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The impact of foreign direct investment on SO2 emissions in the Beijing-Tianjin-Hebei region: A spatial econometric analysis
Journal of Cleaner Production 166 (2017) 189e196
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The impact of foreign direct investment on SO2 emissions in the Beijing-Tianjin-Hebei region: A spatial econometric analysis Lin Zhu a, Qingmei Gan a, Yan Liu a, Zhijun Yan a, b, c, * a
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China c Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 12 January 2017 Received in revised form 30 June 2017 Accepted 5 August 2017 Available online 5 August 2017
This paper examines the spatial impacts of foreign direct investment (FDI) on sulfur dioxide (SO2) emissions in the Beijing-Tianjin-Hebei region located in northern China. To avoid making coefficient estimation errors, we use spatial panel data on 10 cities in the Beijing-Tianjin-Hebei region for 2000 to 2013. We first analyze the effects of FDI on air pollution levels in the Beijing-Tianjin-Hebei region from ordinary least squares (OLS). Then, the existence of a spatial correlation is examined via a Lagrange Multiplier (LM) test, and the Spatial Durbin Model (SDM) is adopted to investigate the influence of FDI on SO2 emissions in this region. The results show that FDI has a significantly positive impact on SO2 emissions, suggesting that an increase in FDI inflows would increase air pollution levels in the BeijingTianjin-Hebei region. Furthermore, the air quality of local cities is also influenced by FDI inflows in surrounding areas. © 2017 Published by Elsevier Ltd.
Keywords: Foreign direct investment Sulfur dioxide emissions Spatial econometric analysis Beijing-Tianjin-Hebei region
1. Introduction Since joining the World Trade Organization (WTO), China has become one of the most attractive destinations for foreign direct investment (FDI) in the world. According to the 2014 China statistical yearbook, the volume of FDI inflows into China has reached $119.721 billion. This is much higher than the average FDI of the United States, which achieved an FDI of just $86 million in 2014. Many studies show that FDI plays a critical role in accelerating Chinese economic development not only in terms of promoting technological optimization but also in transforming industrialization processes and business environments in China (Chen et al., 2016). FDI has remarkably promoted China's economic growth over the past few years while influencing local environments significantly at the same time. One of the most intense debates occurring today concerns whether FDI inflows are turning developing countries into “pollution haven” (Wagner and Timmins, 2010). China's economic growth and large FDI inflows seem to be accompanied by serious pollution problems. China's development
* Corresponding author. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China. E-mail address: [email protected] (Z. Yan). http://dx.doi.org/10.1016/j.jclepro.2017.08.032 0959-6526/© 2017 Published by Elsevier Ltd.
model involves “growth first, cleaning up later,” which has resulted in serious pollution problems (Shen et al., 2017; Zhang et al., 2017). The country's significant achievements in economic development have rendered China the largest single contributor to global sulfur dioxide (SO2) emissions (Liu et al., 2017). High SO2 emissions levels have led to the formation of acid rain and have incurred considerable damage to ecosystems while hindering socioeconomic development (Zhang and Chang, 2012). Meanwhile, SO2 also poses threats to human health, leading to the development of chronic diseases (Chiang et al., 2016). The Beijing-Tianjin-Hebei region includes the provinces of Beijing, Tianjin, and Hebei in northeastern China. Due to its geographical advantages and resources, the region attracts considerable levels of foreign investment that have significantly accelerated its industrialization. At the same time, the Beijing-Tianjin-Hebei region is the most heavily polluted area in China. Emissions of SO2 generated in the Beijing-Tianjin-Hebei region still make up a large proportion of emissions generated in China. However, as one of the most important economic and political regions in China, spatial effects of environment pollution in the Beijing-Tianjin-Hebei region are seldom investigated. Using data on SO2 emissions and FDI in the Beijing-TianjinHebei region, this study examines the relationship between FDI and SO2 emissions. Specifically, we adopt spatial econometric models to explore the spatial effects of FDI on air pollution in the
