Spatial economic dependency in the Environmental Kuznets Curve of carbon dioxide: The case of China

Journal of Cleaner Production 218 (2019) 498e510

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Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro

Review

Spatial economic dependency in the Environmental Kuznets Curve of carbon dioxide: The case of China Yanyan Wang, Xubiao He* School of Management, Huazhong University of Science and Technology, Wuhan, 430074, China

a r t i c l e i n f o

a b s t r a c t

Article history: Received 12 August 2018 Received in revised form 27 January 2019 Accepted 30 January 2019 Available online 31 January 2019

Modeling the driving factors and spatial nexus of carbon dioxide (CO2) emissions may be complicated by the fact that trans-provincial transportation of energy- and pollution-intensive products, maybe or to a large extent, transports their emissions to the exporting provinces due to the frequent occurrence of bilateral economic-trade ties and the asynchrony of economic development in China. In this paper, a spatial econometric tool is utilized to examine to what extent CO2 spillover depends on provinces’ bilateral geographical and economic linkages. Besides, different subgroups are built to estimate and compare the degree of spatial interactions among regions under five definitions of spatial dependency, to determine how the spatial effects of CO2 emissions behave among our selected subgroups. The results demonstrate an N-shaped relationship, either before or after grouping, which shows a strong correlation against the effectiveness of the Environmental Kuznets Curve (EKC) hypothesis. Additionally, the results show that economic linkage significantly outperforms all the other linkages in capturing the spatial dependencies between provinces, and the spatial dependence of CO2 emissions in the high-energy structure group is far greater than that in the low-energy structure group. Our results suggest that even though considering the spatial aggregation effect, the economic relations behind CO2 emissions can also capture the spatial features more accurately, providing valuable references for policymaking and carbon emission reduction. © 2019 Elsevier Ltd. All rights reserved.

Keywords: CO2 emission intensity Spatial dependence EKC Panel threshold Spatial aggregation effect

Contents 1. 2. 3.

4.

5.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 499 Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 500 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 3.1. Theoretical framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 3.2. Econometric model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 501 3.3. Spatial weights matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 3.4. Data selection and preprocessing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 3.4.1. Calculation of CO2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 3.4.2. Selection of control variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 Estimation results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 4.1. Spatial dependence test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 502 4.2. Choice of the spatial models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 503 4.3. Selection of grouping variable and estimation of critical value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 504 4.4. Analysis for spatial dependence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 505 4.5. Influence of control variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507 Conclusions and policymaking recommendations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508

* Corresponding author. E-mail addresses: [email protected] (Y. Wang), [email protected] (X. He). https://doi.org/10.1016/j.jclepro.2019.01.318 0959-6526/© 2019 Elsevier Ltd. All rights reserved.

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Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 508 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 509

1. Introduction An Intergovernmental Panel on Climate Change (IPCC) report warned that the extreme events caused by global climate change will continue to increase in the future, and that the hightemperatures and heat-wave will long term affect the world. Climate change presents a new normal and is far-reaching. Among the several environmental pollutants that can cause climate change, CO2 is responsible for 58.8% of all greenhouse gases (Pao et al., 2012). Additionally, China is the largest developing country and has the second largest total CO2 emissions in the world (Vennemo et al., 2009). Authorities have tried to understand the impact of economic growth on the environment to realize the harmony of environmental protection and economic reform. An increasing number of studies have used the EKC theory to explore the relationship between environmental degradation and economic growth in China, such as Diao et al. (2009) for Chinese environment pollution indices and Ren et al. (2015) for CO2 emissions. The EKC hypothesis reveals that indicators of environmental degradation first rise and then fall with increasing income per capita and implies the inverted U-shaped relationship proposed by Shafik and Bandyopadhyay (1992) and Grossman and Krueger (1995). The spatial characteristics of environmental measures have also attracted the attention of some scholars (Giacomini and Granger, 2004; Goodchild et al., 2000). Generally speaking, transboundary transactions on energy- and carbon-intensive products, such as cars and air conditioning, can lead to spatial interactions of CO2 emissions. When estimating spatial dependence, researchers may be overly concerned with whether it occurs in geographically closeness regions, instead of gathering evidence about bilateral economic relations. Traditionally, the indicator of spatial dependence requires specification of a spatial weights matrix, which is

Fig. 1. Average levels of CO2 emission intensity for 30 Chinese provinces between 1995 and 2013.

usually defined based on the traditional geographical distance between any two units or regions. To our knowledge, the geographies of regions are closer, leading to more close linkages. However, the frequent bilateral economic and trade exchanges strengthen the closeness of the cross-region linkages, irrespective of whether the geographies of provinces are far apart or near. Thus, an alternative to the geographical distance is a so-called economic distance which specifies the economic closeness between pairs of provinces. For example, some economic indicators as distance measures, such as provinces’ bilateral trade or transportation costs, impact not only the region in which they are located, but also adjoining regions. Thus, the spatial dependence with economic distance should be taken into account in the EKC in order to verify that CO2 emission intensity may not only be influenced by geographic proximity but may also be related to the economic proximity. The main purpose for this study is to analyze how the spatial correlation in the EKC depends on the cross-province geographical and economic linkages. We employ the spatial autoregressive model (SAR) to test the influence of the possibly important linkage matrices (binary contiguity matrix, geographic weights matrix, economic weights matrix, geo-economic weights matrix and inverse geo-economic weights matrix). Meanwhile, Fu (2004) found a phenomenon that pronounced regional disparities exists between the coastal and inland regions of China. As seen in Fig. 1, the gradual increasing of the provincial CO2 emissions exhibits spatial uneven distribution characteristics from the eastern coast to the north-western regions of China. The coexistence phenomenon of highly congested areas and relatively sparse areas may be due to some locational determinants, which are featured by highly economic- or geo-referenced, such as economic opportunities, transport accessibility and the locations of coal mines, industrial parks and new high-tech establishments. Thus, it is important to study the adaptability of the spatial dependence of the EKC in the case of geographic clustering. We cluster the provinces into several subgroups by the fixed-effect panel threshold regression method (Hansen, 1999), which is more efficient and sensitive to spatial context. The similar studies are carried out. This paper theoretically points out that the bilateral economic relations are an important influence factor for the spatial dependence of CO2 emissions. So far, the research about the spatial dependence with economic distance is still rare. This paper extends the spatial contagion effect in the geographic and economic dimensions. In empirical research, this paper analyzes the influence of the spatial correlation with the geographic and economic matrices to verify that the spatial neighborhoods described by bilateral economic relations are far more superior in terms of capturing CO2 spatial dependence. Meanwhile, the study also considers the effect of the regional clustering about geographic and economic opportunities. This paper enriches the empirical results of the spatial dependence of CO2 emissions. In the application areas, studying on the transmission mechanisms of carbon pollution is of important practical significance. The spatial dependence of carbon emissions depends not only on geographically adjacent regions, but also on those with bilateral economic relations. It will help the government to develop the effective joint regional policies based on regional economic inequalities for controlling air pollution. This paper is arranged as follows. Section 2 presents the literature review. Section 3 explains the methodology and econometric

