of the whole China and its three regions (i.e., eastern, central and western regions(The eastern region includes Liaoning, Beijing, Tianjin, Hebei, Shandong, Jiangsu, Shanghai, Zhejiang, Guangdong, Fujian and Hainan provinces; the central region includes Heilongjiang, Jilin, Shanxi, Henan, Anhui, Jiangxi, Hubei and Hunan provinces; and the western region includes Xinjiang, Gansu, Qinghai, Inner Mongolia, Ningxia, Shaanxi, Sichuan, Chongqing, Guizhou, Yunnan and Guangxi provinces (Zhong et al., 2011).)) based on the panel FMOLS and DOLS estimations. The cointegration coefficients between urban population, pGDP and CO2 emissions are not estimated since the lack of cointegration among them. Two estimators produced almost identical results, suggesting that the estimates were not sensitive to whether the FMOLS or the DOLS method was used. Often the values of the DOLS estimators are determined under the assumption of one lead, one lag or tow leads, two lags in the change of the regressors (Li et al., 2011). The DOLS estimators were thus sensitive to the choice of number of lags and leads. But the most coefficients from DOLS estimation in our sample varied only slightly for different lags and leads. For the whole China, all estimated coefficients were positive and statistically significant at the 1% level when using pGDP, CO2 and built-up area as dependent variables. The results suggest that urban expansion had a positive impact on economic growth and CO2 emissions in China. In turn, economic growth contributed to the expansion of urban built-up area, and more people clustered in cities, which increases energy consumption and accordingly generates more emissions (Cole and Neumayer, 2004). More specifically, a 1% expansion in urban built-up area increases per capita income by approximately 0.81%-0.94%. A 1% increase in per capita income contributes to the expansion in urban built-up area by 0.66%-0.93% when using built-up area as the dependent variable. When using CO2 emissions as the dependent variable, every 1% rise in land urbanization rate increases CO2 emissions by approximately 1.15%-1.19%, and every 1% increase in per capita income increases CO2 emissions by 0.60%-0.94%.
Table 4 Panel cointegration coefficients by FMOLS and DOLS for China and its eastern, central and western regions
Whole China
Variable Dependent variable: pGDP Variable Dependent variable: BA
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
BA 0.90***
(-27.29)		 0.81***
(-21.66)		 0.94***
(-27.58)		 pGDP 0.66***
(-9.64)		 0.22
(-0.47)		 0.93***
(-43.33)
Variable Dependent variable: CO2 Variable Dependent variable: CO2
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
pGDP 0.83***
(-16.22)		 0.60***
(-2.12)		 0.94***
(-61.07)		 BA 1.19***
(-36.78)		 1.16***
(-29.56)		 1.15***
(-34.88)
Obs 341 279 403 Obs 341 279 403
Eastern region
Variable Dependent variable: pGDP Variable Dependent variable: BA
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
BA 1.02***
(-50.73)		 0.99***
(-6.63)		 1.03***
(-38.21)		 pGDP 0.66***
(-9.64)		 0.43
(-0.51)		 1.06***
(-26.07)
Variable Dependent variable: CO2 Variable Dependent variable: CO2
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
pGDP 0.90***
(-20.11)		 0.57
(-0.77)		 0.96***
(-42.37)		 BA 0.98***
(-75.43)		 0.99***
(-10.35)		 0.95***
(-45.29)
Obs 121 99 143 Obs 121 99 143
Central region
Variable Dependent variable: pGDP Variable Dependent variable: BA
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
BA 1.17***
(-26.27		 1.91***
(-52.62)		 1.21***
(-26.97)		 pGDP 0.90***
(-8.34)		 0.30
(-1.4)		 0.83***
(-25.16)
Variable Dependent variable: CO2 Variable Dependent variable: CO2
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
pGDP 0.64***
(-10.6)		 0.40***
(-4.28)		 0.84***
(-28.26)		 BA 0.99***
(-38.35)		 1.03***
(-28.1)		 0.98***
(-24.42)
Obs 88 72 104 Obs 88 72 104
Western region
Variable Dependent variable: pGDP Variable Dependent variable: BA
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
BA 1.24***
(-32.66)		 1.35***
(-49.14)		 1.29***
(-28.75)		 pGDP 0.53***
(-5.90)		 0.25
(-1.92)		 0.79***
(-24.24)
Variable Dependent variable: CO2 Variable Dependent variable: CO2
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
pGDP 0.64***
(-7.24)		 0.63***
(-1.36)		 0.97***
(-29.77)		 BA 1.27***
(-43.92)		 1.29***
(-41.09)		 1.24***
(-27.96)
Obs 88 72 104 Obs 88 72 104

Notes: BA and pGDP are built-up area and per capita GDP, respectively. The t-values are in parentheses. The panel method was grouped estimation. A panel data model with fixed effects was adopted. All tests were performed on the natural logarithm of the dependent and independent variables. Obs is observations. *, **and*** indicate the estimates are statistically significant at the 10%, 5% and 1% level, respectively.

At the regional level, most estimated coefficients were also positive and statistically significant at the 1% level when using pGDP, CO2 and built-up area as dependent variables. The urban expansion had a positive and significant impact on economic growth for the three regions, but the impact in the western region was slightly higher than that in the central and eastern regions. A 1% expansion in the build-up area contributes to the increase in per capita income by approximately 1.24%-1.35% in the western region, 1.17%-1.91% and 0.99%-1.03% in the central and eastern regions, respectively. In turn, economic growth also promoted to the expansion in urban built-up area. Specifically, every 1% rise in income per capita contributes to the expansion in built-up area by 0.66%-1.06% in the eastern region, 0.83%-0.90% and 0.53%-0.79% in the central and western regions, respectively. Meanwhile, both economic development and built-up area expansion also promoted to carbon emissions at the regional scale. A 1% increase in per capita income contributes to the increase in CO2 emissions by approximately 0.90%-0.96% in eastern China, 0.40%-0.84% and 0.63%-0.97% in central and western China, respectively. Every 1% rise in urban expansion increases CO2 emissions by about 0.95%-0.99% in the eastern region, 0.98%-1.03% and 1.24%-1.29% in the central and western regions, respectively. The impact of urban expansion to CO2 emissions in the western region was larger than that in the eastern and central parts, whereas the effect of economic growth to emissions in the eastern region was larger than that in the western and central regions. This could be explained by the fact that in the eastern region, the increasing urban population, the scarcity of urban land, the competitive pressures of markets and advanced technology encourage the substitution of traditional energy sources by more flexible and reliable energy sources, which contribute to the reduction in proportion of coal use in energy consumption (Wang and Lin, 2012). On the other hand, the level of economic development in the eastern region was higher that than in the central and western regions. The economic development was positively associated with energy consumption (Bai et al., 2012). Therefore, the rapid economic growth and more energy consumption in the eastern region inevitably emit more CO2 than that in the western and central regions.
The relationship between the growth rate in per capita income, built-up area and CO2 emissions of China was displayed in Table 5. The correlation between per capita income and built-up area growth was statistically insignificant at the 5% level or higher. But the growth rate in economy and urban expansion had a significant and positive impact on CO2 emissions in China, and economic growth had a larger effect on emissions than urban expansion. A 1% increase in economic growth contributes to the increase in CO2 emissions by 1.76%-3.22%, and every 1% rise in urban expansion increases emissions by about 0.30%-0.80% in the eastern region.
Table 5 Panel cointegration coefficients by FMOLS and DOLS for China based on the growth rate of all variables
Variable Dependent variable: pGDP growth Variable Dependent variable: BA growth
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
BA growth 0.07
(1.29)		 0.04
(0.46)		 0.06
(3.16)		 pGDP growth -0.06
(-0.09)		 2.52
(1.13)		 0.62
(2.30)
Variable Dependent variable: CO2 growth Variable Dependent variable: CO2 growth
DOLS (1,1) DOLS (2,2) FMOLS DOLS (1,1) DOLS (2,2) FMOLS
pGDP growth 2.58***
(5.47)		 3.22**
(2.00)		 1.76***
(7.01)		 BA growth 0.53***
(3.99)		 0.80***
(2.25)		 0.30***
(4.97)
Obs 310 248 372 Obs 310 248 372

Notes: BA and pGDP are the growth rate of built-up area and per capita GDP, respectively. The t-values are in parentheses. The panel method was grouped estimation. A panel data model with fixed effects was adopted. All tests were performed on the natural logarithm of the dependent and independent variables. Obs is observations. **and*** indicate the estimates are statistically significant at the 5% and 1% level, respectively.