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capital region. China's air pollution patterns have clear regional spatial characteristics (Xu et al., 2017; Yu et al., 2015), rendering spatial econometric models appropriate in explaining the inherent relationship between SO2 emissions and FDI. We use the Spatial Durbin Model (SDM) to validate the impacts of FDI on SO2 emissions, and our results offer new insight into the control of SO2 emissions in the Beijing-Tianjin-Hebei region. This paper makes three main contributions. First, to the best of our knowledge, this is the first study to evaluate the influence of FDI on SO2 emissions by applying spatial econometric methods to the Beijing-Tianjin-Hebei region. In using the spatial durbin model, estimation biases resulting from ignoring spatial correlations of air pollution can be avoided. Second, most prior studies have analyzed the effects of FDI on air pollution levels at the country level while studies on the regional level have not been conducted. In this paper, we focus on the Beijing-Tianjin-Hebei region, one of the three main industrial regions of China. It contributed to 10.4% of China's GDP in 2014 and is treated as the main development region in China's 13th Five-Year Plan. At the same time, in recent years due to rapid economic development and population increases in this region, air pollution has progressively become a more serious problem. Tianjin, Hebei and Beijing in particular are now facing much greater pressures to control air pollution levels (Ikram et al., 2015). Finally, we explore the effects of FDI in the second industry on SO2 emissions. Urban air pollution levels are strongly influenced by the second industry (Hao and Liu, 2016). As a major economic hub, the Beijing-Tianjin-Hebei region has attracted considerable levels of FDI with the proportion of FDI flowing into the second industry accounting for more than fifty percent. Therefore, studying the impacts of FDI in the second industry on air pollution levels offers new insights necessary to improve investment structures. The rest of this paper is structured as follows. Section 2 reviews related literature in this field. Section 3 introduces the data sources and methodologies used. Section 4 describes the research results. Section 5 presents a discussion. Finally, we summarize the study results and make policy suggestions in Section 6. 2. Literature review With rapid growth in FDI, many studies have focused on the relationship between FDI and air pollution levels to determine whether increases in FDI affect local air quality levels. However, these studies have not yet obtained consistent results (Kim and Baek, 2011; Kirkulak et al., 2011; Wang and Chen, 2014). FDI accelerates industrial development in local areas and typically increases levels of energy consumption. Many research findings show that air quality is compromised by increases in FDI and energy consumption, which are found in developing and developed countries (Abdouli and Hammami, 2017; Hoffmann et al., 2005; Shofwan and Fong, 2012; Zhang, 2011). Results show that FDI has a negative effect on regional air quality levels and leads to high levels of air pollution. Waldkirch and Gopinath (2008) examined the existence of pollution haven effects of FDI in Mexico. Their results reveal a positive and significant correlation between FDI and SO2 emissions. Similarly, Cole et al. (2009) investigated the relationship between economic growth, FDI, and the environment in China. Using data on 112 cities for 2001e2004, they found evidence of the existence of a pollution haven effect in China. In addition, Wang and Chen (2014) also showed that investments from Organization for Economic Co-operation and Development (OECD) countries have led to significant increases in SO2 emissions in 287 Chinese cities, proving the pollution haven hypothesis. Using panel data on five countries of the Association of Southeast Asian Nations for 1981e2010, Baek (2016) estimated the impacts of FDI inflows on energy consumption and environmental quality. Their results show