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model. Section 4 presents empirical results. Section 5, the paper provides conclusions and policy suggestions. 2. Literature review Environment-economy tradeoff is one of the most persistent and widely investigated issues in empirical research of ecological economics. Recently, more works involve the nexus of CO2 emissions and economic growth. Dong et al. (2017) found that EKC pattern is particularly pronounced for the central-eastern region of China. Similar phenomena are also found in 5 emerging countries (Dong et al., 2018a) and 14 Asia-Pacific regions (Dong et al., 2018b). The empirical researches demonstrated that the EKC pattern is not affected by the countries and regions. Shuai et al. (2017) found that the country-specific income level changes the key impact factor in affecting carbon emissions. However, these conventional EKC studies implicitly assume that CO2 emissions in one district are unaffected by its neighboring districts. Ignoring these spatial correlations could lead to biased and inconsistent results. To overcome this limitation, we make the same work applying spatial econometric techniques. Notably, studies on the issue of interdependencies in the EKC model are not new. Table 1 reviews the studies that have integrated spatial correlation in the EKC. Clearly, despite the growing body of literature on the spatial interaction for the EKC model, scholars have placed overwhelming emphasis on the geographic, social, or adjacent distances. There have been few discussions on how to define the dependency structure through the economic dimension. First, focus of attention was drawn to a simple spatial adjacency matrix. In this case, Rupasingha et al. (2004) used Bayesian Tobit SEM models to study the EKC focusing on toxic pollutants in U.S. counties. The results indicated that all of the spatial models exhibit the EKC relationship and strong significant spatial correlation. Similarly, McPherson and Nieswiadomy (2005) confirmed that for threatened birds and mammals, both spatial autocorrelation and the EKC effect exist in the SAR model. Hosseini and Kaneko (2013) studied the 129 countries case employing a SDM specification. They demonstrated that CO2 emissions spread spatially to their neighbors through the spillover of the institutional quality of countries, and that the robust inverted U-shaped EKC can be seen after

controlling for spatial relationships. The studies by Hao et al. (2016) and Liu et al. (2018) also arrived at similar results that there exists not only an EKC but also the spatial dependence for coal consumption and environmental pollution index, respectively, in China. However, despite of the presence of spatial effects, some works found no empirical evidence in favor of the EKC hypothesis. For instance, in examining both sulfur dioxide (SO2) and soot particles in China, Poon et al. (2006) pointed out that for both the SEM and SAR models, significant spatial effects is present, and that this EKC relationship exhibits an inverted U-shaped relationship for SO2 but a U-shaped curve for soot emissions. Tevie et al. (2011) tested whether an EKC theory holds for biodiversity risk in the US. The econometric evidence only indicated that the EKC assumption is not supported without affecting the existence of spatial effects. Again, Huang (2018) confirmed an N-shaped EKC and positive SO2 spatial spillover among provinces in China. The results from the dynamic SAR model in Zhou and Wang (2018) found significant effect on both dynamic factor and spatial autocorrelation of China’s city-level CO2 emissions, but nonsignificant effect on the EKC. Second, some attention was drawn to multiple spatial diffusion channels. For instance, Pandit and Laband (2007) modeled for spatial dependency in the EKC of species imperilment under four alternative spatial weight matrices, as measured by simple adjacency, higher-order adjacency, distance between adjoining countries’ centroid locations, and percentage of shared border respectively. The conclusion was that the simple adjacency matrix outperforms other metrics at capturing spatial dependencies, and that there exists not only an inverted U-shaped EKC but also the spatial dependence. In line with this, studies like those conducted by Donfouet et al. (2013), Hao and Liu (2016) and Liu and Lin (2018) have concluded that both spatial effect and the EKC relation exist using the alternative spatial matrices, which is just for the implementation of the robustness testing for existing conclusions. In addition, similar results have been discovered by Ma et al. (2016) for China’s PM 2.5 pollution emissions, the economic measure only performs better at improving the significances of control variables and goodness of fit, compared to the geographic distance measure. Conversely, Jiang et al. (2018) and Wang et al. (2013) conducted EKC studies relating to indicator of environmental pollutants for each matrix separately. The alternative spatial weight

Table 1 Literature overview on the spatial econometric analysis of the EKC. Reference

Approach

Dependent variables

Spatial weight matrices

Results

Rupasingha et al. (2004)

Bayesian Tobit SEM SAR

toxic pollutants

W2 W2

Inverted U-shape; N-shape (incorporation of a cubic term) U-shape

W1, W2 W3, W2 W1, W2 W1, W1, W2, W2 W1, W2 W2

McPherson and Nieswiadomy (2005) Poon et al. (2006)

SAR, SEM

threatened bird; mammal species SO2; soot

Pandit and Laband (2007) Tevie et al. (2011) Donfouet et al. (2013) Hosseini and Kaneko (2013) Wang et al. (2013) Hao et al. (2016) Hao and Liu (2016) Ma et al. (2016) Jiang et al. (2018) Huang (2018) Liu and Lin (2018) Liu et al. (2018) Zhou and Wang (2018)

SAR,SEM SAR, SEM SAR SDM SEM, SDM SDM SAR, SEM SAR SAR, SDM SDM SAR, SEM, SDM SAR, SEM Dynamic SAR

species imperilment biodiversity risk CO2 emissions CO2 emissions ecological footprint coal PM 2.5 PM 2.5 air quality index SO2 environmental pollution index environmental pollution index CO2 emissions

unclear W2, W3, W4 W5, W6 W2, W3 W2, W6 W7 W3, W6 W2

Inverted U-shape applied for SO2; U-shape applied for soot Inverted U-shape a lack of an EKC relationship Inverted U-shape Inverted U-shape a lack of an EKC relationship Inverted U-shape Inverted U-shape Inverted U-shape a lack of an EKC relationship N-shape inverted N-shape Inverted U-shape a lack of an EKC relationship

Notes: (W1) geographic distance; (W2) binary adjacency; (W3) higher-order adjacency; (W4) length (or percentage) of shared border; (W5) social network spatial weights matrix based on the countries being neighbors if they speak the same official language; (W6) inverse distance; (W7) economic distance. (SAR) spatial autoregressive model; (SEM) spatial error model; (SDM) spatial Durbin model

Y. Wang, X. He / Journal of Cleaner Production 218 (2019) 498e510

matrices were defined according to different linkages such as simple adjacency, higher adjacency, countries within 1750 miles, and so on. It was shown that significant spatial autocorrelation is detected, but there is no evidence of support for the EKC hypothesis. In reality, trade, technology and economic differences exist among countries or regions. It becomes obvious that countries can strongly interact with each other via various channels, such as trade, capital inflows, technology diffusion and common environmental policies (Ramirez and Loboguerrero, 2002). Consequently, the pollution emissions in one region can affect its geographic or economic neighbors, making their environmental performance better off or worse off. For example, Maddison (2006) implemented spatial econometric techniques to explore the question of whether emissions per capita depend on the characteristics of adjacent states and concluded that national emissions of nitrogen oxides are decreased by proximity to high-income countries. The results are very similar to those of Cole (2004), that the migration of pollution from high-to low-income countries is associated with international trade. Thus, it has become essential for researchers to study the economic distance, except when geographical distances or other common definitions of spatial dependency are used. Technically, we prefer to use a spatial econometrics approach. Many examples in the literature have employed other modeling techniques to study the dependencies in testing the CO2 emission intensity EKC hypothesis, such as time series vector autoregressive (VAR) models (Wang et al., 2015), vector error correction (VEC) models (Danish et al., 2017; Govindaraju and Tang, 2013), autoregressive distributed lag (ARDL) models, and Granger causality tests (Sugiawan and Managi, 2016; Tiwari et al., 2013). These models allow to measure interactions among the endogenous variables and their own lagged terms, but none consider the issue of how close (social or economic distances speaking) the environmental performance are. The spatial econometrics model overcomes this problem using a spatial weights matrix, which reflects the N
N possible interactions between all N spatial units or regions. The weights matrix is predetermined and needn’t to be estimated from the data. Thus, the spatial correlation in CO2 intensity is modeled by integrating the spatial weights matrix and spatial lag operator; this integration also reduces the number of unknown correlation parameters from N
N to 1, namely, the spatial dependent coefficient. Spatial dependent coefficients alone measure the overall effect of other regions on a particular region, greatly reducing parameter estimates.