4.4 Granger causality tests

4.4.1 Granger causality tests based on panel VECM
Table 6 lists the Granger causality test results based on panel VECM for the whole China and its three regions. Bidirectional long-run causalities between land urbanization (built-up area expansion), economic growth, and CO2 emissions existed at the national level. Whereas there are only unidirectional short-run causal linkages running from: economic growth to land urbanization; land urbanization to CO2 emissions; and economic growth to CO2 emissions (Figure 3). Specifically, economic growth contributed to urban expansion, but not vice versa (Panel A), which is not in agreement with a previous study showing long- and short-run unidirectional causality between economic growth and land urbanization in China (Bai et al., 2012). Further, it can be found that both land urbanization and economic growth were the Granger cause of CO2 emissions, but not vice versa (Panel B and C).
Table 6 Wald F-test statistics based on panel-based vector error corrected models for the whole China and its eastern, central and western regions
Panel Causal Result F-statistic value
Short-run causality Long-run causality
Whole China A pGDP BA 15.237 (0.00) 192.31 (0.00)
BA pGDP 7.204 (0.12) 6421.924 (0.00)
B BA CO2 18.446 (0.00) 313.338 (0.00)
CO2 BA 17.518 (0.10) 194.408 (0.00)
C pGDP CO2 9.019 (0.05) 6331.291 (0.00)
CO2 pGDP 4.444 (0.34) 305.076 (0.00)
Eastern region D pGDP BA 0.635 (0.73) 14.571 (0.01)
BA pGDP 3.562 (0.17) 28.332 (0.00)
E BA CO2 5.004 (0.05) 8.156 (0.00)
CO2 BA 6.806 (0.16) 19.042 (0.15)
F pGDP CO2 3.373 (0.04) 10.885 (0.02)
CO2 pGDP 2.385 (0.30) 59.190 (0.10)
Central region G pGDP BA 0.708 (0.70) 12.490 (0.01)
BA pGDP 3.116 (0.21) 110.951 (0.00)
H BA CO2 10.981 (0.00) 19.285 (0.00)
CO2 BA 3.562 (0.16) 10.308 (0.12)
I pGDP CO2 2.640 (0.05) 13.521 (0.00)
CO2 pGDP 12.367 (0.14) 133.69 (0.10)
Western region J pGDP BA 0.231 (0.89) 2.928 (0.71)
BA pGDP 3.988 (0.13) 53.577 (0.10)
K BA CO2 2.112 (0.35) 6.898 (0.22)
CO2 BA 3.018 (0.22) 6.749 (0.15)
L pGDP CO2 1.749 (0.42) 4.501 (0.34)
CO2 pGDP 1.086 (0.58) 169.38 (0.34)

Notes: The null hypothesis is non-causality. BA, pGDP and CO2 are built-up area, per capita GDP and CO2 emissions, respectively. Cases with probability levels (shown in parentheses) lower than 0.05 reject the null hypothesis.

Figure 3 Long- and short-run Granger causality between land urbanization, economic growth and CO2 emissions in China
At the regional scale, a bidirectional long-run causality between land urbanization and economic growth was found in the eastern and central regions (Panel D and G). But there exist only unidirectional long- and short-term causal linkages running from: land urbanization to CO2 emissions (Panel E and F); and economic growth to CO2 emissions in the eastern and central regions (Panel H and I). Both no long- and short-term causal relationship between land urbanization, economic growth and CO2 emissions was detected in the western region (Table 6).
4.4.2 Heterogeneous panel Granger causality tests
Table 7 provides the heterogeneous panel Granger causality test results for the whole China and its eastern and western regions(Heterogeneous panel Granger causality test results for the central region was not given due to the number of cross-section in this region less than 9. Dumitrescu and Hurlin (2012) causality tests require the cross-sectional number greater than or equal to 9.). These findings further verified the presence of unidirectional short-term causal linkages between urbanization, economic growth and CO2 emissions at the national level. The short-run causalities from economic growth to land urbanization were detected in both Lag 1 and Lag 2 models. Land urbanization was the short-run Granger cause of CO2 emissions in both Lag 1 and Lag 2 models, but not vice versa. In addition, the short-run Granger causality from economic growth to CO2 emissions was also found both in Lag 1 and Lag 2 models, but the causal relation from CO2 emissions to economic growth was detected only in Lag 2 model (Table 7). These results indicated that both land urbanization and CO2 emissions have little or no short-run impact on economic growth in China between 1997 and 2010. At the regional scale, we also further discovered the unidirectional short-term causal linkages running from land urbanization to CO2 emissions and from economic growth to CO2 emissions in the eastern region. Meanwhile, we also detected the presence of the unidirectional short- term causality between economic growth and land urbanization in the eastern region. The short-run Granger causality relationship between land urbanization, economic growth and CO2 emissions in the western region were not found.
Table 7 Heterogeneous panel Granger causality test results for the China and its eastern and western regions
Panel Causal Result Wald-static Zbar-static Probability
Whole China A pGDP BA Lag 1: 2.052 Lag 1: 2.063 0.04
Lag 2: 5.458 Lag 2: 3.460 0.00
BA pGDP Lag 1: 3.743 Lag 1: 6.411 0.10
Lag 2: 3.207 Lag 2: 0.530 0.60
B BA CO2 Lag 1: 10.190 Lag 1: 22.990 0.00
Lag 2: 10.792 Lag 2: 10.403 0.00
CO2 BA Lag 1: 1.537 Lag 1: 0.739 0.46
Lag 2: 3.742 Lag 2: 1.226 0.22
C pGDP CO2 Lag 1: 7.138 Lag 1: 15.143 0.00
Lag 2: 12.426 Lag 2: 12.531 0.00
CO2 pGDP Lag 1: 1.789 Lag 1: 1.386 0.17
Lag 2: 4.363 Lag 2: 2.035 0.04
Eastern region Panel Causal Result W-static Zbar-stat Porb.
D pGDP BA Lag 1: 1.729 Lag 1: 0.735 0.46
Lag 2:3.052 Lag 2: 0.196 0.84
BA pGDP Lag 1: 3.643 Lag 1: 3.665 0.00
Lag 2: 7.598 Lag 2: 3.721 0.00
E BA CO2 Lag 1: 9.610 Lag 1: 12.806 0.00
Lag 2: 7.604 Lag 2: 3.725 0.00
CO2 BA Lag 1: 1.514 Lag 1: 0.404 0.69
Lag 2: 3.502 Lag 2: 0.554 0.59
F CO2 pGDP Lag 1: 0.953 Lag 1: -0.454 0.65
Lag 2: 3.680 Lag 2: 0.683 0.49
pGDP CO2 Lag 1: 6.039 Lag 1: 7.336 0.00
Lag 2: 15.939 Lag 2: 10.189 0.00
Western region Panel Causal Result W-static Zbar-stat Porb.
J pGDP BA Lag 1: 2.006 Lag 1: 0.987 0.32
Lag 2: 5.319 Lag 2: 1.666 0.10
BA pGDP Lag 1: 4.519 Lag 1: 4.271 0.10
Lag 2: 5.262 Lag 2: 1.628 0.10
K BA CO2 Lag 1: 7.959 Lag 1: 8.765 0.09
Lag 2: 11.269 Lag 2: 5.601 0.25
CO2 BA Lag 1: 1.566 Lag 1: 0.412 0.68
Lag 2: 1.266 Lag 2: -0.014 0.31
L CO2 pGDP Lag 1: 1.643 Lag 1:0.513 0.61
Lag 2: 4.694 Lag 2: 1.252 0.21
pGDP CO2 Lag 1: 8.808 Lag 1: 9.873 0.35
Lag 2: 13.559 Lag 2: 7.114 0.68

Notes: The null hypothesis is homogeneous non-causality. Cases with probability levels lower than 0.05 reject the null hypothesis. Lag 1 and Lag 2 represent the test models of the Dumitrescu and Hurlin (2012) causality tests of lag order 1 and 2, respectively.

5 Validation of the EKC hypothesis in China

To further validate the EKC hypothesis in China, the Hausman test was performed to determine which one should be selected from two models: random effect and fixed effect models. Based on the assumption of the random effect model, the null hypothesis should be rejected for both quadratic and cubic models at the 1% significance level, which means that the fixed effect model may be more suitable than the random effect (Table 8).
Table 8 Hausman test results
Test summary Chi-Sq. statistic
Chi-Sq. statistic Quadratic Cubic
40.304 41.210
Prob. 0.000 0.000
Accept model Fixed effects Fixed effects
Table 9 shows the estimates from the panel OLS estimator. For quadratic model, economic growth and urbanization had a positive and significant impact on CO2 emissions. But the estimated coefficient on income squared was statistically insignificant at the 5% level. For cubic model, all the coefficients in the panel OLS equation were statistically significant at the 1% level or lower. Furthermore, the Wald test was also performed to choose the most appropriate one between the quadratic and cubic models. Results showed that the null hypothesis (the quadratic model) be rejected at the 1% significance level, indicating that the cubic function was more preferable to be accepted. From the sign of the parameters, there existed an inverse N-shape relationship between economic growth and CO2 emissions in China. This demonstrates that as economic develops, CO2 emissions fist decrease, and then rise after the left turning point and it will decline at last when arrive at the right turning point. By calculation, the left turning point was quite low and real GDP per capita was approximately 127 yuan (1997 prices) and the right turning point was approximately 10,201 yuan. These results further confirmed that land urbanization had a positive and significant impact on CO2 emissions in China, implying that China’s urbanization, especially land urbanization, does contribute to CO2 emissions in the long-run.
Table 9 Estimation results in pGDP and CO2 emissions based on the panel OLS estimator
Dependent variable [ln (CO2)] Quadratic model Cubic model
Coefficient t-statistic Prob. Coefficient t-statistic Prob.
ln(pGDP) 0.674 3.054 0.000 -6.336 -2.402 0.017
ln(pGDP)2 -0.003 -0.184 0.854 0.997 2.657 0.008
ln(pGDP)3 - -0.047 -2.666 0.008
ln(BA) 0.293 8.239 0.000 0.286 8.089 0.000
Constant -1.717 -2.247 0.025 14.589 2.368 0.018
Turning point - (127.41, 10201.29)
F-statistic 597.542 0.000 589.035 0.000
Adjusted R2 0.980 0.980
Wald test H0: the quadratic model; H1:the cubic curve
Wald statistic 7.110***

Note: Fixed effect OLS estimator was used. The number of samples was 434. “***” indicates the estimator of a parameter is significant at the 1% level.

