Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China

Renewable and Sustainable Energy Reviews 58 (2016) 943–951

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Renewable and Sustainable Energy Reviews journal homepage: www.elsevier.com/locate/rser

Does foreign direct investment lead to lower CO2 emissions? Evidence from a regional analysis in China Chuanguo Zhang n, Xiangxue Zhou 1 School of Economics, Xiamen University, Xiamen 361005, PR China

art ic l e i nf o

a b s t r a c t

Article history: Received 17 February 2015 Received in revised form 22 September 2015 Accepted 24 December 2015 Available online 14 January 2016

Existing studies have been concerned with the relationship between foreign direct investment and CO2 emissions in recent years. However, little attention has been paid to regional differences in China. This paper investigates the impact of FDI on China's CO2 emissions at the national and regional levels using provincial panel data from 1995 to 2010. The Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was adopted. The results suggest that FDI contributes to CO2 emission reductions in China. FDI's impact on CO2 emissions decreases from the western region to the eastern and central regions. Our findings support the pollution halo hypothesis, which claims that foreign firms can export greener technologies from developed to developing countries and conduct business in an environmentally friendly manner. & 2016 Elsevier Ltd. All rights reserved.

Keywords: Foreign direct investment CO2 emissions Regional differences

Contents 1. 2. 3.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Literature review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Model and methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4. Data source and description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Data source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. Data description. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5. Empirical results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1. Whole analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2. Regional analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7. Conclusions and policy implications. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction With economic reform, China has witnessed an impressive inflow of foreign direct investment (FDI). FDI in China increased

n

from $3.5 billion in 1990 to $121 billion in 2012. The World Investment Prospects Survey (WIPS) by the United Nations Conference on Trade and Development (UNCTAD) suggested that China will be the top prospective host country for FDI during

Corresponding author. Tel.: þ 86 592 13720898826. E-mail addresses: [email protected] (C. Zhang), [email protected] (X. Zhou). 1 Tel.: þ86 532 15192666969.

http://dx.doi.org/10.1016/j.rser.2015.12.226 1364-0321/& 2016 Elsevier Ltd. All rights reserved.

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2013–2015. However, a dramatic FDI increase may lead to increased emissions [1]. China became the world's largest carbon emitter in 2009, accounting for 24.2% of global CO2 emissions [2]. Annual average CO2 emissions growth was more than 6% from 1990 to 2010. Total carbon emissions increased from approximately 671.1 million tons in 1990 to 2260.3 million tons in 2010, according to the Carbon Dioxide Information Analysis Center (CDIAC). In response to these challenges, China created the Outline of the Twelfth Five-year Plan (2011–2015). This plan noted that CO2 emissions per unit of GDP should be reduced by 17% of the 2010 level. Investigating the effects of FDI on CO2 emissions is necessary to achieve this target. Despite considerable research, the relationship between FDI and CO2 emissions is unknown. Some studies show that FDI inflows lead to an increase in CO2 emissions [3–5]. A positive relationship between FDI in China and CO2 emissions was also found by Zhang [2], indicating that FDI is a contributory factor to this pollution. However, others indicate that the increase in FDI inflows causes a reduction in CO2 emissions [6,7]. Zheng et al. [8] noted that FDI inflows can reduce air pollution. This author used panel data of 35 Chinese cities from 1997 to 2006. In sum, the relationship between FDI and CO2 emissions remains unclear. Moreover, China has vast territory and large regional differences, and existing studies have failed to consider these differences. Therefore, this paper examines the effects of FDI on Chinese CO2 emissions from 1995 to 2010 using a panel dataset of 29 provinces. We employ the STIRPAT model to explore this effect.

2. Literature review The nexus between FDI and environmental pollution has long been debated. The most famous hypothesis supporting relationships between FDI and environmental pollution is the ‘pollution haven hypothesis’ [9,10]. According to this hypothesis, multinational firms transfer pollution-intensive industries to those countries with lower environmental regulations to circumvent costly regulatory compliance in their home countries. Therefore, developing countries become ‘pollution-havens’ and suffer more environmental pollution. Conversely, the pollution halo hypothesis [11] poses that multinational firms diffuse their clean technology in developing/hosting countries through the export of modern technologies. As a result, FDI inflows can help reduce environmental pollution. However, Grossman and Krueger [12], suggested that scale, composition and technique effects make the nexus between FDI and pollution more difficult to analyze [13]. Scale effect refers to an increase in pollution emissions and FDI-led increases in economic activity. FDI inflows can expand economic activity in host countries. If such activity remains unchanged, pollution emissions must increase. The technique effect can help reduce emissions and enhance environmental quality. Foreign firms can transfer modern technologies that are cleaner. Technology transfer contributes to improved energy efficiency and emission reductions. The composition effect can have both positive and negative environmental impacts [14]. Lax environmental regulation in host countries attracts an inflow of polluting foreign capital. This inflow causes the proportion of polluting sectors to increase. The comparative advantage theory suggests that an abundance of cheap labor will increase the attractiveness of lesspolluting labor-intensive industries. Theoretical arguments and empirical evidence of the association between FDI and CO2 emissions are inconclusive. Most studies have used multi-country analyses, and the results varied by country. One conclusion is that FDI has a positive influence on carbon emissions. Smarzynska and Wei [4] analyzed 24 transition economies in Europe, finding that FDI inflows increase CO2