501

capita of the year t as Yit ’s proxy variable (Friedl and Getzner, 2003). In addition, one aspect to consider is the functional form relating CO2 emissions to GDP. The norm is a second order or at most third order polynomial function for the linear or log-linear models. This paper introduces the cubic term of GDP per capita, that allows the carbon-economy nexus to be more flexible. The final function specification depends on the best model of fitting data and on explanatory strength of the variables. Therefore, under the EKC hypothesis, the benchmark model Eq. (1) is rewritten as follows:

lnCO2 intensityit ¼ b0 þ b1 lnðGDPit Þ þ b2 ðlnðGDPit Þ Þ2 X j þ b3 ðlnðGDPit Þ Þ3 þ hj Zit þ εit

(2)

j

where Zitj 2ðlnPopit ; lnFDIit ; lnTRit ; lnINDUSTYit ; lnECSit ; lnURBit Þ. All variables in Eq. (2) take the logarithmic form. The regression coefficients reflect the elastic characteristics. Among them, subscripts i, t represent the observation sample and time, respectively, and CO2 intensityit is the carbon dioxide emission intensity index. GDP per capita is a proxy for Yit and represented by the symbol GDP. In addition, Z j it is one of control factors that influence the carbon dioxide emission intensity, including the population size (Pop), foreign direct investment (FDI), trade openness (TR), energy consumption structure (ECS), industrial structure (INDUSTY), and urbanization ratio (URB). The selection criteria of these control variables are mainly based on the relevant literature, such as Ren et al. (2014) and Al-Mulali et al. (2016). 3.2. Econometric model Getis (2007) believed that, in addition to measuring the time dependence, the spatial dependence also must be considered. The traditional time series literature has only focused on the dependency of the observed values at different times, without considering that the observations may be affected by geographical or economic factors. Therefore, this paper is interested in the empirical study based on Eq. (2) using the spatial panel model. The panel SAR can be described as follows:

ln CO2 intensityit ¼ lWlnCO2 intensityit þ b1 lnðGDPit Þ þ b2 ðlnðGDPit Þ Þ2 þ b3 ðlnðGDPit Þ Þ3 þ

X

hj Zitj

j

þ ai þ gt þ εit 3. Methodology

(3) The panel SEM can be expressed as follows:

3.1. Theoretical framework Since Grossman and Krueger (1995) published their groundbreaking and controversial report on the EKC, considerable effort has been devoted to investigate environmental impacts of economic development using the EKC. The basic model is written as:

ln Eit ¼ b0 þ b1 ln Yit þ b2 ðln Yit Þ2 þ εit

(1)

According to the traditional EKC hypothesis, Eit represents the pollutant emission level in the year t, for the i province; Yit represents the productivity level of i province in the year t; b1 is a positive slope; b2 is a negative slope; and εit is the random disturbance item following independent and identical distribution. Considering the logarithmic forms of all regressions can reduce data volatility and the possible influence of the heteroscedasticity, in this study, we choose the i province carbon dioxide emission intensity level of the year t as Eit ’s proxy variable. We choose the i province GDP per

ln CO2 intensityit ¼ b1 lnðGDPit Þþ b2 ðlnðGDPit ÞÞ2 þ b3 ðlnðGDPit ÞÞ3 P j þ hj Zit þ ai þ gt þ mit

mit ¼ rW mit þεit

j

(4) where lnCO2 intensityit represents the natural logarithm of the carbon dioxide emission intensity from the province iði ¼ 1; 2; /; NÞ, t ¼ 1; 2; /; T. l is the spatial lagged value of the explanatory variable WlnCO2 intensityit , which represents the spatial correlation coefficient in Eq. (3). Its sign and significance directly reflect whether the spatial interactions exist between different provinces. l > 0 shows that the emission intensity distribution has a positive spatial interdependence; otherwise, it is the negative spatial interdependence. r is the spatial lagged value of the error term W mit , which represents the intensity of CO2 emission’s interaction

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among the provinces. The spatial correlation in the residual term in Eq. (4) can reflect the interactions of CO2 emissions among the provinces. W is the pre-defined spatial weights matrix with dimension N
N that generates the cross-sectional correlation. The spatial weights matrix reflects the dependency structure of the spatial units. To facilitate estimation, W is usually row normalized from a symmetric matrix such that its sum for each row equals 1. Z j it is one of control variables and the same as in Eq. (2). εit is i:i:d: with zero mean and variance s20 . ai is the individual effect. gt is the time effect. All variables in Eqs. (3) and (4) take the logarithmic form. The literature on these model can be observed in Elhorst (2003) and Elhorst (2010), where the models were estimated by using maximum likelihood estimators. First, within the group deviation transformation, we removed the individual effects and time effects, and then use MLE estimation. Lee and Yu (2010) considered fixed effects model, which was more robust and computationally simpler than random effects model. Based on their study, we consider a fixed-effect spatial model. We also perform a Hausman test and report the results. 3.3. Spatial weights matrix A crucial question for estimating Eq. (3) or (4) is the specification of spatial weights matrix. The traditional distance weights matrix considers only the geographic distance and ignores the influence of the economies scales of the adjacent provinces. Thus, we also resort to an economic distance measure, which allows the spatial correlation to depend on economic characteristics. In addition, because of the effect of the economically developed regions on the backward areas, adjusting the single distance as the weights of the neighboring economies is necessary. In this article, we focus on the following several types of spatial weight matrices: (1) The binary contiguity spatial weights matrix is measured based on the geographic location of 30 provinces. The wij is unity if connected and zero otherwise. (2) The geographic spatial weights matrix is constructed using the great circle distance and implemented in Stata software using the china_spatdwm.zip package (Yu, 2009). (3) The economic spatial weights matrix is written as:

1  wij ¼  GDPi  GDPj  þ 1 (4) The geographic and economic weighted spatial weights matrix is measured as:

  wij ¼ GDPi
GDPj
exp  Dij (5) The inverse geographic and economic weighted spatial weights matrix is given by:

  1  exp  Dij wij ¼  GDPi  GDPj þ 1 where wij is an element in the predefined weights matrix W corresponding to observation pair i, j. Dij represents the geographic distance between locations i and j, which is calculated as the great circle distance based on the latitude and longitude coordinate data of the 30 provinces. GDPi denotes the annual average of GDP per capita from province i, measured in Yuan (current price). All these matrices are row-standardized and the elements on the main

diagonals are 0. 3.4. Data selection and preprocessing 3.4.1. Calculation of CO2 The CO2 emission data of each province in China from 1995 to 2013 are derived from the China Energy Statistical Yearbook. There are 9 types of final energy consumption in the provinces: coal, coke, crude oil, gasoline, kerosene, diesel, fuel oil, natural gas, and electric power. Electric power is produced by other energy sources such as coal. Greater than 90% of China’s thermal electricity is from coal. Thus, the calculation is not repeated. The calculation method is provided by the IPCC Guidelines (IPCC, 2006). The carbon dioxide emission intensity is the carbon dioxide emissions divided by the provincial GDP; that is, the per unit GDP emissions. The estimation formula is as follows:

CO2i ¼

8 X

Ei
Ti
NCVi
CEFi
COFi


i¼1

8 44 X ¼ E
qi 12 i¼1 i

where Ei represents the consumption of energy type i; Ti , NCVi , CEFi , and COFi represent the reciprocal of the coefficient to transform standard coal, average net calorific power, carbon emission coefficient, and carbon oxygenation efficiency of ith energy, respectively; 44/12 represents the carbon conversion coefficient; and qi represents the CO2 emission factor of ith energy. Table 2 shows the carbon dioxide emission factors for each energy type. The carbon dioxide calculation approach is referred to in Li et al. (2016). 3.4.2. Selection of control variables Population size is the total population at the end of the year (10,000 persons) from the National Bureau of Statistics (data range: 2005e2013) and the Provincial Statistical Yearbook for various provinces (data range: 1995e2004) (Zhou et al., 2013). The FDI intensity of the GDP per unit is the ratio of provincial FDI to GDP. In this paper, the dollar-denominated FDI is converted into RMBdenominated currency at the average exchange rate of the year. FDI uses the foreign investment amount [FDI (million U.S. dollars)] from the Wind database; trade openness is the ratio of total imports and exports to GDP; the dollar-denominated total amount of imports and exports is converted into RMB-denominated at the average annual exchange rate of the year; and the provincial GDP and the total amount of imports and exports are from the CCER financial and economic database. Industrial structure is the ratio of the added value of the secondary industry of the provinces to the GDP of the province from the China National Bureau of Statistics. The ECS is the ratio of coal consumption in each province to the total energy consumption in the province, which is from the China Energy Statistical Yearbook. Urbanization (URB) is the province’s urban population of its total population (urban population data was replaced with the non-agricultural population data due to missing of data). These variables are often considered influencing factors of environmental quality and are usually chosen as control variables (see Table 3). 4. Estimation results and discussion 4.1. Spatial dependence test We first attempt to use Moran scatter plots to visually reveal the spatial dependence of CO2 emission intensity using the 1995 and 2013 data, respectively, which corresponds to the economic distance spatial weights matrix. A Moran scatter plot is used to study the dependence of local space (Anselin, 1993). The horizontal X-axis

Y. Wang, X. He / Journal of Cleaner Production 218 (2019) 498e510

503

Table 2 CO2 emission factor for the types of energy. Energy type

CO2 emission factor (t CO2/toe)

Energy type

CO2 emission factor (t CO2/toe)

Coal Coke Crude oil Gasoline

2.769 3.134 2.147 2.029

Kerosene Diesel oil Fuel oil Natural gas

2.104 2.168 2.265 1.642

Table 3 Symbols, definition and descriptive statistics. Variables/Symbols

Description

Mean

CO2 intensity (toe/billion Yuan) Pop (10,000 persons) FDI (%) TR (%) lNDUSTY (%) ECS(%)

Carbon dioxide emissions divided by the provincial GDP.

58,634.57 47,490.99 5091.29 427,497.40

Total population at the end of the year. FDI intensity (ratio of FDI actually used foreign investment amount to GDP) Trade openness (ratio of total imports and exports to GDP). Industrial structure (ratio of the added value of the secondary industry to GDP of the province). Energy consumption structure (ratio of coal consumption to the total energy consumption in each province). Urbanization (ratio of the province’s urban population to the province’s total population). Take natural logarithm of GDP per capita. Square of the natural logarithm of GDP per capita. Cube of the natural logarithm of GDP per capita.

4276.81 2.90 56.96 45.71 97.97

2600.96 3.00 149.42 7.97 33.42

481.00 0.07 0.00 19.21 27.30

11,430.00 24.25 1440.89 62.42 230.62

42.46 9.49 90.88 877.24

17.20 0.87 16.73 242.23

0.32 7.51 56.40 423.55

89.61 11.51 132.57 1526.43

URB(%) lnGDP(Yuan) lnGDP2 lnGDP3

Std. Dev. Min

Max

Fig. 2. Moran scatter plots for CO2 emission intensity in 30 provinces under the economic distance spatial weights matrix for 1995 (left) and 2013 (right).

is the attribute value (carbon emission intensity) after normalization of each unit, and the vertical Y-axis represents the normalized average value of the surrounding area attribute values. The scatter diagrams are divided into the four quadrants by horizontal X and vertical Y axes labeled as “high-high” (first quadrant), “low-high” (second quadrant), “low-low” (third quadrant), and “high-low” (fourth quadrant), respectively. If the high and high values are clustered together, and the low and low values are clustered together, it indicates positive spatial autocorrelation (first quadrant and third quadrant); the converse is true if the high and low values exhibit negative spatial dependence (second quadrant and fourth quadrant). Fig. 2 shows that most provinces are located in the first and third quadrants, implying that there exists positive spatial dependence of carbon pollution among China’s provinces. It is clear that the local Moran’s I value was 0.06 in 1995 but rose to 0.13 in 2013. This confirms the above conjecture that a province’s CO2 emissions affect other provinces via the economic linkage. As you can see, the first quadrant of the figure shows that bilateral economic result in higher CO2 spillover in western China, such as Shanxi, Ningxia, Neimenggu, and Gansu; the third quadrant of the figure displays that the spillover effects of a province’s CO2 emissions on its economic neighbors are comparably

small in eastern China, such as Beijing, Anhui, Shandong and Zhejiang. 4.2. Choice of the spatial models To manage the potential spatial dependence, an appropriate spatial econometric model should be utilized. In the first step, however, the spatial dependence should be tested. Specifically, this paper preliminarily investigates the existence of spatial dependence on data by Moran scatter plots. Next, we use five types of spatial weight matrices to dig out the spatial effects, test the adaptability and validity of the weights matrix, and select the appropriate spatial model. Spatial correlation indicators mainly include the global Moran’s I (Moran, 1948) and Geary’s C (Geary, 1954), which examines the spatial dependence effect of the entire spatial sequence. Moran’s I index can be written as:

Pn Pn I¼

i¼1



j¼1 wij ðxi  xÞ Pn 2 i¼1 ðxi  xÞ

xj  x

Geary’s C is defined as:

 ; I2½1; 1

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Y. Wang, X. He / Journal of Cleaner Production 218 (2019) 498e510

Table 4 Comparison of spatial weight matrices’ test results. Spatial weights matrix

Binary contiguity

Geographic distance

Economic distance

Geo-economic

Inverse Geo-economic

Moran’ I Z(I) Geary’ C Z(I) LM-err LM-lag

0.170 5.675*** 0.812 5.543*** 0.470 7.856**

0.014 1.078 1.016 1.011 1.318 1.045

0.076 2.793** 0.912 2.939** 0.776 3.738*

0.206 5.554*** 0.830 4.152*** 0.019 10.671***

0.171 4.368*** 0.848 3.483** 0.002 8.503***

Notes: ***, **, and * indicate significance levels of 1%, 5%, and 10%, respectively. All these matrices are row-standardized. The main diagonal is 0.