Our empirical findings do not support the EKC hypothesis, which is in agreement with previous studies (Wang et al., 2011; Wang et al., 2012a; Du et al., 2012). In addition to the differences as summarized in Table 10, a major difference is that the models we employed are different from those used by Jalil and Mahmud (2009), which may have an impact on the validity of EKC hypothesis. The quadratic model in the regression equations was used in the study by Jalil and Mahmud (2009). More importantly, the studies by Wang et al. (2012a), and Jalil and Mahmud (2009) were based on the time series data while our study performed a panel data analysis using the China’s provincial data. It is generally acknowledged that panel data models have several major advantages over conventional cross-sectional or time series data models (Wang et al., 2014). Panel data models allow controlling for individual heterogeneity, as well as identifying effects that cannot be detected in simple time series or cross-section data (Du et al., 2012). Our results further suggest that the relationship between economic growth and CO2 emissions in China do not support the EKC hypothesis.
Table 10 Comparison with the other studies
Source Data type Method Result
Jalil and Mahmud, 2009 China; Time series (1975-2005) ARDL, quadratic model; VECM; EKC hypothesis Inverted U-shaped, GDP→CO2
Wang et al., 2011 China’s 28 provinces; panel data (1995-2007) Pedroni cointegration; Panel VECM; EKC hypothesis U-shaped curve; GDP→CO2 (long-run)
Du et al., 2012 China’s 28 provinces; panel data (1995-2009) Quadratic and cubic models; EKC hypothesis; GMM estimator Inverted U-shaped is not strongly supported
Wang et al., 2012a Beijing; Time series (1997-2010) STIRPAT; OLS Not support for EKC
This study China’s 31 provinces; panel data (1997-2010) Pedroni cointegration; Panel VECM; EKC hypothesis; OLS; cubic model Long-run: BA↔GDP; BA↔CO2; GDP↔CO2; short-run: GDP→BA; BA→CO2; GDP→CO2; inverted N-shaped curve

Note: ARDL refers to the auto regressive distributed lag; GMM represents the generalized method of moment; OLS is the ordinary least square; and STIRPAT refers to the stochastic impacts by regression on population, affluence and technology. The symbol “↔”, “→” represent the bidirectional and unidirectional Granger causality, respectively.

6 Discussion and conclusions

Based on a balanced panel data of 31 provinces in China over the period 1997-2010, this study used the cointegration and Granger causality analysis to investigate the relationship between urbanization, economic growth, and CO2 emissions at the national and regional scales. Results show that there is long-term equilibrium relationship between land urbanization, economic growth, and CO2 emissions in China between 1997 and 2010, which is supported by Bai et al. (2012). In the long-term, a 1% growth in real GDP per capita accelerates urban expansion by approximately 0.66%-0.93% and accordingly increases CO2 emissions by about 0.60%-0.94% in China. In turn, a 1% expansion in urban built-up area increases real GDP per capita by 0.81%-0.94% and contributes to CO2 emissions by 1.15%-1.19% in China, which is consistent with a previous study by Zhang and Lin (2012). This result is also supported by Ponce de Leon Barido and Marshall (2014), whose result from a panel data of 80 countries for the period 1983-2005 suggested that a 1% increase in urbanization raises CO2 emissions by 0.95%. At the regional level, land urbanization had positive and significant influence on economic growth, showing that a 1% expansion in the build-up area contributes to the increase in per capita income by approximately 1.24%-1.35% in western China, 1.17%-1.91% and 0.99%-1.03% in central and eastern China, respectively. Conversely, economic growth also contributed to the expansion in urban built-up area. Urbanization not only had a significant positive impact on economic growth but also contributed to CO2 emissions in China. This could be explained by the fact that urbanization leads to the accelerated development of public and private transport, consuming more energy and emitting more CO2 (Wang et al., 2012a; Zhang and Lin, 2012). With the development of urbanization, the shift of resident lifestyles may change consumer needs and behaviors, requiring more energy consumption and leading to more CO2 emissions (Zhang and Lin, 2012; Feng et al., 2012). Furthermore, both economic growth and land urbanization also increased CO2 emissions for the three regions. The effect of the former to CO2 emissions in the western region was larger than that in the eastern and central parts, whereas the effect of economic growth to emissions was larger in the eastern region than that in other two regions. China’s rapid economic growth is closely related to the increasing CO2 emission (Feng et al., 2013). Further investigations demonstrate that the growth rate in economic development and urban expansion had a significant and positive impact on CO2 emissions in China but the impact of the former on emissions was greater than that the latter. As the urbanization level has reached a high level of approximately 66% in 2012 in the eastern region, further urbanization may be more difficult, thus its driving force on emissions is expected to be relatively small. To reduce CO2 emissions, we should give up the old pattern of high pollution and energy use in exchange for economic growth. Our findings also suggest that the eastern region should reduce the GDP growth rate and the western region should focus more on the speed of urbanization. It would be necessary to optimize industrial structure, improve energy efficiency, make an appropriate reduction in secondary industrial proportion, and boost the upgrade of high energy consumption industry for eastern China. In the western region, considering the strongest impact of urbanization on CO2 emissions and its abundant renewable resources (i.e., wind, water and solar energy), it is urgent to develop the utilization of renewable energy as an alternative to the traditional fuels. China’s central region is characterized by energy-guzzling heavy industry base, thus promoting industrial restructuring and utilizing clean coal technology would be pivotal for its success of energy-saving and emission-reducing.
The panel Granger causality analysis results suggest that there exist long-run bidirectional causal linkage between land urbanization, economic growth and CO2 emissions in China. A bidirectional long-run causality between land urbanization and economic growth also existed in the eastern and central regions. This indicates that land urbanization does have causal effect on economic growth; and it is not only the consequences of economic growth among provinces in China, but also the drivers of such growth, implying that China’s CO2 emissions would not decrease in a long time period since reducing CO2 emissions may hinder economic growth to some extent. It is not the most feasible measure to reduce CO2 emissions at the expense of sacrificing economic development for China. Instead, more realistic and feasible means for China to reduce CO2 emissions is to control the pace of urbanization process and improve energy structure (Lin and Liu, 2010). In addition, the panel Granger causality analysis revealed bidirectional long-run causalities running: from land urbanization to economic growth and CO2 emissions; and from economic growth to emissions. The heterogeneous panel Granger causality testing further verified the existence of unidirectional short-term causalities running from economic growth to urbanization and from urbanization (or economic growth) to CO2 emissions in China, which are in agreement with previous studies (Jalil and Mahmud, 2009; Wang et al., 2011). At the regional scale, there exist only unidirectional short-term causal nexus running from land urbanization to CO2 emissions and from economic growth to emissions in the eastern and central regions. Further investigations show that there was an inverted N-shaped relationship between CO2 emissions and economic growth in our sample, not supporting the EKC hypothesis. The two turning points of GDP per capita for CO2 emissions were approximately equal to 127 yuan and 10,201 yuan, respectively. China’s per capita income has reached more than 30,000 yuan in 2012 and it has surpassed the left inflection point. We should be wary of the appearance in the next turning point again in energy-saving and emission-reducing in the future.
Urbanization is a dynamic and multidimensional process that caused profound changes in land use, economic structure, ecological and environmental aspects (Bloom et al., 2008; Glaeser, 2011; Bai et al., 2012). To realize the urban dream, China needs to face three policy challenges, i.e., land, people and the environment (Bai et al., 2014). Urbanization will be an important engine of economic growth in China in the future. Blind and excessive land exploitation in the process of rapid urbanization has contributed to the dramatic decrease of the country’s arable land, raising concerns about food security (Gao et al., 2006; Chen et al., 2007; Wang et al., 2012b). As urbanization accelerates, the average annual cropland area used for construction in China has increased drastically from 1.3 million ha in 1990-2000 to more than 2 million ha in 2000-2010, further highlighting the conflict between urbanization and cropland conservation (Liu Jiyuan et al., 2014).
Rapid urbanization has also important effects on labor force, climate, and public health (Zhou et al., 2004; Yang et al., 2011; Gong et al., 2012; Yang, 2013). A majority of farmers have become peasant workers with accompanying processes of industrialization and urbanization from the early 1980s onwards. According to the National Migrant Workers Monitoring Survey Report 2012 issued by the National Bureau of Statistics of China (NBSC), the total number of national migrant workers reached 262.21 million with a growth rate of 3.9% in 2012. Because of the household registration system, many peasant workers cannot gain access to urban medical insurance, education and other public services without adequate social security (Bai et al., 2014). The majority of the rural-to-urban migrants are men and some family members have been left behind in rural communities, which results in a large left-behind population consisting of women, children, and the elderly. It is reported that there were approximately 58 million children, 47 million women and 40 million elderly have been left behind in rural communities by their migrant family members (XNA, 2011). These three left-behind groups have caused societal unrest and psychological development problems for children left behind (Yang, 2013). Furthermore, rapid urbanization has contributed to the warming of mean surface temperature of 0.05℃ per decade in southeast China and 0.04℃ per decade in the eastern region (Zhou et al., 2004; Yang et al., 2011). Meanwhile, urbanization would continue to increase population exposure to major risk factors for disease, especially those that relate to the challenging environmental and social conditions that dominate China’s large cities (Gong et al., 2012). Therefore, more attention should be paid to such negative consequences of rapid urbanization.
Our empirical findings demonstrate that China’s rapid urbanization and economic growth has exerted a positive and significant effect on CO2 emissions, meaning that it might be difficult for China to control urban expansion and reduce CO2 emission without sacrificing economic growth in the long-run. Under its current economic growth model, it is essential for China’s government to control the pace of urbanization process and implement effective policies of environmental protection consistently. China’s economic growth will be slowed down under the background of the current ‘new normal’ economic development, we should seize the opportunity to adjust the industrial structure, improve energy efficiency, slowdown and then gradually decrease CO2 emissions growth.

