How do population and land urbanization affect CO2 emissions under gravity center change? A spatial econometric analysis

Accepted Manuscript How do population and land urbanization affect CO2 emissions under gravity center change? A spatial econometric analysis Guanglai Zhang, Ning Zhang, Wenmei Liao PII:

S0959-6526(18)32376-X

DOI:

10.1016/j.jclepro.2018.08.146

Reference:

JCLP 13930

To appear in:

Journal of Cleaner Production

Received Date: 3 May 2018 Revised Date:

28 June 2018

Accepted Date: 4 August 2018

Please cite this article as: Zhang G, Zhang N, Liao W, How do population and land urbanization affect CO2 emissions under gravity center change? A spatial econometric analysis, Journal of Cleaner Production (2018), doi: 10.1016/j.jclepro.2018.08.146. This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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How do population and land urbanization affect CO2 emissions under gravity center change? A spatial econometric analysis

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Guanglai Zhang1, Ning Zhang1* Wenmei Liao2,3 Department of Economics, College of Economics, Jinan University, Guangzhou, 510632, China

School of Economics and Management, Jiangxi Agricultural University, Nanchang, 330045, China 3

Research Center of Rural Land Resources Use and Protection, Jiangxi Agricultural University, Nanchang, 330045, China

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Abstract: We examines the movement trajectory of gravity centers of China’s CO2 emissions on a regional level based on a gravity model, and then investigate the impact of urbanization on CO2 emissions by utilizing an extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model under the change of gravity center. We measure the urbanization in different dimensions including both population and land urbanization. Besides, a panel data between 2005 and 2014 is used for our empirical estimation, and the Spatial Durbin Panel model is used for estimation. The results show that the movement trajectory of gravity centers as a whole moved towards the northwest over the period. With CO2 emissions distribution presenting the characteristics of spatial agglomeration, we utilize the spatial econometric model to capture spatiality. The results show that the effect of population urbanization is insignificant; however, population urbanization has a positive and significant spatial spillover effect. Meanwhile, we find that the impact of land urbanization is significantly positive, while its spatial spillover effect is insignificant. Regarding other socioeconomic factors, it is proved that population scale, energy intensity and GDP per capita have a significantly positive impact, while industrialization level has a negative influence. These novel methodology and findings reveal that policy makers should carefully consider the characteristics of the rapid urbanization growth in China through the establishment of low-carbon urbanization policy standards, and strategies should emphasize China’s land-use conditions and promote coordinate development between population urbanization and land urbanization to achieve the sustainable development of urbanization and a low-carbon economy. Key words: Urbanization; CO2 emissions; center of gravity; spatial econometric model

1. Introduction China, as the largest emitter in the world, has taken up more than a quarter of the *

Corresponding author E-mail address: [email protected]

ACCEPTED MANUSCRIPT global carbon emission. Meanwhile, the urbanization in China, which is considered as one of the major contributor of carbon emission, has increased from 19.39% in 1980 to 54.77% in 2014, with a high average annual growth rate of 1.02%(Han et al., 2018). Rapid urbanization has posed some tremendous challenges related to environmental

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pressures, including energy consumption and CO2 emissions(Bahrami and Amini, 2018; Du et al., 2016; Zhang and Lin, 2012). These challenges have drawn nationwide attention in China and the government promised to try their best efforts to cut CO2 emissions in the future (UNFCCC, 2015). To meet these targets, China has

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been proactively questing an operable pathway of CO2 emissions abatement that adapts to its current state of urbanization development. Thus, it is extremely important

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for us to analyze the urbanizational influence on China’s CO2 emissions. Recently, a copious literature has studied the topic about urbanization’s influence on CO2 emissions (Hossain, 2011; Kasman and Duman, 2015; Li and Lin, 2015; Liu et al., 2017b; Wang et al., 2016; Zhang et al., 2017a), scholars have documented the following four tendencies: (1) urbanization increase regional CO2 emissions(Wang et

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al., 2013; Zhang and Lin, 2012); (2) the impact of urbanization on CO2 emission reduction are positive(Zhang and Xu, 2017); (3) urbanization has no significant impact on CO2 emissions(Behera and Dash, 2017); (4) the impact of urbanization on

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CO2 emissions take the form of an inverse U-shaped curve(Shahbaz et al., 2016; Zhang et al., 2017a). In addition to these conclusions, some researches investigate the effects of urbanization on CO2 emissions while considering the different levels of

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development(Li and Lin, 2015) and different stages of urbanization(Liu and Bae, 2018).

The main shortcoming of these existing literatures is the rough definition of

urbanization. In general, urbanization is defined as the process of all resources in rural area to develop urban, non-agricultural and promoting the local transfer of population (Shahbaz et al., 2016), but many existing literatures describe the urbanization as the rapidly increasing proportion of urban population, such as Lin et al. (2009), Liddle and Lung (2010), Zhou et al. (2012), Liu et al. (2016), Zhang et al. (2017a) and (Liu et al., 2017b), etc. It is obvious that the proxy variable is a blunt measurement because

ACCEPTED MANUSCRIPT urbanization encompasses the comprehensive influence of land urbanization and population urbanization, and these different aspects of urbanization may have different impacts on the carbon dynamics of urban ecosystems(Peng et al., 2017). Hence, analyzing the effects of urbanization on CO2 emissions requires more

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complete examination (Lin et al., 2017) and particularly what we need to focus on is the process of land urbanization, because the land use property is converted from agricultural land to urban construction land and the property right attribute is changed from rural collective land to state-owned land, which could have an important impact

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on China’s urbanization (Lin et al., 2017; Xu and Zhang, 2016). This study not only studies the effects of population urbanization but also takes into account the impact of

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land urbanization on CO2 emissions, which will lead to a more comprehensive and robust result due to a better explanation of current China’s urbanization development patterns.

The second key challenge in identifying the effect of urbanization on CO2 emissions is selecting appropriate empirical method. There are two main methods that

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can study explanatory factors of CO2 emissions, Structural Decomposition Analysis (Cansino et al., 2016; Duarte et al., 2013; Zeng et al., 2014; Zhang, 2012) and Index Decomposition Analysis (Andreoni and Galmarini, 2016; Lyu et al., 2016; Xu and

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Ang, 2014). However, one disadvantage of these models is they don’t take into account the external effects of energy consumption. In addition, as for policy implication, these two series of methodologies do not provide accurate conclusion

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because of the stochastic shocks and statistical inference (Zhang et al., 2017a). Thus, to overcome these drawbacks, this study adopts the STIRPAT model, which was first constructed by Dietz and Rosa (1997) and has been used widely by scholars in recent years (Shahbaz et al., 2017; Wang et al., 2017; Wei, 2011; Xie et al., 2017; Zhao et al., 2014). The third challenge is to analyze the spatial heterogeneity of regional CO2 emissions. With the development of spatial econometrics, the fact that China’s rapid urbanization development is spatial dynamic correlated with CO2 emissions cannot be ignored (Liu et al., 2016), and LeSage and Pace (2009) propose that the spatial effects