that FDI deteriorates the environment, supporting the pollution haven hypothesis. However, other studies have made conflicting conclusions that more FDI improves air quality levels. Letchumanan and Kodama (2000) found that FDI improves environmental welfare by spurring the use of cleaner technologies and environmentally friendly products and manufacturing processes. Dean et al. (2005) claimed that FDI benefits the environment of the host country. When FDI from developed countries results in the use of cleaner technologies, it can improve environmental outcomes in developing countries. Zeng and Eastin (2007) investigated the effects of trade openness and FDI on air pollution levels for 1996e2004 in China. Their results show that increased trade openness and FDI are positively correlated with environmental protection in China and with reductions in SO2 emissions. Zheng et al. (2010) used a cross-city dataset to test relationships between ambient air pollution, house prices, wages and FDI. Their results show that higher per-capita FDI inflows result in lower SO2 concentrations. Kirkulak et al. (2011) analyzed the impacts of FDI on China's air quality levels based on panel data on 286 cities for 2001 to 2007. Their results show that the presence of FDI reduces SO2 emissions, as characteristics of FDI are normally driven by the use of advanced technologies. Some studies have also found that FDI had little impact on air quality levels. He (2006) established simultaneous equations to explore relationships between FDI, income and SO2 emissions in China. The results show that while FDI increases SO2 emissions levels, related impacts are marginal. Similar results were found by Kim and Baek (2011), who estimated the impacts of FDI on air pollution levels in 40 developing countries using an autoregressive distributed lag (ARDL) model. They found that FDI has limited effects on environmental quality in both developed and developing countries over the long term. Although previous studies have extensively examined the effects of FDI on air pollution, corresponding results vary considerable. FDI flows into different industries and investments in different industries can have different environmental effects. However, industry characteristics have been largely ignored in previous studies (Kirkulak et al., 2011; Zheng et al., 2010). As 65.7% of FDI flows into the second industry in China, the relationship between FDI in the second industry and air pollution is worth exploring. The second industry is a major choice of short-term FDI, and FDI in the second industry has stronger impacts on the environment than FDI in the first and third industries (Li and Luo, 2012). Recent studies also suggest that it is better to attract FDI inflows in the services industry than in the manufacturing industry for purposes of environmental protection (Baek, 2016). However, quantitative analyses of impacts of FDI inflows into the second industry on air pollution are currently missing. As a new area of economic development, the Beijing-TianjinHebei region has received more attention in recent years (Xu et al., 2017). The Chinese government is trying to interactively coordinate the economic, social and scientific development of the area. Due to its rapid economic development, the region has been reported to be one of the most heavily polluted areas in China (Xu et al., 2017; Zhao et al., 2013). Due to inherently strong connections between cities in this region, air pollution in surrounding areas may contribute to the city's air quality, and thus regional air pollution control strategies have been highly cost-efficient (Wu et al., 2015). Over the past decade, FDI has played a key role in economic development in this area. In 2013, FDI in the Beijing-Tianjin-Hebei region accounted for 28.6% of that of the entire country (CNBS, 2014). As a valuable promoter of local economic development, FDI has a significant spatial effect on regional economic development that also influences environment quality (Ng and Tuan, 2006).
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In summary, studies have extensively examined the relationship between FDI and SO2 emissions. However, spatial effects of FDI on SO2 emissions have been explored less often. At the same time, the Beijing-Tianjin-Hebei region is one of most heavily industrialized and polluted areas in China. Spatial relationship analyses of FDI and SO2 emissions for this region can help policy makers control regional air pollution levels. Finally, studies have largely disregarded variations in FDI across industries, which may have biased research results. Therefore, we use FDI in the second industry to evaluate the impacts of FDI on SO2 emissions. 