emissions in host countries. This study supports the pollution haven hypothesis. Pao and Tsai [15] investigated the dynamic relationships between CO2 emissions, FDI, energy consumption and economic growth for BRIC countries. Adopting a panel cointegration framework, these authors discovered that FDI has a positive influence on CO2 emissions. Using a panel model, Almulali [3] found that FDI net inflows longitudinally increase CO2 emission in 12 Middle Eastern countries. Another conclusion is that a negative relationship exists between FDI and CO2 emissions. For example, Tamazian et al. [7] found that financial development and higher levels of FDI can help reduce CO2 emissions in BRIC countries. Lee and Brahmasrene [16] found a negative nexus between FDI and CO2 emissions in the European Union countries. Al-mulali and Tang [17] found that FDI contributes to decreased carbon emissions in gulf cooperation council countries, rejecting the pollution haven hypothesis. Mielnik and Goldemberg [18] found similar results in 20 developing countries. The final view suggests that inward FDI has no influence on CO2 emissions. For example, Perkins and Neumayer [19] found that FDI inflows have no influence on CO2 efficiency. Hoffmann et al. [20] discovered that no causal relationship between FDI and CO2 emissions exists in high-income countries. Using panel data from 1970 to 2006, Atici [21] concluded that FDI has no impact on CO2 emissions. Lee [22] investigated contributions of inward FDI to CO2 emissions using the panel data of 19 nations of the G20. The results showed that FDI inflows are not correlated with CO2 emissions. Alternatively, some studies have focused on a single country. Using time series data from Malaysia, Lee [23] and Hitam and Borhan [24] found that FDI significantly determined pollution and that increased FDI will raise CO2 emissions. Acharyya [25] used cointegration and found that FDI in India has a large positive impact on CO2, supporting the pollution haven hypothesis. However, List and Co [6] found that FDI in the United States contributed to improved energy efficiency and reduced CO2 emissions. Shahbaz et al. [26] found that globalization had an inverse impact on CO2 emissions in Turkey because FDI contributes to the transference of energy-efficient technologies to domestic firms. Sbia et al. [27] found that FDI helped reduce energy consumption and CO2 emissions in the UAE. Merican et al. [28] obtained similar results in Indonesia. More attention has been placed on Chinese environmental policy due to rapidly increasing FDI inflows and growing environmental pollution. He [13] built a simultaneous model to examine the association between FDI and carbon emissions, finding that FDI inflows lead to industrial emissions. Zhang [2] suggested that FDI has contributed to the increase in China's CO2 emissions. Cole et al. [29] investigated the effects of FDI on the environmental quality in 112 major cities of China, finding a significant positive impact on air and water pollution. This study supports the pollution haven effect. Zheng et al. [8] examined the relationship between home prices, wages, FDI and ambient air pollution using a unique cross-city panel data set. The results showed that FDI decreases air pollution in the cities included in the study. Wu and Li [30] concluded that FDI is not concentrated in pollutionintensive industries, and there is no evidence that this practice increases pollutant emission levels in Shandong province. According to Zhang [31], government targets contribute to energy savings and emissions reductions. The Chinese government attracts more FDI inflows in energy-saving technologies to help achieve its carbon reduction target. The main reason that studies have been inconclusive regarding the relationship between FDI and pollution is that stages of economic development and environmental regulations are different in different countries. Research methods and designs have also differed. Existing Chinese studies have mainly focused on the national level. Such studies have failed to consider regional

C. Zhang, X. Zhou / Renewable and Sustainable Energy Reviews 58 (2016) 943–951

differences. The results may be biased due to varying regional regulations causing unobserved heterogeneity and behaviors affecting CO2 emissions. Therefore, further research on this topic is necessary. Our findings show that FDI contributes to reduced CO2 emissions. Additionally, the impact of FDI on CO2 emissions varies by region. These findings support the pollution-halo hypothesis. These findings contribute to the existing literature and warrant attention from Chinese policymakers.

3. Model and methodology 3.1. Model Ehrlich and Holdren [32] first used the IPAT model (I¼ PAT) to analyze the effects of the growing population on the environment. This model analyses the impact of human behavior on the environment. However, it does not permit hypothesis testing because the known values of some terms determine the value of the missing term [33]. Therefore, we observe the proportionate impact of environmental change by modifying one factor and simultaneously holding other factors constant [34]. To overcome limitations of the IPAT model, Dietz and Rosa [35,36] established the STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) version to achieve the stochastic form, as follows: I i ¼ aP bi Aci T di ei