ACCEPTED MANUSCRIPT could be disaggregated into direct effects and indirect effects. Hence, this study uses an estimation technique that pairs gravity model and spatial econometric model with an extended STIRPAT model to conduct an empirical study, taking into account the impact with clarification from spatial econometric perspective by decomposing the

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population and land urbanization effect. In addition to providing new evidence on the impact of urbanization on CO2 emissions in China, which is the world’s largest developing country. This paper makes several contributions. First, compared with previous researches that just only take

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population urbanization as a proxy for urbanization(Liu et al., 2017b) or do not take into consideration the spatial dynamic effects(Zhu et al., 2017), this paper tries to

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incorporate both population and land urbanization into urbanization and we first utilize spatial econometric model to study the impacts based on an extended STIRPAT model, which will lead to more comprehensive and robust results due to a better explanation of current patterns for urbanization development in China. Besides, The impacts of land or population urbanization are further disaggregated into direct and

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indirect impacts from the perspective of spatial econometrics. Second, in order to better capture spatial and temporal characteristics of China’s CO2 emissions distribution, the other innovation of this paper is the application of gravity model,

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which could enable us to observe the movement trajectory of gravity centers of China’s CO2 emissions on a regional level and spatiotemporal evolution characteristics. Third, benefiting from the extended STIRPAT model, other

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influencing factors aside from population and land urbanization are also investigated. The results show that a 1% growth in population will cause China’s CO2 emissions increasing 1.838% and the CO2 emissions in a neighboring region decreasing 1.454%, a 1% increase in GDP will lead to growth of 2.312% in CO2 emissions and 1.749% reduction in a neighboring region, and a 1% decrease in energy intensity will cause 1.713% reduction in China’s CO2 emissions. Our analysis proceeds in three stages. We first discuss the movement trajectory of gravity centers of CO2 emissions by utilizing a gravity model through the period from 2005 to 2014 on a regional level in China. Then, to study the impacts of land and

ACCEPTED MANUSCRIPT population urbanization on CO2 emissions, we apply spatial econometric model by utilizing an extended STIRPAT model in the perspective of spatial econometric, with particular consideration for stochastic impacts. Specifically, we investigate some other influencing factors aside from population and land urbanization. Third, we analyze

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the spatial effects with the impacts being disaggregated into direct effects and indirect effects by using a method derived by(LeSage and Pace, 2009). This stage is aimed at dealing with the argument that the prediction of the spillover effect will get biased estimates due to the point estimate of multiple spatial regression.

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The reminder of the paper is organized as follows. Section 2 introduces the methodology of the gravity model, the extended STIRPAT model, spatial econometric

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model and data resources to conduct an empirical study of China. In Section 3, we report and discuss the estimation results. Section 4 contains conclusions, discussion and offers policy recommendations.

2.1. Gravity model

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2. Methods and Data

The concept of a center of gravity is derived from physics and it was first utilized to analyze the population spatial distribution of U.S. in 1872 (Hilgard, 1872). In fact, a

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center of gravity represents the point that could balance all the gravity produced by the fulcrum in the gravitational field, no matter where the object is (Kumler and

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Goodchild, 1992). Over the years, extensive research regarding centers of gravity have focused on population (McKee et al., 2015; Yang and He, 2017), economics (Kandogan, 2014), urbanization (Fu et al., 2015), land utilization (Xiaolin and Fei, 2011), environmental pollution (Liu et al., 2017a), consumption (Fu et al., 2011), energy supply and demand (Zhang et al., 2012). In addition, some scholars have used gravity models to present the trajectory of gravity centers of CO2 emissions and study its spatial and temporal differences (Song et al., 2015; Wang and Feng, 2017; Zhang et al., 2012). The location of the regional gravity center of CO2 emissions at year t can be

ACCEPTED MANUSCRIPT expressed in terms of longitude and latitude, and it is calculated as follows:

∑ CO x ∑ CO

(1)

∑ CO y ∑ CO

(2)

Xt =

2 ti i 2 ti

Yt =

2 ti

i

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2 ti

Where ( X t , Yt ) respectively represents the longitude and latitude coordinates of the gravity centers of CO2 emissions; where CO2ti is the degree of CO2 emissions at year t for province i; (xi , yi) are the coordinates of provinces k, which are represented by the

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longitude coordinates and latitude coordinates for the capital city of province i (Zhang

2.2. The Extended STIRPAT Model

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et al., 2012).

The Influence, Population, Affluence, and Technology (IPAT) model was first presented by Ehrlich and Holdren (1971), which has advantages to analyze the

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possible effects of humankind activities on ecological environment. It has received great attention due to its simplicity and useful framework. In this model, the environmental impact (I) is mainly composed of three factors, one factor is population size (P), the other factor is affluence (A) and the third factor is the technology level

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(T). The general form of an IPAT model is expressed as the following equation I=PAT

(3)

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However, the IPAT model can only obtain the influence of different factor within

constrain proportions. To overcome this drawbacks, Dietz and Rosa (1997) utilized a modified STIRPAT model and the model is expressed with the following equation:

I =ai Pitb AitcTitd eit

(4)

Whereas parameter a, b, c and d respectively denotes the constant term, population scale (P), affluence (A) and technology (T). The variable e is stochastic error, and the subscript i represents analysis object, such as an individual province, and the subscript t represents the specific time period. After natural logarithm processing of datum of equation (2), the new equation can be converted to linear form, meanwhile it can

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ln Iit = ln ai + b ln Pit + c ln Ait + d ln Tit + eit

(5)

The outstanding advantages of the STIRPAT model is that some other driving factors can be added in the extended STIRPAT model and study their impact on

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environmental pressure (York et al., 2003). There is a lot of literature on the study of expending the STIRPAT model by adding different additional explanatory variables, for example, Wang et al. (2013)employ the STIRPAT model to examine the impact factors of CO2 emissions in Guangdong Province, they expanded the STIRPAT

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model by incorporating urbanization level, industrial structure, energy structure, and foreign trade degree, into the model; Li and Lin (2015) add energy intensity into the

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extended STIRPAT model to investigate the impacts of urbanization and industrialization on energy consumption/CO2 emissions; Zhou and Liu (2016) take into account the share of the working-age population (16–64 years old), the average household size, the share of the added values of industry sector in the GDP and industry energy intensity, and their aim is to assess the impact of demographic and

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income changes on China’s energy-related CO2 emissions at the national and regional levels; Long et al. (2017) employ a static and dynamic STIRPAT models and their model utilizes two variables of the share of industry and the share of

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services to reflect economic structure; and Zhang et al. (2017a) use a panel data of 141 countries over the period of 1961-2011 and employ two-way fixed effects model

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based on the extended STIRPAT theoretical frameworks to analyzes the impact of urbanization on carbon dioxide emissions, their STIRPAT model studies the impacts of population structure, GDP growth and trade openness. In the literature mentioned above, the influence of demographic and economic factors on environment can be measured by the variables of total population and GDP per capita . In addition, Lin et al. (2017) and Zhang et al. (2017a) point out that trade openness has significant impacts on regional carbon dioxide emissions, besides, Jalil and Mahmud (2009) and Shahbaz et al. (2013) confirm that the variable of trade openness, which is the ratio of entire gross import and export valued to GDP, has a negative impact on CO2

ACCEPTED MANUSCRIPT emissions. But Li and Lin (2015) use foreign trade degree as a proxy for the trade openness and get opposite results for their research on China’s CO2 emissions. Hence, though it has been proved that there is a long run relationship between foreign trade and CO2 emissions(Kohler, 2013), the key to this question is to

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examine whether the relationship will change if we take into account the spatial characteristics in our empirical model. Therefore, this uncertain variable is added into our expended STIRPAT model.