4.3. Selection of grouping variable and estimation of critical value

 2 P P ðn  1Þ ni¼1 nj¼1 wij xi  xj h i; C2½0; 2  C¼ P P Pn n n 2 2 i¼1 j¼1 wij i¼1 ðxi  xÞ where wij is an element of a predefined weights matrix; xi and xj are the variable of interest for locations i and j (with mean x). A Moran’s I index greater than 0, equal to 0, or less than 0 represents positive correlation, noncorrelated, and negative correlation, respectively. Conversely, a Geary’s C index greater than 1, equal to 1, or less than 1 represents negative correlation, noncorrelated, and positive correlation, respectively (Anselin, 1995). In addition, a series of Lagrange Multiplier (LM) tests are performed not only for determining the existence of spatial dependency, but also for testing on spatial model selection. Here, we are interested in testing the relevant model for application to the EKCdthat is, SAR (LM-lag) or SEM (LM-err) models. The spatial specification selection should be decided based on the most significant LM test statistic. For instance, since the LM test statistics yielded higher values for SEM than for SAR, Pandit and Laband (2007) favored SEM over SAR. Specifically, the global spatial autocorrelation index of the explanatory variables is a two-tail bilateral test, rather than a one-tail unilateral test (i.e., only positive spatial autocorrelation is considered), in which the OLS regression must be performed first, and Eq. (2) is the benchmark equation for LM tests. Table 4 reports the results of spatial effects tests and pairwise LM comparisons. According to the values of Moran’s I and Geary’s C, it can be observed that the index values lie within range of 0e1, and pass the Z (I)-value significance test for all spatial weight matrices, except the one based on geographic distance. This indicates that positive CO2 spatial dependencies exist among Chinese provinces, and using a spatial panel model is feasible and necessary. When contrasting LM-lag results with LM-err results, LM-lag test statistics have higher values and are significant, but none of the LM-err test statistics are significant. Thus, we have selected the SAR model relative to the SEM model to analyze the determinants and spatial interaction effects of CO2 emissions.

The spatial EKC model from Eq. (3) can capture the contemporaneous and spatial dependence of CO2 emissions. However, regional economic development is unbalanced in China, which presents the characteristics of a high east and low west, and a prosperous south and depressed north. If the entire sample is divided into several subsamples for regression, whether or not the coincident results could be obtained? To date, there’s no evidence that China’s CO2 emissions are characterized by spatial aggregation. Further complicating this matter is the fact that the determined groupings in traditional grouping methods cannot be tested by rigorous methods of statistical inference, thus making the estimated results less accurate. To overcome the arbitrariness and subjectivity issues of sample segmentation, a pair-wise correlation coefficients matrix is calculated using the Pearson correlation coefficient to identify the three largest candidate grouping variables, and then the fixed-effect panel threshold regression is used to estimate the threshold value of the grouping variable (the threshold variable). Table 5 reports time-series averages of yearly cross-sectional correlation, starting in 1995 and ending in 2013. According to the magnitude of the correlation coefficients between CO2 emission intensity and its influencing factors, ECS, FDI, and TR are in the top three in turn and are selected as candidate grouping variables. Next, we evaluate the critical value of each selected threshold variable using a threshold regression model so that we can cluster the 30 provinces of China in the sample into different regions. In this subsection, we rewrite Eq. (2) into Eq. (5) as a general panel threshold model, which allows evaluating the main drivers of CO2 emissions under different groups.

(

’

ln CO2 intensityi;t ¼ ti þðd1 Þ Xi;t þ εit ; qi;t  g ln CO2 intensityi;t ¼ ti þðd2 Þ’ Xi;t þ εit ; qi;t > g

(5)

where Xi;t is the vector formed by all independent variables on the right side of Eq. (2). Since GDP per capita is used as a core variable for the EKC analysis, the GDP per capita is selected as the regimedependent variable. The variable qi;t is the threshold variable, and

Table 5 Time-series average of cross-sectional correlations.

CO2 intensity GDP Pop FDI TR INDUSTY ECS URB

CO2 intensity

GDP

Pop

FDI

TR

INDUSTY

ECS

URB

1.000 ¡0.332 ¡0.239 ¡0.465 ¡0.385 0.163 0.805 ¡0.172

1.000 0.172 0.613 0.776 0.043 0.323 0.588

1.000 0.072 0.106 0.365 0.061 0.273

1.000 0.540 0.005 0.436 0.381

1.000 0.206 0.454 0.522

1.000 0.320 0.127

1.000 0.252

1.000

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505

Table 6 Test result of threshold effects. Variables

Situation

Threshold

F-stat [P-value]

The 95% confidence interval

(1) lnECS

Single-Threshold Double-Threshold Triple-Threshold

4.618 3.680; 4.371 3.680; 4.371; 4.623

57.06 [0.003] 85.84 [0.000] 55.22 [0.617]

[4.613,4.618] [3.586,3.712]; [4.358,4.374] [3.586,3.712]; [4.358,4.374]; [4.615,4.626]

(2) lnFDI

Single-Threshold Double-Threshold Triple-Threshold

1.856 1.855; 1.898 0.350; 1.855; 1.898

15.03 [0.507] 14.67 [0.357] 5.09 [0.850]

[1.722,1.877] [1.831,1.916]; [1.888,1.903] [0.349,0.355]; [1.831,1.916]; [1.888,1.903]

(3) lnTR

Single-Threshold Double-Threshold Triple-Threshold

2.993 2.993; 3.274 2.156; 2.993; 3.274

12.14 [0.573] 36.78 [0.023] 10.29 [0.833]

[2.744,3.008] [2.975,3.008]; [3.229,3.308] [2.011,2.159]; [2.975,3.008]; [3.229,3.308]

Table 7 Grouping results. High-energy structure group (12) Mean (lnECS) > 4.618

Low-energy structure group (18) Mean (lnECS)  4.618

Hebei, Shanxi (山西), Neimenggu, Jilin, Heilongjiang, AnHui, Jiangxi, Shandong, Henan, Guizhou, Shanxi (陕西), NiXia

Beijing, Tianjing, Liaoning, Shanghai, Jiangsu, Zhejiang, Fujian, Hubei, Hunan, Guangdong, Guangxi, Hainan, Chongqin, Sichuan, Yunnan, Gansu, Qinghai, Xinjiang

g is the threshold parameter that divides the equation into two regimes with coefficients d1 and d2 . The parameter ti is the indi-

vidual fixed effects. εi;t is i:i:d: The three rows in Table 6 correspond to three different regressions of Eq. (5) using alternative threshold variablesdthat is, ECS, FDI, or TR. Additionally, for each model with a specific threshold variable, we also conduct the single-, double-, and triple-threshold regressions, and test whether multiple thresholds exist (that is, multiple regimes). According to the single-, double-, and triple-threshold statistical tests reported in Table 6, the single- or double-threshold estimator of the energy structure is statistically significant at the 1% level, it can be used as a grouping variable. However, for all the three types of threshold tests, any threshold estimators of both the industrial structure and urbanization ratio are not significant, so they can’t be used as grouping variables. Taken together, there exists the

Table 8 MLE Estimation for the Spatial Autoregressive Panel Model for full sample. Full-Sample

(1) BW

Main variables: lnGDP 14.550*** (-6.424) lnGDP2 1.510*** (6.315) lnGDP3 0.053*** (-6.365) lnPop 0.481*** (4.025) lnFDI 0.012 (-1.044) lnTR 0.003 (0.569) lnINDUSTY 0.547*** (7.777) lnECS 0.966*** (20.723) lnURB 0.000 (-0.027) 0.157*** Spatial l (3.406) R2 0.884 Hausman test 6.11 P-value [0.635]

(2) GW

(3) EW

(4) GEW

(5) IGEW

14.467*** (-6.414) 1.515*** (6.370) 0.054*** (-6.470) 0.536*** (4.431) 0.021* (-1.852) 0.005 (1.068) 0.536*** (7.619) 0.966*** (20.818) 0.003 (0.271) 0.255*** (4.066) 0.886 51.79 [0.000]