The authors have declared that no competing interests exist.

References

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Bai Xuemei, Chen Jin, Shi Peijun, 2012. Landscape urbanization and economic growth in China: Positive feed backs and sustainability dilemmas.Environmental Science and Technology, 46(1): 132-139.Accelerating urbanization has been viewed as an important instrument for economic development and reducing regional income disparity in some developing countries, including China. Recent studies (Bloom et al. 2008) indicate that demographic urbanization level has no causal effect on economic growth. However, due to the varying and changing definition of urban population, the use of demographic indicators as a sole representing indicator for urbanization might be misleading. Here, we re-examine the causal relationship between urbanization and economic growth in Chinese cities and provinces in recent decades, using built-up areas as a landscape urbanization indicator. Our analysis shows that (1) larger cities, both in terms of population size and built-up area, and richer cities tend to gain more income, have larger built-up area expansion, and attract more population, than poorer cities or smaller cities; and (2) that there is a long-term bidirectional causality between urban built-up area expansion and GDP per capita at both city and provincial level, and a short-term bidirectional causality at provincial level, revealing a positive feedback between landscape urbanization and urban and regional economic growth in China. Our results suggest that urbanization, if measured by a landscape indicator, does have causal effect on economic growth in China, both within the city and with spillover effect to the region, and that urban land expansion is not only the consequences of economic growth in cities, but also drivers of such growth. The results also suggest that under its current economic growth model, it might be difficult for China to control urban expansion without sacrificing economic growth, and China's policy to stop the loss of agricultural land, for food security, might be challenged by its policy to promote economic growth through urbanization.

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Bai Xuemei, Shi Peijun, Liu Yansui, 2014. Realizing China’s urban dream.Nature, 509: 158-160.

3
Bloom D E, Canning D, Fink G, 2008. Urbanization and the wealth of nations.Science, 319(5864): 772-775.The proportion of a country's population living in urban areas is highly correlated with its level of income. Urban areas offer economies of scale and richer market structures, and there is strong evidence that workers in urban areas are individually more productive, and earn more, than rural workers. However, rapid urbanization is also associated with crowding, environmental degradation, and other impediments to productivity. Overall, we find no evidence that the level of urbanization affects the rate of economic growth. Our findings weaken the rationale for either encouraging or discouraging urbanization as part of a strategy for economic growth.

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4
Chen Jie, 2007. Rapid urbanization in China: A real challenge to soil protection and food security.Catena, 69(1): 1-15.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">To feed its 1.3&nbsp;billion population with a per capita cultivated land far below the world average, China is already facing a great challenge of land scarcity. Accelerated urbanization along with explosive economic growth has further worsened the shortage of agricultural land over the last two decades. Increasing concern over land is expressed in terms of soil availability for grain production and soil quality degradation. Based on official statistics and data derived from satellite imagery, dynamics of China's cultivated land over the past two decades is outlined and the causes and destinations of cultivated land loss are analyzed in this paper. Particularly, urbanization-related land-use changes and their spatial variation across the country are demonstrated. Furthermore, impacts of urbanization and associated waste disposals, consequent shifts of soil utilization on areal soil quality are expatiated. It is initially concluded that China's cultivated land is shrinking at a rather shocking rate. Although conversion to urban and industrial uses took up a comparatively small share of total cultivated land loss, urbanization should still be considered as a great threat to future agricultural production for several reasons. Urbanization is increasing the risk of soil pollution through waste disposal and acid deposition derived from urban air pollution. Facing rapid urbanization, China is making positive policy responses to the challenge of decreasing availability of cultivated land and offering unremitting efforts towards the goal of national food security.</p>

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5
Chen Mingxing, Huang Yongbin, Tang Zhipenget al., 2014. The provincial pattern of the relationship between urbanization and economic development in China.Journal of Geographical Sciences, 24(1): 33-45.lt;p>Understanding the relationship between China's urbanization and economic development on a provincial scale is of profound theoretical and practical significance. Based on data from 124 countries or regions throughout the world and 31 provinces or autonomous regions in China, applying improved methods using the quadrant map approach, this paper analyzed the spatial pattern of the relationship between China's urbanization and economic development level. The study identified the following results. (1) The 31 province-level regions fall into six categories: only one region is in the category of sharp over-urbanization, 3 regions are in medium over-urbanization, 11 slight over-urbanization, 8 basic coordination, one medium under-urbanization, and seven slight under-urbanization. (2) There are significant regional differences on a provincial scale in the relationships between urbanization and the level of economic development. (3) The provincial pattern of urbanization and economic development is significantly different between east and west. The eastern coastal areas are mainly over-urbanized, while the central and western regions are mainly under-urbanized. (4) The relationship between urbanization and the level of economic development is similar to the Matthew effect. Hence, two important insights are proposed. First, the phenomenon of over-urbanization in some developed regions should be viewed with some concern and vigilance. Second, urbanization needs to be speeded up moderately in the central and western regions.</p>

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Chen Mingxing, Liu Weidong, Tao Xiaoli, 2013. Evolution and assessment on China’s urbanization 1960-2010: Under-urbanization or over-urbanization?Habitat International, 38: 25-33.There has been a significant transformation in the urbanization and economic growth in post-reform China. The nature and degree of urbanization is a subject of some controversy. This paper examines empirical data for 110 counties and employs a quadrant plots method involving estimation of several parameters to analyze empirically the evolvement of urbanization in China during 1960-2010. There are three periods of China's urbanization in the pattern of world, which is the rapid decline stage (1960-1978), the stable stage of ascension (1979-1995) and rapid promotion stage (1996-2010). Over the entire period, compared to the rest of the world, urbanization and economic growth in China appears to be coordinated and at moderate levels. However, China's urbanization process has progressed faster than economic growth since 2004, and it is right time that China should rethink under-urbanization and it's countermeasure in development strategy. And the core of new stage of urbanization is to improve the quality of urbanization and to take little count of urbanization quantity. (C) 2012 Elsevier Ltd. All rights reserved.

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Cheng Yeqing, Wang Zheye, Zhang Shuozhiet al., 2013. Spatial econometric analysis of carbon emission intensity and its driving factors from energy consumption in China.Acta Geographica Sinica, 68(1): 1418-1431. (in Chinese)The economic and social development has been facing with serious challenge brought by global climate change due to carbon emissions. As a responsible developing country, China pledged to reduce its carbon emission intensity by 40%- 45% below 2005levels by 2020. The realization of this target depends on not only the substantive transition of society, economy and industrial structure in national scale, but also the specific action and share of energy saving and emissions reduction in provincial scale. Based on the method provided by the IPCC, this paper examines the spatio-temporal dynamic patterns and domain factors of China's carbon emission intensity from energy consumption in 1997- 2010 using spatial autocorrelation analysis and spatial panel econometric model. The aim is to provide scientific basis for making different policies on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China's carbon emissions increased from 4.16 Gt to 11.29 Gt in 1997-2010, with an annual rate of 7.15%, which was much slower than that of annual growth rate of GDP(11.72%); therefore, China's carbon emission intensity tended to decline. Secondly, the changing curve of Moran's I indicated that China's carbon emission intensity from energy consumption has a continued strengthening tendency of spatial agglomeration at provincial scale. The provinces with higher and lower values appeared to be path-dependent or space-locked to some extent. Third, according to the analysis of spatial panel econometric model, it can be found that energy intensity, energy structure, industrial structure and urbanization rate were the domain factors that have impact on the spatio- temporal patterns of China's carbon emission intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, we should improve the utilizing efficiency of energy, and optimize energy and industrial structure, and choose the low- carbon urbanization way and implement regional cooperation strategy of energy conservation and emissions reduction.

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Choi I, 2001. Unit root tests for panel data.Journal of International Money and Finance, 20: 249-272.

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Cole M A, Neumayer E, 2004. Examining the impact of demographic factors on air pollution.Population and Environment, 26(1): 5-21.<a name="Abs1"></a>This study adds to the emerging literature examining empirically the link between population size, other demographic factors and pollution. We contribute by using more reliable estimation techniques and examine two air pollutants. By considering sulfur dioxide, we become the first study to explicitly examine the impact of demographic factors on a pollutant other than carbon dioxide at the cross-national level. We also take into account the urbanization rate and the average household size neglected by many prior cross-national econometric studies. For carbon dioxide emissions we find evidence that population increases are matched by proportional increases in emissions while a higher urbanization rate and lower average household size increase emissions. For sulfur dioxide emissions, we find a U-shaped relationship, with the population-emissions elasticity rising at higher population levels. Urbanization and average household size are not found to be significant determinants of sulfur dioxide emissions. For both pollutants, our results suggest that an increasing share of global emissions will be accounted for by developing countries. Implications for the environmental Kuznets curve literature are described and directions for further work identified.