Bongaarts (1992) suggested that the differences in economic structure could be well

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reflected in energy intensity and some scholars viewed energy intensity as a proxy for T (Lin et al., 2017; Poumanyvong and Kaneko, 2010; Zhang and Lin, 2012). Apart

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from the energy intensity, the industrialization level is also an important measurement to describe the T (Al-Mulali and Ozturk, 2015; Asane-Otoo, 2015; Lin et al., 2017; Zhang and Lin, 2012). Many literatures adopt the share of industry in GDP to measure the process of industrialization(Li et al., 2012; Shafiei and Salim, 2014; Wang et al., 2013; Xu and Lin, 2015), beyond that, Liu et al. (2017b) consider separately the

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different industrial branches of each sectors of the economy, such as the proportion of secondary industry and proportion of tertiary industry, to investigate the effect of new-type urbanization on energy consumption in China. However, Li and Lin (2015)

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prove that these measurement methods may cause a misleading index to measure the process of industrialization by the share of industry in GDP. Their study presents the share of industry in GDP in some representative countries and finds that although the

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developed/industrialized countries have a high level of the share of service in GDP, the developing countries may also have a high level of this index (such as China and India, respectively). Therefore, they conclude the share of industry in GDP can not accurately measure the process of industrialization level, and further use the ratio of secondary industry to primary industry to measure the process of industrialization. According to the statistical analysis in Robinson et al. (1986) and the experience of industrialized countries, the higher the ratio, the higher the country’s level of industrialization. Hence, this article also uses the ratio of secondary industry to primary industry to measure the industrialization level. In other words, the energy

ACCEPTED MANUSCRIPT intensity and industrialization level can show the technology level and we thus apply the two variables into the basic model. Lastly, as mentioned above, with the consideration of population urbanization and land urbanization, we add the two variables into this STIRPAT model.

below:

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For the sake of research, we can rewrite the expended STIRPAT model as defined

ln CEit = α + β1 (ln Pit ) + β 2 (ln GDPit ) + β 3 (ln EI it ) + β 4 (ln INDit ) + β 5 (ln FTDit ) +

β 6 (ln PU it ) + β 7 (ln LU it ) + ε it

(6)

2.3. The spatial panel-data econometric model

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Whereas the subscripts, i and t, respectively represent provinces and years.

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In this study, a spatial panel-data econometric model is built in view of the potential spatial correlation and spatial heterogeneity of the CO2 emissions for the expanded STIRPAT model, so that the panel-data method is well aligned with the spatial econometric method. Moreover, The spatial panel-data econometric model is widely acknowledged as a more accurate estimation method due to the explanation of both

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spatial and temporal characteristics for different studies (Chen et al., 2017; Elhorst, 2014; Liu et al., 2017b). As pointed out by Anselin et al. (2008) and Elhorst (2012), when specifying spatial dependence among the observations, a spatial panel data

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model may contain a spatially lagged dependent variable, or the model may incorporate a spatially autoregressive process in the error term. The first model is known as the spatial lag model and the second as the spatial error model. A third

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model, advocated by LeSage and Pace (2009), is the spatial Durbin model that contains a spatially lagged dependent variable and spatially lagged independent variables. Specifically, the spatial lag model (SLM) is formulated as: N

yit = ρ ∑ wij yit +α + xit β + µi + λt + ε it

(7)

j =1

where yit is the dependent variable for cross-sectional unit i at time t (i=1,…,N; t=1,…,T), ρ is spatial auto-correlation coefficient. The variable ∑ wij yit denotes the interaction effect of the dependent variable yit with the dependent variable y jt in

ACCEPTED MANUSCRIPT neighboring units, where wij is the i, jth element of a prespecified nonnegative N × N spatial weights matrix W describing the arrangement of the spatial units in the sample.

α is the constant term parameter. xit a 1× K vector of exogenous variables, and β a

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matching K × 1 vector of fixed but unknown parameters. ε it is an independently and identically distributed error term for i and t with zero mean and variance σ 2 . Where µi denotes a spatial specific effect and λt a time-period specific effect.

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In the spatial error model (SEM), the error term of unit i, φit , is taken to depend on the error terms of neighboring units j according to the spatial weights matrix W and an

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idiosyncratic component ε it , or formally

N

yit = α + xit β + µi + λt + φit , φit = θ ∑ wijφit + ε it

(8)

j =1

Where φit reflects the spatial error auto-correlation, θ is called the spatial

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auto-correlation coefficient.

Under the circumstances, we need accurately identify which spatial model is more appropriate to describe the data than a model without any spatial interaction effects. Hence, as shown in Fig.1, we creatively design a flowchart to explain how to select

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the optimal model by summarizing previous researches(Anselin et al., 2008; Elhorst, 2012; LeSage and Pace, 2009). As LeSage and Pace (2009) point out, one may use

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Lagrange Multiplier (LM) tests for a spatially lagged dependent variable and for spatial error auto-correlation, as well as the robust LM tests which test for a spatially lagged dependent variable in the local presence of spatial error autocorrelation and for spatial error autocorrelation in the local presence of a spatially lagged dependent variable. Testing for spatial effects in spatial panel models centers on the null hypotheses H0 : ρ = 0 and/or H0 : θ = 0 in the various models that include spatial lag terms or spatial error autocorrelation.

ACCEPTED MANUSCRIPT OLS regression Select SEM LM test: LM-Error test LM-Lag test

Reject the

Neither reject

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LM-Error test

Whether reject the

Reject one of

null hypothesis

the two LM test

Select

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Reject the

Both of

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them reject

Robust LM test:

LM-Error test

Select SLM

Robust LM-Error test Robust LM-Lag test

Whether reject the

Reject the Robust

LM-Error test

null hypothesis

LM-Lag test

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Reject the Robust

Select SEM

Select SLM

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Fig. 1. The analytical flowchart of the selection of spatial econometric model1

In addition, because independent variables will also have the interaction effect,

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therefore, there is a new spatial panel model that extends the spatial lag model with spatially lagged independent variables, which called spatial durbin model (SDM) N

N

j =1

j =1

yit = ρ ∑ wij yit +α + xit β +∑ wij xijtϕ + + µi + λt + ε it

(9)

where ϕ , just as β , is a K × 1 vector of parameters. The SDM tests two hypotheses,

H0

ϕ =0 and H 0

ϕ +ρβ =0 . The first hypothesis examines whether the spatial

Durbin can be simplified to the spatial lag model, and the second hypothesis whether 1 For this flowchart, the authors thank Prof. Lei Jiang and 2017 Summer School of Nanchang University for insightful comments.