15.391*** (-6.801) 1.597*** (6.675) 0.057*** (-6.739) 0.396*** (3.274) 0.015 (-1.315) 0.002 (0.485) 0.594*** (8.324) 0.973*** (20.737) 0.008 (0.638) 1.531** (2.272) 0.885 117.01 [0.000]

15.445*** (-6.792) 1.597*** (6.643) 0.056*** (-6.678) 0.449*** (3.725) 0.009 (-0.823) 0.002 (0.359) 0.562*** (7.898) 0.981*** (20.842) 0.001 (0.063) 0.039 (1.109) 0.883 42.39 [0.000]

14.628*** (-6.471) 1.514*** (6.340) 0.054*** (-6.375) 0.488*** (4.082) 0.008 (-0.690) 0.002 (0.436) 0.546*** (7.770) 0.965*** (20.711) 0.000 (0.003) 0.122*** (3.496) 0.880 14.01 [0.081]

Notes: the t statistics in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1, and bold typeface indicates a spatial dependence coefficient.

significant evidence of threshold effect which separates the samples based on energy structure. We use the stata14.0’s xthreg command for implementing this model (Wang, 2015). The sample segmentation is performed as follows. First, the annual average of energy structure variables for each province from 1995 to 2013 is calculated. By comparing the calculated annual mean with the estimated single-threshold, we can cluster the China’s 30 provinces into two classes. A high-energy structure group is set where the average of the energy structure variables is greater than its threshold. Conversely, a low-energy structure group is set where the average of the energy structure variables is less than its threshold. Next, the samples are divided according to the double-threshold of energy structure variables, since the data availability precludes this case. The final grouping results are presented in Table 7. Although members of the groups are geographically far or adjacent, the energy structure is the same. The 12
12 dimensional weights matrix for a low-energy structure group is constructed separately based on the five definitions of neighborhood, as described in section 3.3. Again, the 18
18 dimensional weights matrix for a high-energy structure group is constructed in a similar way. Thus, we repeat the SAR analysis using subsamples with high- or low-energy structure provinces. This enables us to test whether energy consumption structure would obviously and asymmetrically affect the spatial dependence of CO2 emissions.

4.4. Analysis for spatial dependence Table 8 firstly shows the full-sample panel SAR estimator results for Eq. (3). The five columns in Table 8 correspond to five different SAR regressions under alternative definitions of the spatial dependency. The columns labeled “BW”, “GW”, “EW”, “GEW”, and “IGEW” are the spatial weight matrices defined as the binary distance, geographic distance, economic distance, geo-economic weighted distance, and inverse geo-economic weighted distance, respectively. The command we used in our econometric estimation is the “Stata 13.0” xsmle command. From the EKC’s perspective, the estimated results of Table 8 show that the first, second, and third term coefficient of the GDP per capita are statistically significant at the 1% level; the symbols are negative, positive, and negative, respectively. This result means a non-linear relationship between CO2 emission intensity and GDP,

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Y. Wang, X. He / Journal of Cleaner Production 218 (2019) 498e510

Table 9 MLE Estimation for the Spatial Autoregressive Panel Model: High vs. Low Group. Variable Labels

Panel A: High-energy structure group (1) BW

lnGDP lnGDP2 lnGDP3 lnPop lnFDI lnTR lnINDUSTY lnECS lnURB Spatial l R2 Hausman test P-value

(2) GW *

8.611 (-1.819) 0.860* (1.725) 0.030* (-1.701) 1.946*** (4.790) 0.060*** (3.175) 0.007 (-0.926) 0.150 (1.093) 0.839*** (12.300) 0.001 (0.046) 0.371*** (5.467) 0.878 37.00 [0.000]

*

5.774 (-1.741) 0.566 (1.594) 0.019 (-1.533) 2.102*** (6.745) 0.045** (2.445) 0.009 (-1.333) 0.090 (0.735) 0.844*** (13.179) 0.010 (0.783) 0.483*** (6.355) 0.895 4054.35 [0.000]

(3) EW

Panel B: Low-energy structure group (4)GEW

***

13.388 (-3.966) 1.371*** (3.775) 0.048*** (-3.723) 2.341*** (7.008) 0.055*** (2.979) 0.011 (-1.621) 0.198 (1.620) 0.960*** (14.840) 0.002 (0.186) 16.205*** (5.236) 0.898 519.61 [0.000]

**

8.354 (-2.456) 0.830** (2.273) 0.029** (-2.206) 1.900*** (5.920) 0.061*** (3.232) 0.009 (-1.194) 0.169 (1.341) 0.857*** (12.748) 0.001 (-0.054) 0.241*** (4.076) 0.886 174.91 [0.000]

(5)IGEW

(1) BW

***

9.031 (-2.652) 0.906** (2.479) 0.032** (-2.424) 1.847*** (5.742) 0.059*** (3.117) 0.009 (-1.262) 0.180 (1.418) 0.867*** (12.885) 0.006 (0.427) 0.212*** (3.780) 0.885 82.90 [0.000]

(2) GW ***

11.239 (-3.212) 1.172*** (3.195) 0.042*** (-3.282) 0.330** (2.415) 0.011 (-0.780) 0.008 (1.066) 0.606*** (7.071) 1.117*** (16.880) 0.085*** (2.966) 0.080* (1.712) 0.896 48.80 [0.000]

(3) EW ***

11.711 (-3.604) 1.235*** (3.634) 0.045*** (-3.776) 0.390*** (2.789) 0.020 (-1.408) 0.006 (0.907) 0.600*** (7.163) 1.130*** (17.423) 0.104*** (3.742) 0.119 (1.509) 0.901 47.09 [0.000]

(4)GEW ***

12.316 (-3.815) 1.291*** (3.813) 0.046*** (-3.938) 0.339** (2.523) 0.015 (-1.116) 0.005 (0.761) 0.614*** (7.256) 1.131*** (17.340) 0.097*** (3.498) 7.275 (0.955) 0.900 126.87 [0.000]

(5)IGEW ***

10.910 (-3.347) 1.137*** (3.326) 0.041*** (-3.416) 0.344*** (2.589) 0.012 (-0.847) 0.004 (0.704) 0.607*** (7.295) 1.124*** (17.460) 0.088*** (3.184) 0.094** (2.336) 0.900 1431.87 [0.000]

9.953*** (-3.068) 1.038*** (3.053) 0.037*** (-3.145) 0.357*** (2.713) 0.010 (-0.703) 0.004 (0.632) 0.604*** (7.337) 1.118*** (17.546) 0.084*** (3.080) 0.132*** (3.405) 0.896 504.51 [0.000]

Notes: the t statistics in parentheses, ***p < 0.01, **p < 0.05, *p < 0.1, and bold typeface indicates a spatial dependence coefficient.