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Du Limin, Chu Wei, Cai Shenghua, 2012. Economic development and carbon dioxide emissions in China: Provincial panel data analysis.China Economic Review, 23(2): 371-384.This paper investigates the driving forces, emission trends and reduction potential of China's carbon dioxide (CO 2 ) emissions based on a provincial panel data set covering the years 1995 to 2009. A series of static and dynamic panel data models are estimated, and then an optimal forecasting model selected by out-of-sample criteria is used to forecast the emission trend and reduction potential up to 2020. The estimation results show that economic development, technology progress and industry structure are the most important factors affecting China's CO 2 emissions, while the impacts of energy consumption structure, trade openness and urbanization level are negligible. The inverted U-shaped relationship between per capita CO 2 emissions and economic development level is not strongly supported by the estimation results. The impact of capital adjustment speed is significant. Scenario simulations further show that per capita and aggregate CO 2 emissions of China will increase continuously up to 2020 under any of the three scenarios developed in this study, but the reduction potential is large.

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Dumitrescu E I, Hurlin C, 2012. Testing for Granger non-causality in heterogeneous panels.Ecological Modeling, 29(4): 1450-1460.Downloadable ! Author(s): Christophe Hurlin & Elena Dumitrescu. 2012 Abstract: This paper proposes a very simple test of Granger (1969) non-causality for hetero- geneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units. First, this statistic is shown to converge sequentially to a standard normal distribution. Second, the semi- asymptotic distribution of the average statistic is characterized for a fixed T sample. A standardized statistic based on an approximation of the moments of Wald statistics is hence proposed. Third, Monte Carlo experiments show that our standardized panel statistics have very good small sample properties, even in the presence of cross-sectional dependence.

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Feng Kuishuang, Davis S J, Sun Laixianget al., 2013. Outsourcing CO2 within China.Proceedings of the National Academy of Sciences of the United States of America, 110(28): 11654-11659.

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Feng Kuishuang, Siu Y L, Guan Daboet al., 2012. Analyzing drivers of regional carbon dioxide emissions for China.Journal of Industrial Ecology, 16(4): 600-611.Summary China faces the challenge of balancing unprecedented economic growth and environmental sustainability. Rather than a homogenous country that can be analyzed at the national level, China is a vast country with significant regional differences in physical geography, regional economy, demographics, industry structure, and household consumption patterns. There are pronounced differences between the much-developed Eastern-Coastal economic zone and the less developed Central and Western economic zones in China. Such variations lead to large regional discrepancies in carbon dioxide (CO 2 ) emissions. Using the 28 regional input-output tables of China for 2002 and 2007 and structural decomposition analysis (SDA), we analyze how changes in population, technology, economic structure, urbanization, and household consumption patterns drive regional CO 2 emissions. The results show a significant gap between the three economic zones in terms of CO 2 emission intensity, as the Eastern-Coastal zone possesses more advanced production technologies compared to the Central and Western zones. The most polluting sectors and largest companies are state-owned enterprises and thus are potentially able to speed up knowledge transfer between companies and regions. The &ldquo;greening&rdquo; of the more developed areas is not only a result of superior technology, but also of externalizing production and pollution to the poorer regions in China. The results also show that urbanization and associated income and lifestyle changes were important driving forces for the growth of CO 2 emissions in most regions in China. Therefore, focusing on technology and efficiency alone is not sufficient to curb regional CO 2 emissions.

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Gao Jay, Liu Yansui, Chen Yifu, 2006. Land cover changes during agrarian restructuring in Northeast China.Applied Geography, 26(3): 312-322.land use/cover change; land use policy; agrarian restructuring; Northeast China

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Glaeser E, 2011. Cities, productivity, and quality of life.Science, 333(6042): 592-594.Technological changes and improved electronic communications seem, paradoxically, to be making cities more, rather than less, important. There is a strong correlation between urbanization and economic across countries, and within-country evidence suggests that productivity rises in dense agglomerations. But urban economic advantages are often offset by the perennial urban curses of crime, congestion and . The past history of the developed world suggests that these problems require more capable governments that use a combination of economic and engineering solutions. Though the scope of urban challenges can make remaining rural seem attractive, agrarian poverty has typically also been quite costly.

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Gong Peng, Liang Song, Carlton E Jet al., 2012. Urbanisation and health in China.The Lancet, 379(9818): 843-852.China has seen the largest human migration in history, and the country's rapid urbanisation has important consequences for public health. A provincial analysis of its urbanisation trends shows shifting and accelerating rural-to-urban migration across the country and accompanying rapid increases in city size and population. The growing disease burden in urban areas attributable to nutrition and lifestyle choices is a major public health challenge, as are troubling disparities in health-care access, vaccination coverage, and accidents and injuries in China's rural-to-urban migrant population. Urban environmental quality, including air and water pollution, contributes to disease both in urban and in rural areas, and traffic-related accidents pose a major public health threat as the country becomes increasingly motorised. To address the health challenges and maximise the benefits that accompany this rapid urbanisation, innovative health policies focused on the needs of migrants and research that could close knowledge gaps on urban population exposures are needed.

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17
Grossman G M, Krueger A B, 1995. Economic growth and the environment.Quarterly Journal of Economics, 110: 353-377.As the discipline of economics has developed over the last two hundred years, there have been significant changes in analytic method but a remarkable continuity of the key issues which have been addressed. The role of the environment as a constraint on economic growth has been one such issue. This was given wide public prominence by Thomas Malthus in his Essay on Population , first published in 1798. The essence of his argument is one that still appears in policy debates on the future of the planet. The human race, like other biological populations has the capacity to grow at a compound rate. Population growth is ultimately halted by the limited supply of fertile land and the failure of food production to match the growth of population. Where lack of foresight results in population growth beyond food supply, real wages fall, and ultimately death from disease and famine will reduce population to the level where the living can just survive on a bare subsistence income. Economics, with some justice, earned the title of ‘the dismal science’.

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Guan Dabo, Liu Zhu, Geng Yonget al., 2012. The gigatonne gap in China’s carbon dioxide inventories.Nature Climate Change, 2(9): 672-675.

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Im K S, Pesaran M H, Shin Y, 2003. Testing for unit roots in heterogeneous panels.Journal of Econometrics, 109: 53-74.By M.H. Pasaran, K.S. Im and Yongcheol Shin; Testing for Unit Roots in Heterogeneous Panels

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International Energy Agency (IEA), 2012. Key world energy statistics international energy agency. Statistics Division, Paris.

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Jalil A, Mahmud S F, 2009. Environment Kuznets curve for CO2 emissions: A cointegration analysis for China.Energy Policy, 37(12): 5167-5172.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">This study examines the long-run relationship between carbon emissions and energy consumption, income and foreign trade in the case of China by employing time series data of 1975&ndash;2005. In particular the study aims at testing whether environmental Kuznets curve (EKC) relationship between CO<sub>2</sub> emissions and per capita real GDP holds in the long run or not. Auto regressive distributed lag (ARDL) methodology is employed for empirical analysis. A quadratic relationship between income and CO<sub>2</sub> emission has been found for the sample period, supporting EKC relationship. The results of Granger causality tests indicate one way causality runs through economic growth to CO<sub>2</sub> emissions. The results of this study also indicate that the carbon emissions are mainly determined by income and energy consumption in the long run. Trade has a positive but statistically insignificant impact on CO<sub>2</sub> emissions.</p>

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Levin A, Lin C, Chu C, 2002. Unit root tests in panel data: Asymptotic and finite-sample properties.Journal of Econometrics, 108: 1-24.We consider pooling cross-section time series data for testing the unit root hypothesis. The degree of persistence in individual regression error, the intercept and trend coefficient are allowed to vary freely across individuals. As both the cross-section and time series dimensions of the panel grow large, the pooled t -statistic has a limiting normal distribution that depends on the regression specification but is free from nuisance parameters. Monte Carlo simulations indicate that the asymptotic results provide a good approximation to the test statistics in panels of moderate size, and that the power of the panel-based unit root test is dramatically higher, compared to performing a separate unit root test for each individual time series.

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Li Fei, Dong Suocheng, Xue Liet al., 2011. Energy consumption-economic growth relationship and carbon dioxide emissions in China.Energy Policy, 39(2): 568-574.This paper applies the panel unit root, heterogeneous panel cointegration and panel-based dynamic OLS to re-investigate the co-movement and relationship between energy consumption and economic growth for 30 provinces in mainland China from 1985 to 2007. The empirical results show that there is a positive long-run cointegrated relationship between real GDP per capita and energy consumption variables. Furthermore, we investigate two cross-regional groups, namely the east China and west China groups, and get more important results and implications. In the long-term, a 1% increase in real GDP per capita increases the consumption of energy by approximately 0.48-0.50% and accordingly increases the carbon dioxide emissions by about 0.41-0.43% in China. The economic growth in east China is energy-dependent to a great extent, and the income elasticity of energy consumption in east China is over 2 times that of the west China. At present, China is subject to tremendous pressures for mitigating climate change issues. It is possible that the GDP per capita elasticity of carbon dioxide emissions would be controlled in a range from 0.2 to 0.3 by the great effort. (C) 2010 Elsevier Ltd. All rights reserved.