ACCEPTED MANUSCRIPT it can be simplified to the spatial error model. If these two hypotheses are both rejected, it indicates that the SDM best describes the data. Hence, in this paper, aiming to choose the most appropriate spatial econometric model, we give the following three models and then take some statistic tests:

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(1) Spatial Lag Panel Data Model (SLPM) N

ln CEit = ρ ∑ wij ln CEit + α + β1 (ln Pit ) + β 2 (ln GDPit ) + β3 (ln EI it ) + β 4 (ln INDit ) j =1

(10)

+ β5 (ln FTDit ) + β 6 (ln PU it ) + β 7 (ln LU it ) + µi + λi + ε it

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Where ρ is spatial auto-correlation coefficient, the spatial weight matrix is wij and it N

can figure the spatial relations between region i and j.

∑w

ij

ln CEit demonstrate the

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j =1

spatial correlation between lnCEit of region i and the neighboring region. α and βi respectively denotes the constant and the coefficient of determinants, µi and λt respectively denotes the space-time-fixed effect and the time-fixed effect, and ε represents the residual error.

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(2) Spatial Error Panel Data Model (SEPM) ln CEit = α + β1 (ln Pit ) + β 2 (ln GDPit ) + β 3 (ln EI it ) + β 4 (ln INDit ) + β 5 (ln FTDit ) +

β 6 (ln PU it ) + β 7 (ln LU it ) + µi + λi + φit

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φit = θ ∑

Wijφit + ε it

N j =1

(11)

Where φit reflects the spatial error auto-correlation, θ is defined as the spatial

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auto-correlation coefficient. α is the constant, βi is the coefficient of determinants, and the definition of µi and λt is as described above. (3) Spatial Durbin Panel Data Model (SDPM)

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ln CEit = ρ ∑ wij ln CEit + α + β1 (ln Pit ) + β2 (ln GDPit ) + β3 (ln EIit ) + β4 (ln INDit ) j =1

N

N

+ β5 (ln FTDit ) + β6 (ln PUit ) + β7 (ln LUit ) + ϕ1 ∑ wij ln Pijt + ϕ2 ∑ wij ln GDPijt j =1

j =1

N

N

N

N

j =1

j =1

j =1

j =1

(12)

N

+ϕ7 ∑ wij ln LUijt + µi + λi + ε it j =1

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+ϕ3 ∑ wij ln EIijt + ϕ4 ∑ wij ln INDijt + ϕ5 ∑ wij ln FTDijt + ϕ6 ∑ wij ln PUijt

Where ϕi is defined as the spatial auto-correlation coefficient of the explanatory

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variable, the definition of µi and λt is as described above.

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2.4. Data

Our paper acquires a panel dataset of 30 provinces over the 2005 to 2014 period in China. Tibet is excluded because its data missing in most years. Besides, we collect the data of China’s CO2 emissions from the China Emissions Accounts and Datasets

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(http://www.ceads.net/), which can be found and referenced to Shan et al. (2016). The other eight explanatory variables are collected from the China Statistical Yearbook (2006-2015), Table 1 shows the descriptive statistics of the data. Apart from these

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variables, it is crucial to build the spatial weight matrix wij for the spatial econometric model. Hence, a combined spatial weight matrix is constructed by combining a geographic spatial weight matrix with an economic weight matrix with

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reference to Liu et al. (2017b)2. In addition, we calculate real GDP indexes of different years at 2005 constant price.

2

The combined spatial weight matrix is defined as the product of (1) The reciprocal of the distance between the two provincial capital (province i and j), which is calculated from their longitudes and latitudes. (2) the reciprocal of the absolute value of the difference that calculated by province i and j of their mean value of GDP per capita from 2005 to 2014.

ACCEPTED MANUSCRIPT Table 1 Variable description. Symbol

Variable

Definition

Unit

explained variable

CE

total CO2 emissions

CO2 emissions from fuel consumption

Million Tonnes

explanatory variables

P

total population

Mid-year population

Number

GDP

GDP per capita

EI

energy intensity

IND

industrialization level

FTD

foreign trade degree

PU

population urbanization

LU

land urbanization

mid-year population

Total energy use divided by GDP

Ratio of secondary industry to primary industry

Ratio of total gross import and export value to GDP

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Proportion of urban population to total population

RMB ¥100000

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GDP at 2005 constant price divided by

Proportion of the area of built districts

%

%

%

%

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to total city proper area

tce/ ¥100000

3. Empirical results

3.1. Spatial and temporal characteristics of the CO2 emissions distribution

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In this section, we divide China’s CO2 emissions into five groups by standard division. Specifically, (1) regions with annual CO2 emissions of 1-150 million tonnes for a (province i) belongs to the slight emission group; (2) regions with annual CO2 emissions of 151-300 million tonnes for a (province i) belongs to the small extent

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emission group; (3) regions with annual CO2 emissions of 301-450 million tonnes for a (province i) belongs to the moderate emission group; (4) regions with annual CO2

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emissions of 451-600 million tonnes for a (province i) belongs to the significant emission group; (5) regions with annual CO2 emissions of 600+ million tonnes for a (province i) belongs to the severe emission group. Fig. 2 presents the proportion of cumulative CO2 emissions of each province between 2005 and 2014. We find that the total CO2 emissions in China increased by 104.37% during the 2005-2014 period, from 5809 to 11872 million tonnes. In addition, Shandong province ranks first among the 30 provinces of China in CO2 emissions with 8739 million tonnes, accounting for 9.99% of the total. Shanxi province, Inner Mongolia, Hebei province rank second to fourth, respectively emitting 7360 (8.41%), 5546

ACCEPTED MANUSCRIPT (6.34%) and 5434 (6.21%) million tonnes of carbon dioxide emissions. Against that, Hainan province and Qinghai province cumulatively emitted the least carbon dioxide emissions, 394 (0.45%) and 429 (0.49%), respectively. As shown in Fig. 2, there is a large CO2 emissions gap between the 30 provinces in China, indicating that China’s

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CO2 emissions distribution may exist the characteristics of spatiotemporal.