ð1Þ

where I, P, A, and T denote environmental impact, population, affluence and technology, respectively, and i indicates that those quantities vary across units. Variables a, b, c and d are estimated parameters, and e is the error term. We can add additional factors to the original STIRPAT model as long as they are conceptually appropriate for its multiplicative specification [33]. In this study, we added FDI as an additional variable and estimated an extended version of the STIRPAT model. After rearranging natural logarithms of the model, the empirical model for the panel data of CO2 emissions is specified as follows: ln CO2it ¼ a0 þ a1 lnP it þ a2 lnAit þ a3 lnT it þ a4 lnSit þ a5 lnFDIit þ a6 lnURBit þ eit

ð2Þ where A stands for GDP per capita. Population (P) and urbanization (URB) denote P. FDI is measured by the ratio of the actual amount of FDI in GDP. T is defined as different indicators in different studies. Similar to Li et al. [37], we adopt the economic output of per unit energy consumption to denote technology level (T). The larger value of this variable indicates higher technology and lower emissions. Industrial structure (S) is also considered and defined as the ratio of the tertiary industry value to the secondary industry output value. Detailed descriptions of all variables used in this study are reported in Table 1.

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3.2. Methodology We analyzed the effects of FDI on CO2 emissions at the national and regional levels. We divided China into three regions based on economic development level and geographic position. The eastern region includes 11 provinces: Liaoning, Hebei, Beijing, Tianjin, Shandong, Jiangsu, Shanghai, Zhejiang, Fujian, Guangdong and Hainan. The central region consists of 8 provinces: Heilongjiang, Jilin, Shanxi, Henan, Anhui, Hubei, Jiangxi and Hunan. The western region includes 10 provinces: Guangxi, Yunnan, Sichuan including Chongqing, Guizhou, Shaanxi, Gansu, Qinghai, Ningxia, Xinjiang and Inner Mongolia. Due to the pseudo-regression issue of nonstationary panel data regressions, determining whether data are stationary or not is essential. Each panel unit root test has certain shortcomings. Four panel unit root tests were used in this study, including the Levin, Lin and Chu (LLC) [38], Im, Pesaran and Shin (IPS) [39], Fisher-ADF and Fisher-PP [40] tests. The results for national case and regional data are displayed in Tables 2–5. The results show that time series are nonstationary in levels but stationary in first differences. This finding confirms that panel variables are all integrated of first order. Therefore, panel cointegration tests can examine the long-term equilibrium between variables. Pedroni [41] and Kao [42] cointegration tests were selected to determine long-term relationships between four panels between lnCO2, lnP, lnA, lnT, lnS, lnFDI and lnURB. Pedroni considered two types of test statistics. One is described within dimension, including panel v-, panel rho-, panel PP- and panel ADF-statistics. The other is described between dimensions, including group rho-, group PP- and group ADFstatistics. Panel PP-, panel ADF-, group PP- and group ADFstatistics provide the strongest evidence of cointegration in small samples [41,43]. Therefore, we emphasized these four statistics. Table 6 shows that the null hypothesis of no cointegration is rejected and long-term relationships exist at the national and regional levels. We then estimated the impact of FDI on CO2 emissions at the national and regional levels using fixed effects (FE), linear regression with Newey-West standard errors (N-W), feasible generalized least squares (FGLS), linear regression with panelcorrected standard errors (PCSE) and linear regression with Driscoll–Kraay standard errors (DK). This process generated 20 models. Estimation results are presented in Tables 11–14. The Robust Hausman test was first applied to choose between the fixed and random effects models. The fixed effects model is preferred according to results shown in Table 7. The modified Wald test for group-wise heteroskedasticity, developed by Greene [44], suggested that heteroskedasticity existed in FE models. Autocorrelation was also detected in FE models by the Wooldridge test [45]. A cross-sectional dependence test, developed by Pesaran [46], suggested that cross-sectional dependence existed in models 1, 11 and 16. The results of these tests are reported in Table 8. Estimation results of FE models could possibly be biased. To solve these issues, the N-W, FGLS and PCSE estimations were employed. The Newey-

Table 1 Description of the variables used in the study for the period 1995–2010. Variable

Definition

Unit of measurement

Symbol

CO2 emissions Population GDP per capita Technology level Industrial structure FDI Urbanization

Emission from fuel consumption Population at the end of year Gross domestic product divided by population Economic output of per unit energy consumption The ratio of the tertiary industry value to the secondary industry output value The ratio of actual amount of FDI in GDP Percentage of the urban population in the total population

10,000 t 10,000 persons Yuan (1952 prices) 10,000 yuan/tce % % %

CO2 P A T S FDI URB

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C. Zhang, X. Zhou / Renewable and Sustainable Energy Reviews 58 (2016) 943–951

Table 2 Results of panel unit root tests in national case. Variable

lnCO2 lnP lnA lnT lnS lnFDI lnURB

Levels

First difference

LLC

IPS

Fisher-ADF

Fisher-PP

LLC

IPS

Fisher-ADF

Fisher-PP

6.231  2.788nnn 15.679  3.066nnn 0.907  3.294nnn  0.278

10.486 0.626 20.391 3.921 2.816  1.941nn 6.557

4.992 54.539 3.248 26.440 32.934 74.26n 22.983

1.697 54.651 3.321 37.156 31.843 58.870 23.937

 6.951nnn  17.071nnn  2.530nnn  15.502nnn  7.582nnn  16.882nnn  11.897nnn

 4.832nnn  13.140nnn  1.349n  12.435nnn  5.993nnn  13.268nnn  8.917nnn

113.118nnn 260.887nnn 79.653nn 245.209nnn 132.868nnn 260.623nnn 187.042nnn

126.121nnn 322.348nnn 104.974nnn 278.706nnn 136.168nnn 286.946nnn 206.295nnn

IPS

Fisher-ADF

Fisher-PP

The lag lengths are selected using SIC. Newey-West automatic bandwidth selection and Bartlett kernel. n