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Lin Boqiang, Liu Xiying, 2010. China’s carbon dioxide emissions under the urbanization process: Influence factors and abatement policies.Economic Research Journal, 8: 66-78. (in Chinese)

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Liu Jiyuan, Kuang Wenhui, Zhang Zengxianget al., 2014. Spatiotemporal characteristics, patterns, and causes of land-use changes in China since the late 1980s.Journal of Geographical Sciences, 24(2): 195-210.lt;p>Land-use/land-cover changes (LUCCs) have links to both human and nature interactions. China's Land-Use/cover Datasets (CLUDs) were updated regularly at 5-year intervals from the late 1980s to 2010,with standard procedures based on Landsat TM\ETM+ images. A land-use dynamic regionalization method was proposed to analyze major land-use conversions. The spatiotemporal characteristics,differences,and causes of land-use changes at a national scale were then examined. The main findings are summarized as follows. Land-use changes (LUCs) across China indicated a significant variation in spatial and temporal characteristics in the last 20 years (1990-2010). The area of cropland change decreased in the south and increased in the north,but the total area remained almost unchanged. The reclaimed cropland was shifted from the northeast to the northwest. The built-up lands expanded rapidly,were mainly distributed in the east,and gradually spread out to central and western China. Woodland decreased first,and then increased,but desert area was the opposite. Grassland continued decreasing. Different spatial patterns of LUC in China were found between the late 20th century and the early 21st century. The original 13 LUC zones were replaced by 15 units with changes of boundaries in some zones. The main spatial characteristics of these changes included (1) an accelerated expansion of built-up land in the Huang-Huai-Hai region,the southeastern coastal areas,the midstream area of the Yangtze River,and the Sichuan Basin;(2) shifted land reclamation in the north from northeast China and eastern Inner Mongolia to the oasis agricultural areas in northwest China;(3) continuous transformation from rain-fed farmlands in northeast China to paddy fields;and (4) effectiveness of the &quot;Grain for Green&quot; project in the southern agricultural-pastoral ecotones of Inner Mongolia,the Loess Plateau,and southwestern mountainous areas. In the last two decades,although climate change in the north affected the change in cropland,policy regulation and economic driving forces were still the primary causes of LUC across China. During the first decade of the 21st century,the anthropogenic factors that drove variations in land-use patterns have shifted the emphasis from one-way land development to both development and conservation.The &quot;dynamic regionalization method&quot; was used to analyze changes in the spatial patterns of zoning boundaries,the internal characteristics of zones,and the growth and decrease of units. The results revealed &quot;the pattern of the change process,&quot; namely the process of LUC and regional differences in characteristics at different stages. The growth and decrease of zones during this dynamic LUC zoning,variations in unit boundaries,and the characteristics of change intensities between the former and latter decades were examined. The patterns of alternative transformation between the &quot;pattern&quot; and &quot;process&quot; of land use and the causes for changes in different types and different regions of land use were explored.</p>

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Liu Jianguo, Diamond J, 2005. China’s environment in a globalizing world.Nature, 435: 1179-1186.

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Liu Yansui, Fang Fang, Li Yuheng, 2014. Key issues of land use in China and implications for policy making.Land Use Policy, 40: 6-12.The paper aims to comprehensively analyze key issues of current land use in China. It identifies the major land-use problems when China is undergoing rapid urbanization. Then, the paper interprets and assesses the related land-use policies: requisition-compensation balance of arable land, increasing vs. decreasing balance of urban-rural built land, reserved land system within land requisition, rural land consolidation and economical and intensive land use. The paper finds that current policies are targeting specific problems while being implemented in parallel. There is lacking a framework that incorporates all the policies. The paper finally indicates the current land-use challenges and proposes strategic land-use policy system to guide sustainable land use in the future. (C) 2013 Elsevier Ltd. All rights reserved.

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Liu Yansui, Lu Shasha, Chen Yufu, 2013. Spatio-temporal change of urban-rural equalized development patterns in China and its driving factors.Journal of Rural Studies, 32: 320-330.The urban–rural equalized development is not only significant theoretically, but also a strategic challenge facing the coordinated development of urban and rural China. In this paper we put forward an innovative theory of URED against the background of China's urban–rural transformation. The spatio-temporal pattern, its change and driving factors of urban–rural equalized development during 1996–2009 were analyzed using principal component analysis, the Markov chain model and exploratory spatial data analysis model based on the data for 31 Chinese provinces (autonomous regions and municipalities). It is found that during the study period URED exhibited an obvious tendency of “club homogenization” in China. However, since 2003 the homogenization of the URED for entire China has weakened. Moreover, URED showed a significant geographic characteristic of “polarization” during 1996–2003. Namely, the spatial units of a high URED level were concentrated in eastern China near the coast, and the spatial units of a low URED level were located mainly in central and western China. However, this spatial polarized structure of URED was destroyed since 2003, and the spatial disparity at the provincial level has decreased. Finally, it is concluded that policies and institutional structure, economic growth and urbanization were the main driving factors of the identified URED spatio-temporal pattern and its change in China. This study may serve as a scientific reference regarding decision-making in coordinating urban and rural development and in constructing the new countryside of China.

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Liu Yansui, Wang Lijuan, Long Hualou, 2008. Spatio-temporal analysis of land-use conversion in the eastern coastal China during 1996-2005.Journal of Geographical Sciences, 18(3): 274-282.lt;a name="Abs1"></a>Based on the acquaintance of the regional background of urban-rural transformational development and investigations on the spot, this paper discusses the holistic situation, dominant factors and mechanism of arable land loss and land for construction occupation in the coastal area of China over the last decade, with the aid of GIS technology. Conclusions of the research are summarized as follows: (1) the arable land had been continuously decreasing from 1996 to 2005, with a loss of 1,708,700 hm<sup>2</sup> and an average decrement of 170,900 hm<sup>2</sup> per year; (2) land for construction increased 1,373,700 hm<sup>2</sup> with an average increment of 153,200 hm<sup>2</sup> per year; (3) total area of encroachment on arable land for construction between 1996 and 2005 was 1,053,100 hm<sup>2</sup> accounting for 34.03% of the arable land loss in the same period, the percentages of which used for industrial land (INL), transportation land (TRL), rural construction land (RUL) and town construction land (TOL) are 45.03%, 15.8%, 15.47% and 11.5%, respectively; and (4) the fluctuation of the increase of construction land and encroachment on arable land in the area were deeply influenced by the nation&#8217;s macroscopic land-use policies and development level of regional economy. The growth of population and advancement of technology promoted the rapid industrialization, construction of transportation infrastructures, rural urbanization and expansion of rural settlements in the eastern coastal area, and therefore were the primary driving forces of land-use conversion.

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Liu Yansui, Yang Ren, 2012. The spatial characteristics and formation mechanism of the county urbanization in China.Acta Geographica Sincia, 67(8): 1011-1020. (in Chinese)The spatial and temporal characteristics and the formation mechanism of the county urbanization in China since 1990 were analyzed systematically,using the methods including regional differences,transect and geography detectors. Results show that the temporal and spatial differences of the county urbanization were significant. The "herringbone" shape region pattern of high county urbanization was gradually highlighted,which were made by the counties along the north border and in eastern coastal areas. The county urbanization process of some regions were accelerated and enhanced,including Wuhan metropolitan region,Chengdu-Chongqing region and Guanzhong-Tianshui region. The low county urbanization level was maintained in Southwest China and Qinghai-Tibet Plateau regions. The differences of urbanization and the change rate of county urbanization were converged in China after 2000,but the rate has slowed down since 2000. The county urbanization trend of transects were significantly different,including Lianyungang-Lanzhou railway and Lanzhou-Urumqi railway transects,the Yangtze River transect,the border of north China transect,106 National Road transect,and the eastern coastal transect. There are many factors affecting county urbanization,mainly including economic development stage,the level of secondary and tertiary industries,rural net income per capita,population density,leading position of grain production,demographic statistics and special arrangements for counties. The high county urbanization in northern border regions was a typical type of statistical unrealistically high urbanization. In the future county urbanization development should follow the geographical differences,highlight its leading function,and adopt multiple urbanization development models such as promoting urbanization intensively in key urban economic development areas,separating urbanization in cropland and grain producing areas,migrating urbanization in ecological and water resource protection areas,suburban areas and urban-based urbanization and other leading county urbanization patterns.

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Liu Yansui, Zhou Yang, Wu Wenxiang, 2015. Assessing the impact of population, income and technology on energy consumption and industrial pollutant emissions in China.Applied Energy, 155: 904-917.Elucidating the complex mechanism of the impact of demographic changes, economic growth, and technological advance impacts on energy consumption and pollutant emissions is fundamentally necessary to inform effective strategies on energy saving and emission reduction in China. Here, based on a balanced provincial panel dataset in China over the period 1990-2012, we used an extended STIRPAT model to investigate the effects of human activity on energy consumption and three types of industrial pollutant emissions (exhaust gases, waste water and solid waste) at the national and regional levels and tested the environmental Kuznets curve (EKC) hypothesis. Empirical results show that a higher population density would result in a decrease in energy consumption in China as a whole and in its eastern, central and western regions, but the extent of its effect on the environment depends on the type of pollutants. Higher population density increased wastewater discharge but decreased solid waste production in China and its three regions. The effect of economic development on the environment was heterogeneous across the regions. The proportion of industrial output had a significant and positive influence on energy consumption and pollutant emissions in China and its three regions. Higher industrial energy intensity resulted in higher levels of pollutant emissions. No strong evidence supporting the EKC hypothesis for the three industrial wastes in China was found. Our findings further demonstrated that the impact of population, income and technology on the environment varies at different levels of development. Because of the regional disparities in anthropogenic impact on the environment, formulating specific region-oriented energy saving and emission reduction strategies may provide a more practical and effective approach to achieving sustainable development in China.