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Fig. 2. The proportion of each province’s total CO2 emissions (2004-2015) in China

Moreover, we select three time points at regular intervals, 2005, 2009 and 2014 (and the mean value of 2005-2014), to analyze trends and regularity for the regional

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CO2 emissions in China by using the GIS software (ArcGIS version 10.2) to separately present the spatial pattern of CO2 emissions of 30 China provinces. The

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three time points we selected are typical years for China’s economy and CO2 emissions. 2005 is the starting point of our datasets, as well as 2014 is the ending point of our datasets. Besides, the government of China had to implement the “Circular Economy Promotion Law of the People's Republic of China” in 2009, which is aiming to improve environmental quality and promote the development of circular economy by increasing the utilization efficiency of resources and reducing carbon emissions. Taken account of the changes in spatiotemporal patterns of CO2 emissions distribution of ten years (2005-2014) at the province level (Fig. 3-Fig. 6), we find: (1) Shandong province belongs to the severe emission group through the entire period, emitting the most CO2 emissions in China; (2) during the period

ACCEPTED MANUSCRIPT 2005-2014, with rapid economic development and fast-paced urbanization, especially for the population and land urbanization, China’s CO2 emissions have rapidly grown. In 2005, except for Shandong province, no province emitted CO2 emissions more than 450 million tonnes, and only 4 provinces belong to the moderate emission group.

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Meanwhile, 15 provinces belong to the slight emission group. However, in 2009, Shanxi, Hebei, Jiangsu, Inner Mongolia, Henan and Liaoning joined the significant emission provinces group (451-600 CO2 emissions) and 11 provinces joined the moderate emission group. In the meantime, the slight emission group decreased to 9

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provinces. In 2014, the four significant emission provinces (Shanxi, Inner Mongolia, Hebei and Jiangsu) and one moderate emission province (Shaanx) in 2009 were added

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to the severe emission group because of the increasing CO2 emissions. An even more significant factor is that only 5 provinces remained in the slight emission group. (3) On average across 2005-2014, the 7 provinces belonging to a severe emission group or a significant emission group was mainly in northern China. Conversely, most slight emission provinces and small extent emission provinces are in central and western

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China.

Overall, it is obvious that the spatial distributions of regional CO2 emissions are consistent with the economic development from 2005 to 2014. Specially, the CO2

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emissions in provinces that around (not in ) the Circum-Bohai Sea Economic Circle, the Yangtze Delta and the Pearl River Delta areas are rising rapidly. A major contributor to this phenomenon is that the production capability is transferred from

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economic centers to provinces around them, accompanying with the industrial upgrading. So with the economic development and urbanization, energy demand increased rapidly. In addition, Those provinces are the major power suppliers of the economic center areas and the resources and transportation conditions make thermal power the main way of energy production, which cause highly increases of regional thermal power output that may lead to the rapid CO2 emission increase(Meng et al., 2011). In summary, with the rapidly developmental urbanization, China’s CO2 emissions continues to increase. In addition, with the CO2 emissions distribution presenting the

ACCEPTED MANUSCRIPT characteristics of spatial agglomeration, we improved the utilization of the spatial

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econometric model to capture the spatiality.

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Fig. 3. Spatial pattern of China’s CO2 emissions in 2005

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Fig. 4. Spatial pattern of China’s CO2 emissions in 2009

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Fig. 5. Spatial pattern of China’s CO2 emissions in 2014

Fig. 6. Spatial pattern of mean value of China’s CO2 emissions in 2005-2014

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3.2. Analysis of the movement trajectory of centers of gravity By utilizing a gravity model, the movement trajectory of centers of gravity is presented in Fig. 7. Comparing our results of gravity centers with previous research

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(Song et al., 2015; Wang and Feng, 2017), the major similarity is that both results presented that Henan province is always the center of gravity. The gravity centers of CO2 emissions in our study were distributed longitudinally between 112.99-114.66°E and latitudinally between 34.47-35.08°N during 2005-2014. As shown in Fig. 7, there

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are two main stages of movement trajectory of gravity centers over the 2005 to 2014 period. The first stage is 2005-2007, where it is obvious that the movement trajectory

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of gravity centers was towards southwest. The longitude and latitude of the gravity centers moved from 114.66°E to 114.44°E and 34.77°N to 34.47°N. The second stage is 2007-2014, where the movement trajectory of gravity centers was towards northwest. The longitude and latitude of the centers of gravity is showed the moving from 114.44°E to 112.99°E and 34.47°N to 35.08°N.

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The trend changed when the global financial crisis happened in 2007, after that the economy was cooled down and the increase rate of CO2 emissions dropped significantly in 2008-2014. For example, from 2005 to 2007, the annual economic

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growth rate stayed above 10%, even reached 14.2% in 2007. After 2007 the growth rates fell down quickly, 9.7% in 2008 and 9.4% in 2009. When the financial crisis

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impacted China’s economy since 2008, the increase rate of regional CO2 emissions fell down significantly. There were only nine provinces whose increase rates of CO2 emissions were higher than that in 2007, and six provinces even had a decrease. At the same time, in the planning of national development priority zones in 2007, according to resources and environment carrying capacity, existing development density and their potentials, the land space is divided into four types, optimized development, key development, restricted development and prohibitive development areas to co-ordinate planning for the future population distribution, economic layout, land use and urbanization patterns. Hence, the CO2 emissions in southern provinces has been

ACCEPTED MANUSCRIPT growing at a slower rate than provinces in the north China on account of more tougher restrictions and much more efforts on energy saving. But generally, the movement trajectory of gravity centers as a whole moved towards the northwest during 2005-2014. The reason for this phenomenon was mostly

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due to the implementation of national strategies of the Western Development and the “Rise of Central China”. This is because the Chinese government realized the rapid urbanization development in the western and central provinces would make significant contributions to promoting economic growth in the future. One immediate

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consequence is the shifting of highly polluting enterprises and energy-intensive industry from the eastern regions to the central-western China. Furthermore, the

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shifting did not include advanced production technologies and increased energy efficiency due to the relatively backward development of the central and western areas. In conclusion, with shifting of energy-intensive industry and enterprises with high CO2 emissions and low CO2 emissions efficiency in the western and central regions are the main reasons for the trajectory movement of gravity centers of CO2

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emissions in China.

In the following sections, by utilizing an extended STIRPAT model and a spatial econometric analysis, we analyze the impact of population and land urbanization on

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China’s CO2 emissions.

ACCEPTED MANUSCRIPT 35.2 2014 35.1 2013

35 （

2012

34.9

）

2011

N 2009

34.7 34.6

2005

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° 34.8

2010 2008

2006

2007

113

113.5

114

114.5

115

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34.4 112.5

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34.5

E

°

Fig. 7. The movement trajectory of centers of gravity of CO2 emissions in China

3.3. Results of spatial economitric analysis 3.3.1. Testing the spatial effect

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If an spatial econometric model is applied to our study, we must test whether the spatial effect exist. For spatial panel data, Anselin et al. (2006) and Elhorst and Freret (2009) proposed the LM Test and Robust LM Test to identify whether a spatial

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econometric model is requisite or a non-spatial traditional panel model is enough. In these tests, if any LM Lag Test or LM Error Test result is statistically significant, we

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have to utilize the spatial econometric model to capture the spatiality; otherwise, if neither LM Lag Test nor LM Error Test result is statistically significant, it can be affirmed that the traditional panel model is a better choice rather than an spatial econometric model. Moreover, we test the space-fixed effects within the model, the time-fixed effects within the model and the space-time-fixed effects through the LM Test and Robust LM Test, and we can choose the most appropriate fixed effects by utilizing the joint likelihood ratio (LR) test. Table 2 shows our empirical results. Table 2 shows results of the LM Test, we find that at least one LM test is significant in whatever fixed effect adding to our model, and the robust LM tests are the same.