Rejection of the null hypothesis at the 10% significance level. Rejection of the null hypothesis at the 5% significance level. nnn Rejection of the null hypothesis at the 1% significance level. nn

Table 3 Results of panel unit root tests in eastern region. Variable

Levels LLC

lnCO2 lnP lnA lnT lnS lnFDI lnURB nn

3.166  1.257  3.468nnn  2.735nnn  2.088nn  2.199nn  2.498nnn

First difference IPS 6.464  0.219  0.866 2.348  0.374  1.197 3.799

Fisher-ADF 1.178 24.061 28.730 11.791 21.206 28.522 7.463

Fisher-PP 0.544 23.030 5.191 23.366 20.938 17.451 3.839

LLC nnn

 5.070  10.884nnn  2.880nnn  9.304nnn  5.169nnn  9.012nnn  7.224nnn

nnn

nnn

 3.660  8.495nnn  2.773nnn  7.563nnn  3.972nnn  7.538nnn  5.475nnn

49.523 102.307nnn 41.327nnn 92.980nnn 51.960nnn 91.850nnn 73.103nnn

53.609nnn 135.351nnn 48.697nnn 102.976nnn 48.250nnn 112.606nnn 86.636nnn

Rejection of the null hypothesis at the 5% significance level. Rejection of the null hypothesis at the 1% significance level.

nnn

Table 4 Results of panel unit root tests in central region. Variable

lnCO2 lnP lnA lnT lnS lnFDI InURB

Levels

First difference

LLC

IPS

Fisher-ADF

Fisher-PP

LLC

IPS

Fisher-ADF

Fisher-PP

4.321  1.093  0.875  1.480n 1.649  1.466n  2.342nnn

6.127  0.196 3.795 1.238 2.147  1.106 1.903

1.273 16.824 8.815 9.231 5.481 20.174 8.483

0.385 20.479 0.453 9.808 4.853 17.151 13.054

 3.490nnn  10.985nnn  3.884nnn  7.790nnn  2.110nn  8.664nnn  7.490nnn

 2.193nn  8.015nnn  3.567nnn  6.693nnn  2.056nnn  6.444nnn  6.062nnn

27.529nn 81.001nnn 38.467nnn 67.808nnn 27.490nnn 67.455nnn 62.799nnn

30.922nn 94.499nnn 49.625nnn 75.103nnn 34.672nnn 65.092nnn 68.818nnn

nnn

Rejection of the null hypothesis at the 1% significance level. Rejection of the null hypothesis at the 5% significance level. Rejection of the null hypothesis at the 10% significance level.

nn n

Table 5 Results of panel unit root tests in western region. Variable

lnCO2 lnP lnA lnT lnS lnFDI lnURB

Levels

First difference

LLC

IPS

Fisher-ADF

Fisher-PP

LLC

IPS

Fisher-ADF

Fisher-PP

3.680  1.276 0.782  0.371 2.602  1.986nn 4.702

5.607 1.881 4.426 3.096 3.322  1.058 5.528

2.540 12.852 6.541 5.418 6.253 25.561 7.038

0.768 13.234 7.818 3.981 6.052 24.268 7.043

 3.499nnn  7.681nnn  4.222nnn  9.508nnn  5.256nnn  11.381nnn  5.634nnn

 2.442nnn  6.295nnn  5.421nnn  7.261nnn  4.230nnn  8.957nnn  3.995nnn

36.066nn 77.580nnn 65.827nnn 84.421nnn 53.418nnn 101.318nnn 51.140nnn

41.590nnn 92.498nnn 105.568nnn 100.627nnn 53.246nnn 109.247nnn 50.841nnn

The lag lengths are selected using SIC. Newey-West automatic bandwidth selection and Bartlett kernel. nn

Rejection of the null hypothesis at the 5% significance level. Rejection of the null hypothesis at the 1% significance level.

nnn

C. Zhang, X. Zhou / Renewable and Sustainable Energy Reviews 58 (2016) 943–951

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Table 6 Results of Pedroni and Kao panel cointegration tests. Statistics

Whole country

Eastern region Central region Western region

Panel v Panel rho Panel PP Panel ADF Group rho Group PP Group ADF Kao-ADF

 0.533 5.382  14.097nnn  6.984nnn 7.177  22.228nnn  8.382nnn  2.877nnn

 0.120 3.471  9.722nnn  4.796nnn 4.581  8.193nnn  3.915nnn  1.831nn

 1.841 2.647  14.831nnn  5.260nnn 3.646  17.930nnn  6.579nnn  1.463n

 0.316 2.942  6.351nnn  2.924nnn 4.155  13.223nnn  4.284nnn  1.339n

The lag lengths are selected using SIC. Newey-West automatic bandwidth selection and Bartlett kernel. The null hypothesis is of no cointegration.