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Long Hualou, Zou Jian, Liu Yansui, 2011. Analysis of rural transformation development in China since the turn of the new millennium.Applied Geography, 31(3): 1094-1105.Since the turn of the new millennium, the Chinese central government has focused significant attention on substantially improving rural residents' well-being and achieving the coordinated development of urban and rural areas. This paper examines China's rural transformation development based on three assessing indicator systems (the rural development level, the rural transformation level, and the urban-rural coordination level), using government socioeconomic data from 2000 to 2008. Spatial and statistical analyses, supported by SPSS 13 and ArcGIS 9.2 software, show that rural China has experienced universal and intense transformative development since 2000. China's urban-rural coordination development declined greatly between 2000 and 2008. Our analysis shows that rural transformation development that corresponds to a certain rural development level will lead to the effective development of regional rural systems and an improved urban-rural relationship. This paper suggests that more attention needs to be paid to the powerful factors that fuel rural transformation development, especially in coastal China, to coordinate urban-rural development under the pressure of rapid industrialization and urbanization in the new century. Given the multiscale nature of regional inequalities in rural transformation development, improving rural development policies aimed at various rural transformation development types might be the most effective way to shape a more coordinated urban-rural development pattern in China. (C) 2011 Elsevier Ltd. All rights reserved.

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Maddala G S, Wu Shaowen, 1999. Comparative study of unit root tests with panel data and a new simple test.Oxford Bulletin of Economics and Statistics, 61: 631-652.The panel data unit root test suggested by Levin and Lin (LL) has been widely used in several applications, notably in papers on tests of the purchasing power parity hypothesis. This test is based on a very restrictive hypothesis which is rarely ever of interest in practice. The Im–Pesaran–Shin (IPS) test relaxes the restrictive assumption of the LL test. This paper argues that although the IPS test has been offered as a generalization of the LL test, it is best viewed as a test for summarizing the evidence from a number of independent tests of the sample hypothesis. This problem has a long statistical history going back to R. A. Fisher. This paper suggests the Fisher test as a panel data unit root test, compares it with the LL and IPS tests, and the Bonferroni bounds test which is valid for correlated tests. Overall, the evidence points to the Fisher test with bootstrap-based critical values as the preferred choice. We also suggest the use of the Fisher test for testing stationarity as the null and also in testing for cointegration in panel data.

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Mahadevan R, Asafu-Adjaye J, 2007. Energy consumption, economic growth and prices: A reassessment using panel VECM for developed and developing countries.Energy Policy, 35(4): 2481-2490.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">This paper reinvestigates the energy consumption&ndash;GDP growth nexus in a panel error correction model using data on 20 net energy importers and exporters from 1971 to 2002. Among the energy exporters, there was bidirectional causality between economic growth and energy consumption in the developed countries in both the short and long run, while in the developing countries energy consumption stimulates growth only in the short run. The former result is also found for energy importers and the latter result exists only for the developed countries within this category. In addition, compared to the developing countries, the developed countries&rsquo; elasticity response in terms of economic growth from an increase in energy consumption is larger although its income elasticity is lower and less than unitary. Lastly, the implications for energy policy calling for a more holistic approach are discussed.</p>

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McCoskey S, Kao C, 1999. Comparing panel data cointegration tests with an application of the twin deficits problem. Mimeo.

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Minx J C, Baiocchi G, Peters G Pet al., 2011. A “carbonizing dragon”: China’s fast growing CO2 emissions revisited.Environmental Science and Technology, 45(21): 9144-9153.

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National Bureau of Statistics of China (NBSC), 2011. China Statistical Yearbook 2011. Beijing: China Statistics Press.

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Pedroni P, 2000. Fully modified OLS for heterogeneous cointegrated panels.Advances in Econometrics, 15: 93-130.This chapter uses fully modified OLS principles to develop new methods for estimating and testing hypotheses for cointegrating vectors in dynamic panels in a manner that is consistent with the degree of cross sectional heterogeneity that has been permitted in recent panel unit root and panel cointegration studies. The asymptotic properties of various estimators are compared based on pooling along the 00‘within00’ and 00‘between00’ dimensions of the panel. By using Monte Carlo simulations to study the small sample properties, the group mean estimator is shown to behave well even in relatively small samples under a variety of scenarios.

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Pedroni P, 2004. Panel cointegration: Asymptotic and finite sample properties of pooled time series tests with an application to the PPP hypothesis.Econometric Theory, 20: 597-625.

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Peters G P, Weber C L, Guan Daboet al., 2007. China’s growing CO2 emissions: A race between increasing consumption and efficiency gains.Environmental Science and Technology, 41(17): 5939-5944.

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Ponce de Leon Barido D, Marshall J D, 2014. Relationship between urbanization and CO2 emissions depends on income level and policy.Environmental Science and Technology, 48(7): 3632-3639.We investigate empirically how national-level CO2 emissions are affected by urbanization and environmental policy. We use statistical modeling to explore panel data on annual CO2 emissions from 80 countries for the period 1983-2005. Random- and fixed-effects models indicate that, on the global average, the urbanization-emission elasticity value is 0.95 (i.e., a 1% increase in urbanization correlates with a 0.95% increase in emissions). Several regions display a statistically significant, positive elasticity for fixed- and random-effects models: lower-income Europe, India and the Sub-Continent, Latin America, and Africa. Using two proxies for environmental policy/outcomes (ratification status for the Kyoto Protocol; the Yale Environmental Performance Index), we find that in countries with stronger environmental policy/outcomes, urbanization has a more beneficial (or, a less negative) impact on emissions. Specifically, elasticity values are -1.1 (0.21) for higher-income (lower-income) countries with strong environmental policy, versus 0.65 (1.3) for higher-income (lower-income) countries with weak environmental policies. Our finding that the urbanization-emissions elasticity may depend on the strength of a country's environmental policy, not just marginal increases in income, is in contrast to the idea of universal urban scaling laws that can ignore local context. Most global population growth in the coming decades is expected to occur in urban areas of lower-income countries, which underscores the importance of these findings.

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Poumanyvong P, Kaneko S, 2010. Does urbanization lead to less energy use and lower CO2 emissions? A cross-country analysis.Ecological Economics, 70(2): 434-444.Despite the relationship between urbanization, energy use and CO 2 emissions has been extensively studied in recent years, little attention has been paid to differences in development stages or income levels. Most previous studies have implicitly assumed that the impact of urbanization is homogenous for all countries. This assumption can be questionable as there are many characteristic differences among countries of different levels of affluence. This paper investigates empirically the effects of urbanization on energy use and CO 2 emissions with consideration of the different development stages. Using the Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model and a balanced panel dataset of 99 countries over the period 1975-2005, the findings suggest that the impact of urbanization on energy use and emissions varies across the stages of development. Surprisingly, urbanization decreases energy use in the low-income group, while it increases energy use in the middle- and high-income groups. The impact of urbanization on emissions is positive for all the income groups, but it is more pronounced in the middle-income group than in the other income groups. These novel findings not only help advance the existing literature, but also can be of special interest to policy makers and urban planners.

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Raupach M R, Marland G, Ciais Pet al., 2007. Global and regional drivers of accelerating CO2 emissions.Proceedings of the National Academy of the Sciences of the United States of America, 104(24): 10288-10293.

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Song Tao, Zheng Tingguo, Tong Lianjun, 2008. An empirical test of the environmental Kuznets curve in China: A panel cointegration approach.China Economic Review, 19(3): 381-392.<h2 class="secHeading" id="section_abstract">Abstract</h2><p id="">This paper investigates the relationship between environmental pollution and economic growth in China based on the environmental Kuznets curve hypothesis, using Chinese provincial data over 1985&ndash;2005. Waste gas, waste water and solid wastes are used as environmental indicators and GDP is used as the economic indicator. It is found by panel cointegration test that there is a long-run cointegrating relationship between the per capita emission of three pollutants and the per capita GDP. According to comparisons with the dynamic OLS estimator and the Within OLS estimator, we find that panel cointegration estimation is preferable for all pollutants except for solid wastes. The results also show that all three pollutants are inverse U-shaped, and water pollution has been improved earlier than gas pollution and solid pollution.</p>

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Sun Changlong, Jin Nuo, Zhang Xiaoleiet al., 2013. The impact of urbanization on the CO2 emission in the various development states.Scientia Geographica Sincia, 33(3): 266-272. (in Chinese)Urbanization and carbon emissions have attracted intensively attention.This article analyzes the impacts of the urbanization on CO2 emissions in three aspects.Then the historical data of 76 countries(regions)(1980-2007) are employed by separating them into different categories according to urbanization levels to study the correlation between the stage of urbanization and the dynamic evolution of the CO2emissions.The main impact factors on CO2emissions during different stages of urbanization are evaluated based on the STIRPAT model.Finally,according to analysis results,combined with the characteristics of the various elements in the development stage of urbanization,this study explores the impacts of various stages of urbanization on CO2 emissions.The results showed that: the urbanization affects the CO2 emissions mainly through changing lifestyle,production and land use,which performances a driving and restricting role.In the early stage of urbanization,the driving and restricting effect of the CO2emissions are not obvious,showing slow development in urban system and the slow growing carbon emissions rate;in the mid-stage,the driving role of urbanization on CO2emissions becomes to be dominant,but the restricting effect is weaker,which is characterized by the acceleration development of urbanization system,the city development in numbers and scales,the accelerated industrialization process,and a rapid growth of CO2 emissions;in latter stage,the city enters a slow phase of development,urbanization on the driving role of CO2emissions is still dominant,but the restricting effect is gradually enhancing.In this stage,the quality of urbanization continuously improves,and household consumption reaches a high level.Meanwhile,the phenomenon of suburban urbanization and counter urbanization appear,the tertiary industry takes up most part of industrial structure,technical level increases rapidly,CO2emissions becomes slow,but the total emissions are still increasing.Therefore,the impact of urbanization on CO2emissions is different in various urbanization stages.In response to CO2 emission reduction mandate in the context of global climate change,countries(regions) should reasonably guide the process of urbanization and enhance the restricting effect of urbanization.