ACCEPTED MANUSCRIPT Thus, it can be rejected that the spatial correlations do not exist, moreover, utilizing a spatial econometric model should be essential to conduct our research rather than using the traditional panel model. Meanwhile, neither the joint likelihood ratio test of space-fixed effects nor within time-fixed effects reject the null hypothesis that there would not exist space-fixed effects. Hence, aiming to capture the impacts more

provide analysis. Table 2 Panel OLS

Space fixed

Time fixed

Space-time fixed

Constant

-14.973***(-15.047)

---

---

---

Ln(P)

1.047*** (35.472)

1.013*** (4.44)

1.054*** (35.463)

-213.028 (-1.14)

Ln(GDP)

1.21*** (12.754)

1.29*** (12.103)

1.106*** (9.26)

-212.683 (-1.139)

Ln(EI)

1.388*** (22.012)

1.34*** (10.796)

1.4*** (22.073)

1.404*** (10.476)

Ln(IND)

-0.155*** (-4.534)

-0.171** (-2.361)

-0.137*** (-3.779)

-0.162** (-2.175)

Ln(FTD)

-0.056 (-1.641)

-0.09** (-2.319)

-0.032 (-0.877)

-0.071* (-1.65)

Ln(PU)

0.404* (1.873)

0.6** (2.265)

0.463** (2.096)

0.453 (1.626)

Ln(LU)

0.12*** (4.588)

0.012 (0.587)

0.111*** (4.187)

0.01 (0.462)

0.018

0.075

0.017

0.8

0.872

0.33

R

0.077

2

Adjusted R

0.883 2

0.88

Durbin-Waston Likelihood value

0.87

0.316

2.054

1.671

0.796

2.026

1.696

-36.089

188.997

-33.962

186.607

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σ

2

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Variable

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The LM Test and the joint likelihood ratio (LR) test.

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accurately, we utilize the spatial econometric model with space-fixed effects to

0.108

(0.743)

0.492 (0.482)

47.629 (0.000)

1.666 (0.197)

Robust LM spatial Lag

0.443

(0.506)

12.726 (0.000)

33.475 (0.000)

0.514 (0.474)

LM spatial Error

11.783

(0.001)

3.38 (0.066)

14.164 (0.000)

1.183 (0.277)

15.612 (0.001)

0.009 (0.924)

0.03 (0.862)

Fixed effects

Statistics

DOF

P-value

Space-fixed

441.14

30

0.000

9.22

10

0.511

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LM spatial Lag

Robust LM spatial Error The LR ratio of the joint

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test

12.118

(0.001)

Time-fixed

Note: t statistics in parentheses. As for the LM tests, we show the P-values in brackets. DOF refers to the degree of freedom. *, **, and *** respectively denote significance at different levels (10%, 5% and 1%).

3.3.2. The impact of population and land urbanization on CO2 emissions. As LeSage and Pace (2009) mentioned, if the spatial correlation do exist for spatial panel data model, we have to construct a general Spatial Durbin Panel Model (SDPM) to examine whether the SDPM could be transfomed into a spatial lag model (SLM) or a spatial error model (SEM) through the Wald and LR test for two null hypotheses of spatial correlation relating to parameter, which is H0 : φ=0 and H0 : φ+ρβ=0. If these

ACCEPTED MANUSCRIPT two hypotheses are both rejected, it can indicate that the SDPM best fits the data. Table 3 presents the empirical results of SDPM with space-fixed effects. As shown, the empirical results of the Wald test spatial Lag, LR test spatial Lag, Wald test spatial error and LR test spatial error with space-fixed effects are all statistically significant,

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implying that the SPDM cannot be simplified into a SLM or a SEM. Thus, it is certain that the SDPM makes a more reliable conclusion and our paper finally chooses to utilize the SDPM for analysis.

According to the results in Table 3, we prove that the impact of the spatial effect on

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CO2 emissions is a significantly positive correlation, which can be reflected there is a demonstration effect existing in interlocal CO2 emissions. The result means that a 1%

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reduction in CO2 emissions in a perimeter zone could cause 0.214% CO2 emissions reduction in the local region. The spatial effect result shows that if the targets for China’s CO2 emissions reduction are disaggregated into several scattered targets in different provinces or regions, it would be tremendously beneficial to promote the realization of CO2 emissions reduction targets. As for the results of the variables of

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population and land urbanization, the coefficient of population urbanization is positive but statistically insignificant, while its spatial spillover effect is positive and significant. It shows that a 1% growth in population urbanization rate could cause

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0.277% increase in total CO2 emissions and 1.763% CO2 emissions increase in a neighboring region. Moreover, land urbanization has a significant positive correlation with China’s CO2 emissions, but its spatial spillover effect is insignificant. The

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finding shows that a 1% growth in land urbanization rate will cause China’s CO2 emissions increasing 0.034% and the CO2 emissions in a neighboring region increasing 0.017%.

Apart from the key variable of population and land urbanization, we need to

analyze the impacts of other driving factors. As showed in Table 3, the variable of population produces a significant positive effect on CO2 emissions and a negative spatial spillover effect. It is clear that the regional CO2 emissions will increase along with the growth in population (specifically, a 1.838% increase in CO2 emissions resulting from a 1% growth in population), nonetheless, a 1% population growth

ACCEPTED MANUSCRIPT could cause CO2 emissions reduction of 1.454% in a perimeter zone. As for the GDP variable, its relationship with CO2 emissions is significantly positive, with the results that a 1% increase in GDP causing growth of 2.312% in CO2 emissions, while its spatial spillover effect is significantly negative (specifically, a 1.749% decrease in

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CO2 emissions resulting from a 1% growth in GDP). These findings indicate that a demonstration effect existing in interlocal CO2 emissions, so that we can achieve CO2 emissions reduction and does not affect economic growth. In addition, as mentioned above, this study applies energy intensity (EI) and industrialization level (IND) to

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show the technology level. As shown in table 3, the energy density is significantly positive, this means, based on the decrease of total energy use divided by GDP, the

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CO2 emissions will reduce simultaneously (specifically, 1.713% reduction in CO2 emissions resulting from a 1% decrease in energy intensity); conversely, the spatial spillover effect is insignificant and negative. Meanwhile, the impact of industrialization level is insignificant and negative, while the spatial spillover effect is significantly negative. The main reason is that China’s current industrial

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characteristics are moving towards high efficiency and low-energy consumption, especially for the characteristic of low CO2 emissions in the later period of industrial process, which will be conducive to CO2 emissions reduction. In the end, the degree

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of foreign trade, and the finding shows its impact on CO2 emissions is insignificant negative, as well as its spatial spillover effect. Table 3