Fig. 1. Total CO2 emissions in three regions of China during 1995–2010.

n

Rejection of the null hypothesis at the 10% significance level. nn Rejection of the null hypothesis at the 5% significance level. nnn Rejection of the null hypothesis at the 1% significance level. Table 7 Results of Robust Hausman test. Statistic F stat nnn

Whole country 27.46

nnn

Eastern region 62.50

nnn

Central region nnn

1319.77

Western region 589.80nnn

Rejection of the null hypothesis at the 1% significance level.

Table 8 Results of group-wise heteroskedasticity, autocorrelation and cross-sectional dependence test. Statistics Wald stat F stat CD stat nn

Whole country nnn

3981.24 57.301nnn 9.754nnn

Eastern region nnn

2068.02 530.725nnn  0.786

Central region nnn

120.04 176.305nnn 5.019nnn

Western region 54.34nnn 9.222nn 9.770nnn

Rejection of the null hypothesis at the 5% significance level. Rejection of the null hypothesis at the 1% significance level.

Fig. 2. Total FDI in three regions of China during 1995–2010.

Newey-West estimation for the eastern region (model 7), as its estimator addresses heteroskedasticity and autocorrelation problems in the region.

nnn

4. Data source and description Table 9 VIF tests for multi-collinearity.

4.1. Data source

Variable

Model 5

Model 7

Model 15

Model 20

lnP lnA lnT lnS lnFDI lnURB Mean VIF

1.83 2.00 2.15 1.41 1.59 1.50 1.75

3.12 5.79 2.03 2.92 1.78 1.78 3.45

2.09 1.85 1.61 1.16 1.24 2.33 1.71

2.83 2.03 2.87 1.20 1.28 1.18 1.90

This study includes annual data of 29 Chinese provinces from 1995 to 2010. Population, GDP per capita, tertiary industry value, secondary industry output value and FDI were collected from the China Statistical Yearbook and Statistical Yearbook of Provinces. GDP per capita was standardized to 1952 constant prices. Energy consumption data were taken from the China Energy Statistical Yearbook. CO2 emissions data were calculated using the IPCC formula (2006) (http://www.ipcc-nggip.iges.or.jp/). 4.2. Data description

West estimation [47] was applied to test heteroskedasticity and autocorrelation without consideration of the cross-sectional dependence. FGLS estimation is infeasible if the time dimension T is smaller than the cross-sectional dimension N. This approach tends to underestimate true variability [48]. The finite sample properties of the PCSE estimator are rather poor when N is larger than T [49]. Therefore, the Driscoll–Kraay estimation [50] is applied to eliminate deficiencies of other large T consistent covariance matrix estimators, such as the FGLS or PCSE. The latter typically becomes inappropriate when N is large [49]. We also test for multi-collinearity in models 5, 7, 15 and 20 using the variance inflation factor (VIF). Table 9 shows that VIF values are all less than 10, suggesting no multi-collinearity. In summary, we focused on the Driscoll–Kraay estimation (model 5, 15, 20) to address heteroskedasticity, autocorrelation and cross-sectional dependence issues. We emphasized the

Fig. 1 demonstrates that emissions in the eastern region were highest, with 5237.01 million tons in 2010. The western region had the lowest, with 2738.51 million tons in 2010. During 1995–2010, total emissions in the three regions trended upward and increased at a higher rate from 2001 to 2010. Fig. 2 shows that FDI distribution is similar to the distribution of CO2 emissions across the three regions. FDI in the eastern region was larger than in the central and western regions. In 2010, FDI in the eastern region was the largest with 129.16 billion dollars. This was followed by the central region with 29.67 billion dollars, and the western region with 18.21 billion dollars. FDI in the eastern region showed significant growth beginning in 2000. However, FDI in the central and western regions was relatively slow. Table 10 shows that GDP per capita changed most rapidly, while population hardly changed. The change in CO2 emissions in

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Table 10 Relative percentage changes of all the variables for every five years in three regions of China.

Table 12 Estimation results for CO2 emissions in eastern region. Variable

A

T

Year

Eastern region

2000 14.16 8.97 54.05 32.74 2005 103.73 12.21 154.13 57.92 2010 191.15 21.60 315.25 127.90 2000 1.13 2.10 63.70 65.27 2005 69.04 2.53 191.48 91.22 2010 133.41 3.85 249.78 193.75 2000 7.06 2.95 48.40 41.10 2005 101.11 4.21 151.60 51.81 2010 230.34 4.41 361.91 135.83