3. Data and method 3.1. Study areas According to China's National Development and Reform Commission, the Beijing-Tianjin-Hebei region includes 10 cities, namely, Beijing, Tianjin, Shijiazhuang, Tangshan, Qinhuangdao, Langfang, Baoding, Cangzhou, Chengde and Zhangjiakou (Fig. 1). Data show that in 2014, this area accounted for 2.2% of China's land and 7.6% of China's population and yielded 10.4% of the national GDP and consumed more than 10% of the country's coal resources. According to 2013 air quality status reports on 74 cities in China (Tang et al., 2016), 7 of China's 10 cities with the worst air quality are located in Hebei, thus rendering the Beijing-Tianjin-Hebei region one of the most heavily polluted regions in China. 3.2. Data source High levels of coal consumption demand lead to serious levels of air pollution (Yan et al., 2016; Yu et al., 2015). In 2015, coal consumption levels in the Beijing-Tianjin-Hebei region were 30 times
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as high as average global consumption levels. Of air pollutants generated from coal consumption, SO2 is the main pollutant generated in the Beijing-Tianjin-Hebei region. Moreover, industrial SO2 emissions levels are consistently recorded in the China City Statistics Yearbook. Therefore, we use total annual industrial SO2 emissions for the ten cities from the China City Statistical Yearbook as pollution levels in our analysis. As the second industry is the main generator of air pollution, we focus on exploring the spatial relationship between annual FDI flowing into the second industry and air pollution (Chandran and Tang, 2013). Annual FDI in the second industry was drawn from the China City Statistical Yearbook (CNBS, 2014). According to previous studies, the following other factors are used as explanatory variables: R&D investment (RD), gross domestic product (GDP), population, and energy consumption (EC). R&D investment refers to investment in technology and science. As previous studies show that technologies have a positive impact on environment protection (Kirkulak et al., 2011), we propose that R&D investment may have a positive impact on air pollution reduction outcomes. GDP is a proxy for economic growth (Lee, 2013), and this variable captures the impacts of economic activities on air pollution. A population is defined as the total number of permanent residents in a city, and Liddle (2015) found that population size is the largest driver of air pollutant emissions. Energy consumption is a key influencing factor of air pollution levels, and Al-Mulali and Ozturk (2015) found that energy consumption and air pollution levels exhibit a positive long-run bi-directional relationship. These factors are thus also used as explanatory variables and are included in the models used, and data were obtained from China Statistical Yearbook for 2000e2013. More detailed information on the key variables used is shown in Table 1. 3.3. Methods We first explore the association between FDI and SO2 emissions by applying a traditional method without taking spatial correlations between adjacent cities into consideration. By introducing FDI, GDP, R&D investment, population size, and energy consumption into the model, we establish the OLS model as follows:
lnðSO2 Þit ¼ b0 þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit þ b4 lnPOPit þb5 lnECit þ εit (1)
Fig. 1. The Beijing-Tianjin-Hebei region.
where i represents a city and where t denotes a year. (SO2)it is the SO2 emissions of a city i for year t. FDIit represents FDI made in the second industry of city i in year t. GDPit, POPit, RDit and ECit represent the factors given in Table 1, influencing SO2 emissions in a given city at time t. εit is the error term. Before building the spatial econometric model of FDI and SO2 emissions, we first test the existence of a spatial effect. We apply the improved LM test method developed by Elhorst for spatial panel data (Elhorst, 2003). When using the LM test method, when the results of the LM-err and LM-lag are not statistically significant, the traditional panel model should be used. If any of them are significant, the spatial econometric model should be used to describe spatial effects. We then re-examine the relationship between FDI and SO2 by considering spatial correlations. Following Elhorst (2003), three econometric models can be used to examine spatial correlations. The first model is referred to as the Spatial Lag Model (SLM). The SLM hypothesizes that the value of the dependent variable for area i is affected by neighbors' dependent variables. This means that SO2 emissions in area i are affected by the SO2 emissions of neighboring areas.