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Empirical results of the Spatial Durbin Panel Data model with space-fixed effects. Variables

Coefficient

Variables

Coefficient

W*ln(CE)

0.214*** (2.849)

Ln(P)

1.838*** (5.835)

W*Ln(P)

-1.454** (-2.47)

Ln(GDP)

2.312*** (13.336)

W*Ln(GDP)

-1.749*** (-6.319)

Ln(EI)

1.173*** (8.068)

W*Ln(EI)

-0.295 (-1.137)

Ln(IND)

-0.071 (-1.029)

W*Ln(IND)

-0.248* (-1.722)

Ln(FTD)

-0.152 (-3.661)

W*Ln(FTD)

-0.048 (-0.622)

Ln(PU)

0.277 (0.839)

W*Ln(PU)

1.763*** (3.233)

Ln(LU)

0.034* (1.7)

W*Ln(LU)

0.017 (0.335)

σ2 R

2

Squared correlation coefficient

0.015 0.9788 0.8384

ACCEPTED MANUSCRIPT Likelihood value

217.498

Wald test spatial Lag

81.904 (0.000)

LR test spatial Lag

71.953 (0.000)

Wald test spatial error

48.627 (0.000)

LR test spatial error

66.807 (0.000)

Note: t statistics in parentheses. As for the Wald tests and LR tests, we show the P-values in brackets. *, **, and *** respectively denote significance at different levels (10%, 5% and 1%).

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3.3.3. Decomposing the population and land urbanization effect.

With the application of the Spatial Durbin Panel Data Model into this study, it is clear that the impact is no longer only represented through the coefficient of

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population and land urbanization. Instead, it has allowed us to analyze the spatial effects with the impacts being disaggregated into direct effects and indirect effects by

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using a method derived by LeSage and Pace (2009). The decomposition in Table 4 is aimed at dealing with the argument that the prediction of the spillover effect will get biased estimates due to the point estimate of multiple spatial regression. As shown in table 4, despite variables of industrialization level, whereas population and land urbanization are not statistically significant, the decomposition analysis is

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still meaningful because the spillover effects can be interpreted as the explanatory variables is through the use of the spatial lagged variables to affect the explained variable (Liu et al., 2016). As for the direct effects of population urbanization, a 1% growth in population urbanization rate could directly cause 0.271% increase of CO2

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emissions. Besides, the indirect effects finding shows that a 1% growth in the population urbanization rate could indirectly cause 0.068% increase of CO2 emissions.

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The total effects of a CO2 emissions is with 0.339% increase. In addition, the direct and indirect effects, and total effects of land urbanization are both positive (specifically, a 0.033% direct and 0.009% indirect increase in CO2 emissions resulting from 1% growth in land urbanization rate). Moreover, the same decomposition for the effects of other influencing factors are done. Every 1% growth of population size could result in 1.854% directly increase for CO2 emissions and 0.501% indirectly increase for CO2 emissions, the net of which is a total increase of 2.354%. One possible reason may be that the population size will become larger due to the increasing proportion of aging population, which will bring

ACCEPTED MANUSCRIPT out more consumption of energy and resources and environmental issues (Liddle and Lung, 2010; Zhang et al., 2017a), which then increases CO2 emissions. In addition, if GDP goes up by 1%, the CO2 emissions will directly increase by 2.34% and indirectly increase by 0.632%, implying the total effect is an increase of 2.972% in CO2

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emissions. Similarly, if energy intensity goes up by 1%, the CO2 emissions will directly increase by 1.188% and indirectly increase by 0.32%, the net of which is a total increase of 1.508%. By contrast, where a 1% growth in industrialization level will cause a direct reduction of 0.07% and an indirect reduction of 0.018% in CO2

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emissions, the total effect is a 0.088% decrease of CO2 emissions. Finally, the effect decomposition of the degree of foreign trade on CO2 emissions is significantly

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negative while the result in table 3 is insignificant, which forecasts that the spillover effects does exist via spatial lagged variables. A 1% growth in foreign trade degree will directly reduce CO2 emissions by 0.154% and indirectly reduce CO2 emissions by 0.041%, totaling a 0.195% decrease in CO2 emissions. The results suggests that China’s opening up policy has been beneficial to import advanced technologies and

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lead to a low-carbon economy. Thus, increasing the foreign trade degree and actively opening borders for trade and investment are also important factors driving CO2 emissions reduction. Table 4 Variables

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Results of direct effects, indirect effect and total effects for SDPM. (1) Direct effects

(2) Indirect effects

(3) Total effects

1.854*** (5.889)

0.501** (2.162)

2.354*** (5.072)

2.34*** (13.33)

0.632** (2.291)

2.972*** (7.931)

Ln(EI)

1.188*** (8.256)

0.32** (2.273)

1.508*** (6.498)

Ln(IND)

-0.07 (-1.021)

-0.018 (-0.876)

-0.088 (-1.014)

Ln(FTD)

-0.154*** (-3.703)

-0.041* (-1.949)

-0.195*** (-3.48)

Ln(PU)

0.271 (0.81)

0.068 (0.674)

0.339 (0.797)

Ln(LU)

0.033 (1.61)

0.009 (1.276)

0.042 (1.593)

Ln(P)

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Ln(GDP)

Note: t statistics in parentheses. *, **, and *** respectively denote significance at different levels (10%, 5% and 1%).

4. Conclusion and policy implications It is well known that urban systems are important spaces for CO2 emissions, studies on the impacts of urbanization on carbon emissions are of great significance to

ACCEPTED MANUSCRIPT development of effective ways to respond to climate change and realize “carbon emission reductions”(Ameli et al., 2014; Xu et al., 2018). But it should be noticed that urbanization encompasses the comprehensive influence of population urbanization and land urbanization, and these different aspects of urbanization could have different

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impacts on the carbon dynamics of urban ecosystems.(Peng et al., 2017). Hence, this paper measures the urbanization in different dimensions including both population and land urbanization, and analyze their respective impact on CO2 emissions in China. Furthermore, the Chinese government also faces more complex differentiation

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situation problems (such as decomposition of responsibility of different areas, green industry standard definition, etc.) in dealing with a package of carbon reduction, due

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to the significant heterogeneity of regional development (i.e. the serious imbalance of economic level, energy distribution and ecological pressure in geographical space), it will bring more challenges to the formulation of carbon emissions reduction and green development policies. Thus, the spatial heterogeneity of regional CO2 emissions, and the spatial effects of population urbanization and land urbanization in relation to CO2

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emissions also need to be evaluated.