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Wang Jing, Chen Yongqi, Shao Xiaomeiet al., 2012b. Land-use changes and policy dimension driving forces in China: Present, trend and future.Land Use Policy, 29(4): 737-749.China has extremely scarce land resources compared to the world average. There is an urgent need for studies of the current situation and the trends in land-use change and assessment of the performance of land policies in China. Assessment of land-use change has long been hindered by a lack of accurate and reliable data. This paper uses the data obtained from the national land surveys of 1996 and land-use change surveys from 1997 to 2008, to analyze changes in land use and the policy dimension driving forces related to the changes, especially cultivated land, forestry land, grassland, as well as developed land. The aim of this analysis will be to derive the physical, social and economical driving forces of those changes to grasp the trends in land-use change and the effects of land policies and to formulate strategies for the protection and sustainable use of agricultural land. The results indicate that, although the overall change in land use was not large, cultivated land was significantly reduced and developed land rapidly increased. A great deal of high quality cultivated land was changed to developed land and low quality cultivated land generated from unused land, which has resulted in a serious threat to food supplies in China. Predictions using the methods of linear extrapolation and a BP neural network indicate that it is impossible to keep to a target of 0.12 billion hectares of cultivated land in the future under the mode of economic development used between 1996 and 2008. The results also indicate that the implementation of the laws and regulations about controlling the developed land and preserving cultivated land had significant effects on changes in land use, especially cultivated land and developed land. The results suggest that the changes in land use are closely related to economic fluctuation and the enaction and implementation of these land policies had a little time lag for cultivated land protection. There is a pressing need for China to use its limited land resources more efficiently and effectively by enacting or re-enforcing the laws and regulations on land resources protection and economic development, not only for its own growing population, but also the world. Therefore, we must formulate strategies for the protection and sustainable use of agricultural land.

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Wang S, Zhou D, Zhou Pet al., 2011. CO2 emissions, energy consumption and economic growth in China: A panel data analysis.Energy Policy, 39(9): 4870-4875.This paper examines the causal relationships between carbon dioxide emissions, energy consumption and real economic output using panel cointegration and panel vector error correction modeling techniques based on the panel data for 28 provinces in China over the period 1995-2007. Our empirical results show that CO2 emissions, energy consumption and economic growth have appeared to be cointegrated. Moreover, there exists bidirectional causality between CO2 emissions and energy consumption, and also between energy consumption and economic growth. It has also been found that energy consumption and economic growth are the long-run causes for CO2 emissions and CO2 emissions and economic growth are the long-run causes for energy consumption. The results indicate that China's CO2 emissions will not decrease in a long period of time and reducing CO2 emissions may handicap China's economic growth to some degree. Some policy implications of the empirical results have finally been proposed. (C) 2011 Elsevier Ltd. All rights reserved.

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Wang Shaojian, Fang Chuanglin, Guan Xinglianget al., 2014. Urbanisation, energy consumption, and carbon dioxide emissions in China: A panel data analysis of China’s provinces.Applied Energy, 136: 738-749.

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Wang Zhaohua, Yin Fangchao, Zhang Yixianget al., 2012a. An empirical research on the influencing factors of regional CO2 emissions: Evidence from Beijing city, China.Applied Energy, 100: 277-284.In order to further study the realization of carbon intensity target, find the key influencing factors of CO 2 emissions, and explore the path of developing low-carbon economy, this paper empirically studied the influences of urbanization level, economic level, industry proportion, tertiary industry proportion, energy intensity and R&D output on CO 2 emissions in Beijing using improved STIRPAT (stochastic impacts by regression on population, affluence and technology) model. The model is examined using partial least square regression. Results show that urbanization level, economic level and industry proportion positively influence the CO 2 emissions, while tertiary industry proportion, energy intensity and R&D output negatively do. Urbanization level is the main driving factor of CO 2 emissions, and tertiary industry proportion is the main inhibiting factor. In addition, along with the growth of per capita GDP, the increase of CO 2 emissions does not follow the Environmental Kuznets Curve model. Based on these empirical findings and the specific circumstances of Beijing, we provide some policy recommendations on how to reduce carbon intensity. Beijing should pay more attention to tertiary industry and residential energy consumption for carbon emission reduction. It is necessary to establish a comprehensive evaluation index of social development. Investing more capital on carbon emission reduction science and technology, and promoting R&D output is also an efficient way to reduce CO 2 emissions.

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Wang Zheng, Zhu Yongbin, Liu Changxinet al., 2010. Integrated projection of carbon emission for China under the optimal economic growth path.Acta Geographica Sinica, 65(2): 1559-1568. (in Chinese)This paper performed a comprehensive projection of carbon emission through 2050 from the aspects of energy consumption,cement production process and forest carbon sink.Emission from energy consumption is estimated under the energy-economy framework by introducing the economic dynamics model and forecasting the energy demand on the optimal growth path,meanwhile the evolution of energy structure and the variation of carbon contents among energy types are also considered.Emission from cement production is projected on the basis of the forecast of cement output,which is deemed to be relative to urbanization process,while the latter follows the traditional S-curve development law.The estimation of forest carbon sink capability,involving the existing and newly afforested one,is conducted by employing the CO2FIX model.Eventually,all the three carbon sources or sink are composed to obtain the net carbon emission.Our results indicate that carbon emission from energy consumption peaks in 2031,with an emission of 2637 MtC (Million tons of Carbon equivalent) and the GDP per capita in that year is lower than the empirical experience of OECD countries;while the per capita energy-induced emission peaks in 2030,with a volume of 1.73 tC,which is far behind the US level of 2006 and still lower than the EU and Japan level of 2006.Besides,emission from cement production demonstrates a slow-down growth trend,and its emission is confined within 254 MtC,which is equivalently 12% of gross emission (here it refers to those emitted from energy consumption and cement production).Accumulated forest carbon sink is able to absorb 6806.2 MtC CO2 through 2050,but the annual absorption is dropping gradually.It is estimated that the net emission of CO2 will peak in 2033,which is 2748 MtC.

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Xinhua News Agency (XNA), 2011. The left-behind elderly reached more than 40 million. Xinhua News Agency Available from: [Accessed on 7 August 2014]

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Yang Xiaojin, 2013. China’s rapid urbanization.Science, 342: 310.

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Yang Xuchao, Hou Yiling, Chen Baode, 2011. Observed surface warming induced by urbanization in east China. Journal of Geophysical Research: Atmospheres, 116: D14.Monthly mean surface air temperature data of 463 meteorological stations including the ordinary and national basic/reference surface stations over east China during 1981-2007 and the National Centers for Environment Predication(NCEP)/National Center for Atmospheric Research(NCAR) Reanalysis are used to investigate the impact of rapid urbanization on temperature change.These stations are dynamically classified into six categories including metropolis,large city,medium city,small city,suburban and rural by making use of DMSP/OLS nighttime light imagery and population census data.Both analysis approaches of 'observation minus reanalysis(OMR)' and 'urban minus rural(UMR)' are utilized to detect surface air temperature change induced by urbanization.With the objective and dynamic station classification,the changes of observed and reanalyzed temperature over rural areas show a good agreement implying that the reanalysis can capture well regional rural temperature trends. The trends of urban heat island(UHI) effects by using OMR and UMR approaches are generally consistent and indicate that the rapid urbanization has significant impacts on surface warming over east China with an overall contribution of 24.2%to regional averaged warming trend.The largest effect of urbanization on annual mean surface air temperature trends occurs over metropolis and large-city station group with corresponding contribution of about 44%and 35%to the total warming respectively,and the UHI trends are about 0.4 and 0.26 ecade~(-1).

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Zhang Chuanguo, Lin Yan, 2012. Panel estimation for urbanization, energy consumption and CO2 emissions: A regional analysis in China.Energy Policy, 49: 488-498.As urbanization accelerates, urban areas play a leading role in energy consumption and CO2 emissions in China. The existing research is extensively concerned with the relationships between urbanization, energy consumption and CO2 emissions in recent years, but little attention has been paid to the regional differences. This paper is an analysis of the impact of urbanization on energy consumption and CO2 emissions at the national and regional levels using the STIRPAT model and provincial panel data from 1995 to 2010 in China. The results showed that urbanization increases energy consumption and CO2 emissions in China. The effects of urbanization on energy consumption vary across regions and decline continuously from the western region to the central and eastern regions. The impact of urbanization on CO2 emissions in the central region is greater than that in the eastern region. The impact of urbanization on energy consumption is greater than the impact on CO2 emissions in the eastern region. And some evidences support the argument of compact city theory. These results not only contribute to advancing the existing literature, but also merit particular attention from policy makers and urban planners in China. (C) 2012 Elsevier Ltd. All rights reserved.

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Zhang Ming, Mu Hailin, Ning Yadonget al., 2009. Decomposition of energy-related CO2 emission over the 1991-2006 in China.Ecological Economics, 68: 2122-2128.

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Zhong Wei, Yuan Wei, Li Susan Xet al., 2011. The performance evaluation of regional R&D investments in China: An application of DEA based on the first official China economic census data.Omega, 39(4): 447-455.

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Zhou Liming, Dickinson R E, Tian Yuhonget al., 2004. Evidence for a significant urbanization effect on climate in China.Proceedings of the National Academy of the Sciences of the United States of America, 101(26): 9540-9544.

58
Zhou Yang, Liu Yansui, Wu Wenxianget al., 2015. Effects of rural-urban development transformation on energy consumption and CO2 emissions.Renewable and Sustainable Energy Reviews, 52: 863-875.

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