indicating that the EKC pattern is neither linear nor a U-curve but an “N-shape” relationship. That is, as income levels continue to increase, environmental quality levels first increase, then decrease, and eventually increase again. This study finds a strong correlation against the effectiveness of the EKC hypothesis. This article focuses on the EKC hypothesis and, more importantly, an analysis of five given neighborhood measures to identify which one is more capable to capture the spillover effects of CO2 emissions. As shown in Table 8, aside from the geo-economic distance measure, the estimated values of the spatial lag term for CO2 emission intensity are positive and statistically significant at the 1% level for all distance measures. The estimated l-value associated with the economic distance is the largest compared to other alternative implementations of neighborhood measures. Moreover, the estimated value of l from economic neighborhood measure is 6 times higher than its geographic measure counterpart. This confirms our conjecture that economic proximity is the predominant channel for the entire dependencies in CO2 emissions. The estimation results obtained from the SAR specification of GW, BW and IGEW are ranked as second, third and fourth, respectively. This is consistent with the well-established empirical finding that crossprovince spillover effects of CO2 emissions are closely related to simple adjacency or geographical distance. Next, Table 9 shows the regression results of grouped panels to quantify the spatial interdependent degree of CO2 emission intensity in each group, and to investigate their heterogeneous effects. The comparison is made under the high (Panel A) and low (Panel B) energy structure groups. Although the geographies of provinces within each group are far apart or similar, they are structurally identical. With regard to the existence of an EKC, it is shown that almost all models are significant, which indicates that the relationship between CO2 and GDP per capita is not of the EKC type, but is an N-shaped curve. GDP shows a monotonic decreasing relationship with CO2 only in case for specification of GW. Furthermore, as argued previously in section 4.3, sample segmentation caused by the energy structure variables can capture non-uniform relations between provincial CO2 emissions and its influencing factors. If the energy structure variable is a good

measure of grouping by itself, the difference between two estimated spatial l-coefficients should be markedly unequal for these two sub-samples. Consistent with this position, for Panel A, the coefficients of WlnCO2 intensityit are significantly positive at the 1% level across all weight matrices. For all the selected neighborhood measures in Panel B, except for geographic distance and economic distance, the estimated l-values for the remaining measures of distance are statistically significant and are markedly smaller. This might be due to the fact that the uneven economic development in China causes the emergence of polarized-like features on spatial distributions of CO2 emissions. As shown in Table 7, the less developed regions in Panel A include Shanxi, Neimenggu, Ningxia, Qinghai, Guizhou, Shanxi, and other provinces, where the layout of thermal power industries with characteristics of high energy consumption and high emissions are relatively concentrated. The more developed regions Panel B includes Beijing, Guangdong, Shanghai, and other provinces where the economic development relies heavily on the export of energy and semi-processed goods in central-western regions, thereby transporting their emissions to the exporting regions (Liu et al., 2017). For this reason, our results indicate that, irrespective of the type of W, the SAR model of Panel A has a higher degree of spatial correlation of CO2 emissions than the SAR model of Panel B. Again, in Panel A, the estimated l-value is 30 times higher in the spatial estimation of EW than in the estimation conducted with GW spatial model, while they seem to be insignificant in Panel B. Tables 8 and 9 report the estimated results of different panels’ SAR estimates of CO2 intensity on the various influencing factors for Eq. (3) by MLE estimator. The main results of the SAR estimation are summarized as follows: (1) Overall, the results suggest a positive and significant crossborder spillover effect of CO2 emissions even among provinces that are not neighbors with one another. Thus, a higher (lower) level of CO2 emissions in the surrounding areas of a province leads to increased (decreased) CO2 emissions in this province and vice versa. In terms of transmission mechanism, a province imports energy-intensive products and, to a

Y. Wang, X. He / Journal of Cleaner Production 218 (2019) 498e510

large degree, exports their emissions to the exporting provinces through important linkages such as geographical proximity and bilateral economic ties. (2) Besides, the spatial dependence coefficients of CO2 emission intensity are markedly different with regard to geographic distance and economic distance. In most cases, the spatial correlation measure based on economic distance yields the largest estimate of WlnCO2 intensityit and outperforms other alternative definitions of the spatial correlation, implying that bilateral close economic proves to be well-suited to dig out the spatial correlation in CO2 emissions. In the compound weights matrix for CO2 emissions, the estimated l-values obtained from the specification of GEW and IGEW are generally very small and are significant, implying that both channels play important roles. Thus, when setting the spatial weights matrix by considering only the geographic distance weights and ignoring the economic distance weights, it is likely to bring a big bias to the model parameter estimates. (3) For Table 9, comparing the estimated l-values for all distance measures reveals that the SAR specification operates best in Panel A but worst in Panel B in terms of digging out the spatial effects. This may be an indication that segmenting sample using threshold regression is effective in capturing non-uniform and heterogeneous characteristics of the spatial dependency in the EKC, and that different energy-structure levels are of crucial importance for CO2 emissions’ spatial structure. Thus, the spatial attributes of CO2 emissions can be linked not only to bilateral economic ties, we argue, but also to heterogeneous regions in economic inequality. Finally, the fixed- and random-effects tests for the SAR regression are presented at the bottom of Tables 8 and 9. The Hausman test statistic shows that the fixed effects are superior to the random effects model. 4.5. Influence of control variables The population-size variables are positively correlated with the CO2 emission intensity and significant at statistical levels of 1% in Tables 8 and 9. It is observable that, irrespective of the specification of W, a rise in population-size by 1% will cause carbon emissions to raise by approximately 0.4%, 2.0% and 0.3% for entire sample, highand low-energy structure groups, respectively. The differences among the estimated coefficients are generally rather large, by comparison, indicating that a greater impact of population-scale on CO2 emissions is concentrated on samples including centralwestern provinces of China. In the central and western regions, the population is much greater, the more energy is used directly and indirectly, producing the greater the amount of CO2. In the eastern coastal areas, the role is relatively small. In addition, when neglecting the grouping, the SAR model (Table 8) underestimates the impact of population size. FDI is negatively correlated with CO2 emission intensity in the full-sample spatial SAR specifications but not statistically significant. Notably, Panel A is significant, and Panel B is nonsignificant after grouping. In theory, the impact of FDI on carbon emissions is uncertain. FDI promotes the improvement of technological progress and energy efficiency in a province and subsequently reduces the intensity of carbon emissions. The transfer of some international energy-intensive industries may increase the total industrial energy consumption of a province, and then increase its carbon emissions, offsetting the reduced carbon emissions of the improved productivity resulting from FDI. The empirical analysis of this paper represents the second possibility; thus, the FDI coefficient is positive.