Therefore, this article starts from the perspective of spatial effects to investigate the effects of population urbanization and land urbanization on CO2 emissions. The issue

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in China has attracted increasing attention from academic circles and policy makers. Based on the gravity model, we describe the movement trajectory of centers of gravity of China’s CO2 emissions over the 2005-2014 period. Then, by controlling for

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economic development, energy intensity, industrialization level, foreign trade degree and population scale, our work adopts a spatial econometric model to measure the influence of population urbanization and land urbanization on CO2 emissions in 30 provinces using annual data from 2005 to 2014. Urbanization and other factors are then disaggregated into direct and indirect effects. The basic results from our analysis are summarized as follows: (1) We find that the movement trajectory of gravity centers of China’s CO2 emissions as a whole moved towards the northwest during 2005-2014, which indicates that the economic growth of northwest China is on the basis of high carbon emissions. (2) The results show that inter-regional CO2 emissions

ACCEPTED MANUSCRIPT has a certain demonstration effect: when adjacent areas reduce CO2 emissions by 1%, the region’s CO2 emissions will be reduced by 0214%. As a result, we can see that implementing total CO2 emissions control in different regions plays a key role in controlling CO2 emissions in the country as a whole. (3) We find that a 1% growth in

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population urbanization rate will cause 0.277% increase in total CO2 emissions and 1.763% CO2 emissions increase in a neighboring region. Meanwhile, as for the effects decomposition for urbanization’s impact on CO2 emissions, we find that if population urbanization rate goes by 1%, the CO2 emissions can directly increase by 0.271% and

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indirectly increase by 0.068%. The total impact sums up to be a 0.339%. It is clearly stated the target of steadily moving toward new-type urbanization that the China’s

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population urbanization rate should achieve 60% by 2020 as planned in the National New Urbanization Plan (2014-2020)3. According to the simulation in the spatial econometric model with the fact that all 30 provinces emitted 11872 million tones CO2 in 2014, if the population urbanization rate achieves 60% by 2020 (an increase of 9.49% over the 54.8% population urbanization rate in place in 2014), it will directly

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increase total CO2 emissions by 2.572% and indirectly increase total CO2 emissions by 0.645%. The total effect will be an increase in total CO2 emissions of 3.217%. (4) A 1% growth in land urbanization rate will cause 0.034% increase in total CO2

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emissions and 0.017% CO2 emissions increase in a neighboring region. Specifically, a 0.033% direct and 0.009% indirect increase in CO2 emissions resulting from 1% growth in land urbanization rate. Which means a 1% growth in land urbanization rate

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will directly increase total CO2 emissions by 391.78 million tones and indirectly increase total CO2 emissions by 106.85 million tones(relative to the total amount in 2014). The total effect will be an increase in total CO2 emissions of 498.63 million tones.

According to the results, several policy proposals for China’s CO2 emissions can be derived. Firstly, just as the movement trajectory of gravity centers of China’s CO2 emissions moved towards the northwest over the period, and in view of the current economic situation and development trend of western China, it is obvious that the 3

http://ghs.ndrc.gov.cn/zttp/xxczhjs/ghzc/201605/t20160505_800839.html

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among regions, which can be interpreted that the spatial lags of economic development levels affect the CO2 emissions, we should make the best use of the regional demonstration effect and make it an effective way to reduce CO2 emissions(Wang et al., 2013).

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Secondly, it is essential that policy makers need to carefully consider the characteristics of the rapid urbanization growth in China through the establishment of

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low-carbon urbanization policy standards, and strategies should emphasize China’s land-use conditions to achieve the sustainable development of urbanization and a low-carbon economy. By directing a low-carbon planning, effective space-time allocation of land resources, structural optimization, scale control, function enhancement are conductive to a green development pattern, and facilitating the

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formation of high-tech, low resource consumption, low pollution, and low-carbon development(Zhang and Xu, 2017). Specifically, (1) An incentive mechanism is necessary to be designed to revitalize the construction land use, besides, to achieve

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high-quality of land urbanization, local government should avoid urban construction from over-reliance on land revenue. (2) Governments need carefully increase land use indicator for cities based on a unified construction land market in urban and rural

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areas. (3)Reasonably control urban expansion by charging on real estate, and to provide effective sources of sustainable city construction. (4) Apart from the government’s efforts, more than a perfect land market mechanism should be established to optimize land urbanization. Thirdly, governments should actively promote coordinate development between population urbanization and land urbanization, and formulate sensible land-use policy within the principles of different area to “suit the remedy to the case”(Zhang et al., 2017b). For the eastern region, due to the current large population scale is the primary determinant of urbanization development, a reasonable control of population size and

ACCEPTED MANUSCRIPT the enhancement of land-use efficiency can contribute to the reduction in CO2 emissions. For Central China, we need focus on the transformation of the industrial structure and the steps which feature in quantity increase of litter towns shall turn to the new steps in which is set great store by raising quality and reshuffle function. The

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Western China, however, should consider the comparative advantages to gain more human capital and investment, and to increase investment in science and technology to prevent the tension between land supply and the reduction of carbon emissions, on account of the second industrial development is the main determinant of carbon

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emissions.

Overall, all the impacts of driving factors on China’s CO2 emissions have important

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academic value and policy implication in the field of study of green and sustainable development and low carbon economy with high quality urbanization under gravity center change in China. Moreover, this paper still exists some limitations. It should be noted that when taking into account population scale, economic development, energy intensity, industrialization level and foreign trade degree in CO2 emissions driving

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forces, just like most previous studies (Liu and Bae, 2018; Shahbaz et al., 2016; Zhang and Lin, 2012). The expended STIRPAT model in this paper still can not analyze all possible factors that may influence China’s CO2 emissions, such as

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economic incentive and financing level. Though the core issue of this paper is investigate the effects of the population urbanization and land urbanization on CO2 emissions in China by utilizing a spatial econometric model , and we also try to refer

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to previous studies to control some other influence factors of CO2 emissions, it is essential that our future research need take into account the environmental protection, financing level and economic incentives from regional administrations to reduce CO2 emissions. In addition, because urbanization encompasses the comprehensive influence of population urbanization, employment urbanization, land urbanization and economic urbanization, future research will consider all aspects of urbanization and their respective impact on the CO2 emissions, and compare the results between the two different measurements of urbanization and seek out potential causes for the differences.

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Acknowledgement We thank the financial support provided by the National Natural Science Foundation of China 91746112 ,71603102, 71463025, 71663029), Key Project of Chinese

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Ministry of Education (17JZD013) and Research Center on Low-carbon Economy for Guangzhou Region.

Nomenclature

the longitude coordinates of the gravity centers at year t

Yt

the latitude coordinates of the gravity centers at year t

ρ

spatial auto-correlation coefficient

α

constant term parameter

µi

a spatial specific effect

λt

a time-period specific effect

φit

the spatial error auto-correlation

θ

the spatial auto-correlation coefficient

CO2ti xi yi I P A T eit yit wij xit β LM CE P GDP EI

the degree of CO2 emissions at year t for province i the longitude coordinates of provinces k the latitude coordinates of provinces k environmental impact population size affluence technology level stochastic error dependent variable for cross-sectional unit i at time t the i, jth element of a prespecified nonnegative spatial weights matrix W a 1*k vector of exogenous variables a matching k*1 vector of fixed but unknown parameters Lagrange Multiplier total CO2 emissions total populations GDP per capita energy intensity

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Xt

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industrialization level foreign trade degree population urbanization land urbanization

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