Central region Western region

CO2

P

Region

S

FDI

URB

22.48 15.18 30.51 19.98 2.52  13.42 8.37  0.33  9.41

 48.30  51.10  58.55  36.09  2.96  1.48  34.77 2.68  10.92

8.39 37.22 56.59 16.60 35.96 54.72  13.55  12.32 2.76

Table 11 Estimation results for CO2 emissions in national case. Variable

(1) FE

(2) N-W nnn

lnP

0.9789 (0.1712) lnA 0.9192nnn (0.0359) lnT  0.4893nnn (0.0563) lnS  0.4710nnn (0.0578) lnFDI  0.0049 (0.0143) lnURB 0.0911nn (0.0408) Constant  3.9675nnn (1.3489) R2 0.9738 Observations 464 nn

(3) FGLS nnn

1.0424 (0.0262) 0.7606nnn (0.0336)  0.5535nnn (0.0440)  0.5837nnn (0.0594)  0.1243nnn (0.0160) 0.3269nnn (0.0563)  3.5423nnn (0.4995) – 464

(4) PCSE nnn

0.9831 (0.0168) 0.6822nnn (0.0193)  0.4238nnn (0.0248)  0.3389nnn (0.0244)  0.0306nnn (0.0058) 0.0939nnn (0.0160)  2.6688nnn (0.3380) – 464

(5) DK nnn

0.9864 (0.0557) 0.6954nnn (0.0510)  0.4468nnn (0.0812)  0.4191nnn (0.0749)  0.0391nn (0.0192) 0.1341nn (0.0588)  2.5864nnn (0.7773) 0.9809 464

0.5400nn (0.2734) lnA 0.9663nnn (0.0857) lnT  0.6018nnn (0.1232) lnS  0.4694nnn (0.1357) lnFDI 0.0134 (0.0344) lnURB 0.3451nnn (0.1090) Constant  2.0621 (2.0893) R2 0.9834 Observations 176

lnP

nn nnn

1.0424 (0.0246) 0.7606nnn (0.0285)  0.5535nnn (0.0499)  0.5837nnn (0.0390)  0.1243nnn (0.0221) 0.3269nnn (0.0462)  3.5423nnn (0.3765) 0.9304 464

Rejection of the null hypothesis at the 5% significance level. Rejection of the null hypothesis at the 1% significance level.

nnn

the western region was larger than the eastern and central regions in 2010. Emissions changes in the western, eastern and central regions were 230.34%, 191.15% and 133.41%, respectively. FDI in the eastern region steadily decreased, and dropped by 58.55% in 2010. The central and western regions fluctuated. The relative FDI changing rate was negative in most years between 1995 and 2010, likely because the growth rate of FDI was far less than the GDP growth rate during 1995 and 2010. In 2010, changing technology rates in the eastern, central and western regions were 127.90%, 193.75% and 135.83%, respectively. The industrial structure experienced fluctuation in these three regions as well. Urbanization in the three regions showed a continuous upward trend.

5. Empirical results 5.1. Whole analysis National level panel estimation is shown in Table 11. The results of model 5 show that all explanatory variables are significant at the 1% level. FDI enters negatively in the regression, with a 1% increase causing a 0.1243% decline in CO2 emissions. Technology level and industrial structure also had negative correlations with CO2 emissions. A 1% increase in technology level and industrial structure will cut CO2 emissions by 0.5535% and 0.5837%, respectively. Conversely, population, GDP per capita and urbanization are positively related to CO2 emissions. Population,

(6) FE

(7) N-W

(8) FGLS

(9) PCSE

(10) DK

1.0714nnn (0.0461) 0.7177nnn (0.0715)  0.5934nnn (0.0599)  0.5652nnn (0.0777)  0.1161nn (0.0425) 0.4566nnn (0.0885)  4.0179nnn (0.9567) – 176

1.0687nnn (0.0395) 0.9357nnn (0.0365)  0.6365nnn (0.0392)  0.3036nnn (0.0560)  0.00360 (0.0156) 0.0399 (0.0389)  5.6269nnn (0.5909) – 176

1.1256nnn (0.0862) 0.9564nnn (0.0891)  0.6344nnn (0.0920)  0.3618nnn (0.1158)  0.0158 (0.0553) 0.0111 (0.1042)  5.9178nnn (1.3730) 0.9814 176

1.0714nnn (0.0537) 0.7177nnn (0.0728)  0.5934nnn (0.0864)  0.5652nnn (0.0679)  0.1161nn (0.0509) 0.4566nnn (0.1089)  4.0179nnn (1.0643) 0.9612 176

Rejection of the null hypothesis at the 5% significance level. Rejection of the null hypothesis at the 1% significance level.

nnn

Table 13 Estimation results for CO2 emissions in central region. Variable

(11) FE

 0.8453 (0.5280) lnA 0.3197nnn (0.0516) lnT 0.0400 (0.0737) lnS  0.4778nnn (0.0749) lnFDI  0.0251 (0.0278) lnURB 0.7545nnn (0.0993) Constant 14.1081nnn (4.6192) R2 0.9639 Observations 128

lnP

(12) N-W

(13) FGLS

(14) PCSE

(15) DK

0.9422nnn (0.0470) 0.6147nnn (0.0263)  0.3873nnn (0.0370)  0.5160nnn (0.0805)  0.0590n (0.0314) 0.4379nnn (0.0741)  2.1903nn (0.7441) – 128