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The estimates of trade openness are nonsignificant either before or after grouping, implying that trade openness fails to exert any impact on CO2 emissions after controlling for spatial dependency. Firstly, most of the existing research show that trade openness has a positive or negative impact on carbon emissions, which is in line with Kohler (2013)’s review. It can also be inferred that carbon emissions are elastic with respect to trade openness. Secondly, it is also noted that the average cross-sectional correlation between trade openness and GDP per capita is nearly 77% as shown in section 4.3. It appears that for all SAR specifications, the role of trade openness disappears possibly due to its mechanical relation with GDP per capita. Generally speaking, there exists bi-directional causality between trade openness and economic growth, possibly causing its role to be covered by the effects of economic growth. For the industrial structural variables, the estimated coefficients are positive and statistically significant at the 1% level in the whole sample and Panel B but nonsignificant in Panel A. This implies that CO2 emissions are elastic with respect to industrial structure, and a 1% rise in industrial value-added raises carbon emissions within a range of 0.5%e0.6%. The result is in line with Lin et al. (2017), who found that industrialization has different impacts on CO2 emissions across Chinese regions which are at different stages of development. The energy structure is positively correlated with the carbon emission intensity in all cases, and it is statistically significant. By comparing Table 8 with 9, it is observable that, irrespective of the SAR specification considered, the differences among the estimated values are generally very intuitive and obvious, and that a 1% rise in energy consumption enhances carbon emissions by around 0.9%, 0.8% and 1.1% for full sample, high- and low-energy structure groups, respectively. One reason for this might be that the resulting differences are predominantly caused by different levels of economic development. At the provincial level, provinces in which energy consumption causing the augment of CO2 emissions are primarily concentrated in central-east and coastal China (Panel B). Since the reform and opening up in China, these provinces have taken the lead in development; a substantial increase in energy consumption has occurred, causing a rapid increase in carbon emissions. The energy and industrial structures have had a significant, direct impact on carbon dioxide emissions, for example, in the conclusions of Shao et al. (2011) and Yin et al. (2015). The urbanization ratio is statistically nonsignificant in all provinces group, while it is significantly positive for Panel B but nonsignificant for Panel A. The results indicate that urbanization causes the increase of CO2 emissions, which is over-concentrated in areas with better economic and low-energy structures. As discussed by Zhang et al. (2017) and Alvarado et al. (2018), the impact of urbanization on CO2 reduction is constrained by other factors such as regional economic development level and energy structure. Moreover, some benefits of urbanization are neutralized by increased CO2 emissions, partly due to a huge demand for transportation, infrastructure and energy-intensive products in urban areas (Liu and Bae, 2018). Overall, this paper utilizes SAR model of EKC-related to examine to what extent provinces’ bilateral economic and geographical linkages affect their CO2 emissions, both the entire sample and in provinces clustered by energy-structure using threshold regression for full sample. The model predicts that using economic measure to construct spatial lag specifications has a greater explanatory power than using other simple adjacency measures. In addition, the spatial correlation of CO2 in the high-energy structure group is greater than that in the low-energy structure group (Panel A vs. Panel B). The relationship between GDP per capita and CO2 emission intensity, with the effects of spatial interdependence consideration, is adequately described by the N-shaped curve, indicating

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that the relationship between society and environment follows a process of promotion, contradiction, and coordination. Besides, some criticisms on the EKC-pattern reveal that it does not emerge in cases of pollutants with long-term effects, especially for CO2 emissions and greenhouse gases (Dinda, 2004; Kaika and Zervas, 2013). Our results of the spatial panel estimators also show that the EKC-pattern do not survive in China. Another noteworthy change is that the magnitude and/or statistical significance of the estimated effects and control variables are sensitive to the spatial diffusion and distribute features of CO2 emissions, indicating the importance of spatial dependency and aggregation effect in the carbon emissions EKC model. Compared with the traditional nonspatial estimator, the spatial tool can make up for the lack of CO2 spatial transmission mechanism via the investigated linkages and reduce the computational complexity such as the VAR, VEC, or ARDL models; compared with the existing spatial effects-based work, our approach allowed us to go beyond general geographic proximity analyses, overcome the limitations of spatial EKC studies associated with homogeneous regions, and improve the accuracy of spatial dependency structure. 5. Conclusions and policymaking recommendations Modeling for spatial interactions in the EKC has been a hot topic in the literature; however, the focus has been only on the existence of spatial effects. In many cases, the spatial interactions are attributed to geographic closeness, whereas the role of economic proximity tends to be overlooked. Thus, this research attempts to define non-geographic proximities and introduces a spatial econometric tool to reconsider the relationship between economic growth and CO2 emission intensity. This paper compares the performance of different definitions of the spatial dependency to more accurately reflect the degree of spatial interactions among China’s provinces by using simple adjacency, geographic distance, economic distance, geo-economic weighted distance, and inverse geoeconomic distance. The conclusion is that the economic matrix outperforms other metrics in most cases. Furthermore, this paper introduces a threshold effect model to divide the region and subsequently explores the spatial distributions and determinants of CO2 emission intensity by sub-regions. This paper reveals heterogeneous characteristics of the spatial dependency in the CO2 emissions EKC for samples segregated on energy structure. By contrast, a larger spatial correlation of CO2 emissions is confined to samples with high-energy structure, irrespective of the specification of matrix form. Additionally, the estimation results indicate that when the spatial effects are fully accounted for, within the sample period, the relationship between CO2 emission intensity and GDP per capita in China exhibits an N-shape curve instead of an inverted U-shape one. The paper mainly considers the potential cross-border effects among provinces, and then assesses the difference of implied dependency with the heterogeneity of CO2 emissions. This method could be further applied in studying that the gradually increasing global economic integration results in the spatial spread of environmental pollution in various countries. A useful extension of this work would be to model pollution spillover in other countries for predicting the influence of pollution spillover. Furthermore, it would also be interesting to expand the focus of the spatial neighborhoods described by bilateral economic relations to different institutions, sectors, nations, and air pollution categories, in order to investigate spatial spillovers among them. Besides, carbon reduction may require government intervention at a national level, i.e. local governments need to learn more from others who are proximate, geographically and/or economically. Then, applying joint regional policies such as regulation and supervision

can improve efficiency. Finally, our results also could provide some valuable information for policy making. First, this article provides the straightforward policy advice: Provincial CO2 emissions not only exhibit a positive and significant cross-border spillover effect, but also are strongly related to bilateral economic closeness. Thus, policymakers should encourage interprovincial cooperation in dealing with CO2 emissions. Northwest provinces should strengthen cooperation with the centraleast provinces. Specifically, more attention should be paid to cooperation in green energy development for areas such as clean development mechanisms and carbon exchange trading policy. In addition, authorities should conduct economic-related strategies and environmental regulations to increase environmental protection from bilateral economic and trade exchanges. Further, irrespective of the closeness measure considered, capturing spatial dependencies of CO2 emissions perform best in Panel A but worst in Panel B. Due to the polarized-like spatial distributions of CO2 emissions, the Chinese government should transfer the hi-tech and clean energy industries from east urban to west rural areas; improve the proportion of local processing and transformation of resources; and promote economic development in the western regions under the “Belt and Road Initiative” strategy. Second, the sub-regional policy recommendations are as follows: The determinants of CO2 emissions are polarized-like. Clearly, FDI is statistically significant in Panel A, while it appears to be insignificant in Panel B; industrial structure and urbanization are statistically significant in Panel B, whereas they appear to be insignificant in Panel A. For population size, its elasticity changes from higher positive values in Panel A to lower positive values in Panel B. Thus, for the economic-backward inland western provinces (Panel A), higher FDI and larger population size are important factors hindering CO2 reduction. Local governments should adjust the threshold of foreign investment accession in a timely manner and strengthen investment in hi-tech and emerging industries. More importantly, authorities should speed up urban-rural integrated development and encourage people to adopt a green and modern lifestyle such as low-carbon consumption and green commuting. This measure will help to alleviate local environmental problems and increase in the low-carbon awareness among the citizens. For the developed areas in the central and eastern regions (Panel B), bigger industrial structure and higher urbanization are important factors hampering governance concerning CO2 pollution. Therefore, authorities should guide the upgrading of industrial structures into a high degree of processing, high technology, and low-emissions industry. The government should also encourage the consumption of low-carbon products and tax cuts, and promote coordinated development between megacities and medium-small cities, to mitigate the adverse impacts on the environment from the rapid urbanization. In addition to the aforementioned policies, we should consider energy structure as the key to the development of low-carbon economics, irrespective of the type of region. Authorities should promote clean and renewable energy technologies innovation to replace the old coal-dominated fuel portfolio; utilize appropriate policies related to the effective consumption of energy resources and increase their investment on energy saving, energy efficiency projects. Acknowledgments The authors would like to express our gratitude to the associate Editor-in-Chief and the anonymous referees whose comments and

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