0.8715nnn (0.0547) 0.5251nnn (0.0492)  0.3616nnn (0.0523)  0.3471nnn (0.0462)  0.0189 (0.0167) 0.4079nnn (0.0747)  1.5307nn (0.7352) – 128

0.8486nnn (0.0774) 0.4382nnn (0.0733)  0.3714nnn (0.0868)  0.3639nnn (0.0845)  0.00540 (0.0373) 0.6598nnn (0.1320)  1.505 (1.1532) 0.9901 128

0.9422nnn (0.0463) 0.6147nnn (0.0208)  0.3873nnn (0.0098)  0.5160nnn (0.0663)  0.0590nnn (0.0102) 0.4379nnn (0.0913)  2.1903nn (0.7668) 0.9193 128

n

Rejection of the null hypothesis at the 10% significance level. Rejection of the null hypothesis at the 5% significance level. nnn Rejection of the null hypothesis at the 1% significance level. nn

GDP per capita, and urbanization coefficients for CO2 emissions are 1.0424, 0.7606 and 0.3269, respectively. 5.2. Regional analysis A distinct feature of FDI inflows to China is its geographic distribution, with the bulk allocated to the eastern region [51]. The geographic distribution of FDI is similar to that of CO2 emissions. The eastern region accounts for the largest number of CO2 emissions. Different FDI levels may lead to varying regional CO2 emissions. We attempt to uncover different impacts of FDI on CO2 emissions among three regions. Tables 12–14 show the results of models 7, 15 and 20. These findings indicate that FDI coefficients in the three regions are all negatively correlated with CO2 emissions and statistically significant at the 5% level or lower. The influence of FDI on CO2 emissions varies by region. A 1% increase in FDI decreases CO2 emissions in the eastern region by 0.1161%. Such an increase decreases CO2 emissions in the central and western regions by 0.0590% and 0.1337%, respectively.

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Table 14 Estimation results for CO2 emissions in western region. Variable

(16) FE

1.1605nnn (0.3046) lnA 1.1081nnn (0.0552) lnT  0.6324nnn (0.0928) lnS  0.1949nn (0.0864) lnFDI  0.0127 (0.0169) lnURB 0.0133 (0.0497) Constant  7.7139nnn (2.5204) R2 0.9768 Observations 160

lnP

(17) N-W

(18) FGLS

(19) PCSE

(20) DK

1.2182nnn (0.0294) 1.1577nnn (0.0396)  1.0364nnn (0.0603)  0.5359nnn (0.1557)  0.1337nnn (0.0226) 0.3032nnn (0.0502)  8.3829nnn (0.9756) – 160

0.9907nnn (0.0302) 0.9919nnn (0.0363)  0.6080nnn (0.0478)  0.2839nnn (0.0510)  0.0485nnn (0.0084) 0.0647n (0.0334)  5.2932nnn (0.5477) – 160

1.0664nnn (0.0496) 1.0484nnn (0.0537)  0.7252nnn (0.0922)  0.3397nnn (0.1083)  0.0528nnn (0.0183) 0.1336n (0.0731)  6.3738nnn (0.9583) 0.983 160

1.2182nnn (0.0159) 1.1577nnn (0.0400)  1.0364nnn (0.0362)  0.5359nnn (0.1343)  0.1337nnn (0.0235) 0.3032nnn (0.0333)  8.3829nnn (0.8134) 0.9290 160

n

Rejection of the null hypothesis at the 10% significance level. Rejection of the null hypothesis at the 5% significance level. nnn Rejection of the null hypothesis at the 1% significance level. nn

The effects of other estimated variables on CO2 emissions are also heterogeneous across different regions. The elasticity of CO2 emissions to population change in the western region is 1.2182, greater than the eastern (1.0714) and central regions (0.9422). Similarly, the elasticity of CO2 emissions to changes in GDP per capita in the western region is 1.1577, greater than that in the eastern (0.7177) and central regions (0.6147). The influence of urbanization on emission is positive and decreases from eastern to western regions. A 1% increase in urbanization will increase CO2 emissions in the eastern, central and western regions by 0.4566%, 0.4379% and 0.3032%, respectively. Conversely, the impact of technology level and industrial structure is negative and statistically significant at the 1% level. The coefficients of technology level in the eastern, central and western regions are  0.5934,  0.3873 and  1.0364, respectively. Meanwhile, industrial structure coefficients on CO2 emissions are  0.5652,  0.5160 and  0.5359 in the eastern, central and western regions, respectively.

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[13]. Accordingly, to compete with FDI enterprise, Chinese domestic enterprise need to increase R&D investments, and this helps improve energy-related efficiencies and lower CO2 emissions in China. Finally, FDI inflows are influenced by pollution regulation. Dean et al. [55] argued that FDI inflows in pollution intensive industries are prevented in Chinese provinces with more stringent environmental standards. Since the Chinese government has set more stringent regulations, FDI inflows in high-skill-intensive goods are increased and contribute to reduced emissions. This study also found that the impact of FDI on CO2 emissions decreases from the western to eastern and central regions. In the western region, FDI has a greater impact on emissions-reduction than the eastern region because FDI in the former is spatially more concentrated and because the technology spillover effect is stronger. Cheung and Lin [56] reported that location proximity is important to technology spillover and that a higher degree of spatial concentration of FDI tends to yield a stronger spillover effect. In 2010, approximately 82.4% of western region FDI went to Sichuan, Inner Mongolia and Shaanxi. Eastern region FDI was more evenly distributed across provinces. Consequently, technological progress accompanied by FDI helps to improve energy efficiency and reduce emissions in the western region to a greater extent than in the eastern region. In the central region, the influence of FDI on emissions is slight. This difference can be explained by the industrial structure and technology of this region. The value of heavy industrial output accounts for 71.0% of the total value of the region's industrial output. The region specializes in energyguzzling industries and is the largest producer of coal among the three. Less-developed environmentally sound technology and lower energy-related efficiency in the central region make reducing CO2 emissions more difficult. An optimized industrial structure was found to have a negative relationship with CO2 emissions. This finding is in accordance with Wang et al. [57], who concluded that the tertiary industry plays an important role in controlling CO2 emissions. In addition, FDI inflows contribute to optimizing industrial structures. Zhao and Niu [58] suggested that FDI inflows help promote China's industrial structure adjustment. Chinese FDI inflow distribution structures have been optimized in recent years. For example, the proportion of China's FDI in the tertiary industry accounted for 19.9% of total FDI in 1997, and rose to 36.2% in 2010. Therefore, FDI inflows help to optimize industrial structure and cut CO2 emissions because the tertiary industry produces fewer emissions than the secondary.

6. Discussion This study found that FDI inflows decrease CO2 emissions in China. This finding is consistent with that of List and Co [6] and Zheng et al. [8]. The negative nexus between FDI and CO2 emissions seems to support the pollution-halo hypothesis. This finding suggests that FDI contributes to the transference of environmentally sound technologies [52]. These technologies help improve energy efficiency and reduce emissions. Tiwari et al. [53] reported that new ideas, advanced technology and management can transfer to domestic enterprises through FDI. Technology transfer can help domestic firms adopt new technologies and innovate, improving energy efficiency and promoting low carbon economic development [22]. Even if the technological spillovers do not occur and FDI firms specialize in pollution-intensive industries, China's CO2 emissions may not increase due to foreign enterprises using environmentally friendly and efficient energy [54]. FDI enterprises may also give domestic firms an incentive to increase R&D investment and improve energy-related efficiency. The presence of FDI firms can intensify market competition and prompt domestic enterprises to increase their emphasis on research and development activities and to improve the efficiency of production, which will strengthen the technical efficiency of the whole host economy

7. Conclusions and policy implications This paper investigated the impact of FDI on CO2 emissions in China with a consideration of regional differences. Panel data of 29 provinces from 1995 to 2010 and the STIRPAT model were used. The results suggest that FDI inflows contribute to reductions in China’s CO2 emissions. The impact of FDI on CO2 emissions varies by region. The influence of FDI on CO2 emissions in the western region is greater than in the eastern and central regions. This findings support the pollution-halo hypothesis. These findings warrant attention from Chinese policymakers; for example, China should encourage FDI inflows. As the largest carbon emitter in the world, China is facing increasing international and domestic pressure to cut greenhouse gas emissions. Our results indicate that FDI inflows can reduce CO2 emissions. Policies should be implemented to encourage FDI inflows, including domestic market access, and proper preferential taxation. Policy measures attracting FDI should also differ by region. The eastern and western regions should seek to attract FDI to further reduce emissions. The central region should seek to attract FDI in technology-intensive industries and energy-saving technologies due to its energy-guzzling industrial

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structure. Finally, FDI inflow distribution structures should be further adjusted. Our results show that an optimized industrial structure has a negative relationship with CO2 emissions. Therefore, FDI inflows should be directed toward the tertiary industry, especially capitalintensive and technology-intensive industries. FDI firms in China should be encouraged to use and exchange environmentally sound technologies. Domestic firms should seek to absorb advanced foreign technology. These measures will decrease energy consumption, reduce emissions and build a low carbon economy. Some limitations in our study warrant discussion. This study only provides a preliminary explanation of the effects of FDI on CO2 emissions. Countries of origin and FDI structure are not considered in our research. The higher technology of FDI from developed countries and ratio of FDI flowing to different industries have differing influences on emissions. The mechanisms by which FDI reduces emissions are unknown. Further research would help researchers better understand the relationship between FDI and CO2 emissions in China.

Acknowledgments We would like to thank two anonymous referees for their detailed and constructive comments. We are also grateful to the Editor-in-Chief for his encouragement and high efficiency. This research is supported by the Fundamental Research Funds for the Central Universities (No. 20720140020) and by the Social Science Funds in Fujian Province (No. FJ2015B222) and also supported by the Program for New Century Excellent Talents in University of Ministry of Education of China (No. NCET-12-0327) and for Young and Middle-aged Teachers' Educational Science Research in Fujian Province (No. JAS150064).

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