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Table 1 Definition of the variables. Variables
Definition
Unit of measurement
Sulfur dioxide emissions (SO2) Foreign direct investment (FDI) R&D investment (RD) Gross domestic product (GDP) Population size (POP) Energy consumption (EC)
Total industry sulfur dioxide emissions of individual cities Total FDI in the second industry Technology and science level Per city's gross domestic product Total number of people at the end of a year Per city's energy consumption
10,000 10,000 10,000 10,000 10,000 10,000
4. Results
The Spatial Lag Model (SLM) is defined as:
lnðSO2 Þit ¼ r
n X
tons RMB RMB RMB person tons coal
4.1. Descriptive statistics
Wij lnðSO2 Þjt þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit
j¼1
þb4 lnPOPit þ b5 ECit þ mi þ li þ εit (2) where i and j represent cities i and j, respectively (i sj) and where t represents the year. r is the corresponding spatial parameter reflecting the spatial dependence of the sample observations. It thus evaluates the impacts of adjacent area SO2 emissions on local SO2 emissions. Wij is an element in the (N N) spatial weight matrix, which is the geographic distance matrix used in our study. mi denotes the time fixed effect of spatial units. li represents spatially fixed specific effects. εit is the random disturbance term. The second model is the spatial error model (SEM), which assumes that spatial correlation stems from the error term of dependent variables for adjacent areas. The SEM is expressed as:
lnðSO2 Þit ¼ b0 þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit þ b4 lnPOPit þb5 lnECit þ mi þ li þ 4it (3)
Table 2 presents detailed information on all variables for the 10 cities for our study period (2000e2013). Maximum SO2 emissions levels reach 46.4599 while the minimum value is 2.39. FDI in cities of the Beijing-Tianjin-Hebei region ranges from 459 to 852,418. 4.2. The impacts of FDI on SO2 emissions We first examine the impacts of FDI on SO2 emissions by running the Ordinary Least Squares (OLS) estimation. Table 3 presents estimation results of the OLS based on different specifications. The impacts of FDI, RD, EC and GDP are statistically significant. When we do not consider spatial effects, FDI has negative impacts on SO2 emissions. This observation coincides with the results of Kirkulak et al. (2011), who found that FDI reduces air pollution levels. In addition, the coefficient of GDP is also negative, indicating that the impacts of GDP on environment are positive. EC and the RD have a significantly positive impact on SO2 emissions. Population also has a positive impact on SO2 emissions though it is not significant. The coefficients shown in Table 3 imply that a 1% increase in FDI leads to a 0.84% decrease in SO2 emissions. 4.3. Spatial autocorrelation test
4it ¼ d
n X
Wij 4jt þ εit
j¼1
where 4it is the spatial autocorrelation error term. d is the spatial autoregressive coefficient, which reflects the effects of residuals of adjacent areas on residuals of the local area, and εit is an i.i.d (independent and identically distributed) residual. The third model is the Spatial Durbin model (SDM). The SDM assumes that the dependent variable of region i is spatially dependent on the independent and dependent variables of other adjacent regions, which are specified as:
lnðSO2 Þit ¼ r
n X
Wij lnðSO2 Þjt þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit
j¼1
þb4 lnPOPit þ b5 ECit þ q1
q3
n X j¼1
Wij lnGDPjt þ q4
n X j¼1
n X
Wij lnFDIjt þ q2
j¼1
Wij lnPOPjt þ q5
n X
n X
Wij lnRDjt þ
The aim of the Hausman test is to determine whether a model with fixed or random effects is superior. The Hausman test results show that the model with fixed effects is significant at the 1% level (Chi-sq. statistic of 85.774 with 6 degrees of freedom, p < 0.01), suggesting that the model with fixed effects is more appropriate. To identify potential spatial effects, we test spatial correlations for the observations and compare SLM, SEM and SDM values through an LM test. As spatial dependence exists within a certain distance, we use a geographic distance matrix to measure spatial adjacency in the Beijing-Tianjin-Hebei region. Geographic distance matrices are widely used in the spatial effect analysis literature (Hao et al., 2016; Keller, 2001; Wang et al., 2013). Our LM test results are presented in Table 4. According to the results, LM spatial lag test results based on the geographic distance matrix are statistically significant (at the 5% level) in terms of spatial fixed effects and spatial time fixed effects. The coefficient of
j¼1
Wij lnECjt þ mi þ li þεit
j¼1
(4) where q is the spatial autocorrelation coefficient of independent variables. The spatial econometric models are estimated based on maximum likelihood values. All of the analyses are conducted using R 3.2.5 software.
Table 2 Descriptive statistics. Variable
Unit
SO2 FDI RD GDP POP EC
10,000 10,000 10,000 10,000 10,000 10,000
tons RMB RMB RMB person tons coal
Mean
Std. Dev
Min
Max
11.2574 116315.5 52527.31 2540.99 748.375 2565.822
8.6733 207101.7 130861.2 3552.262 428.778 3209.867
2.39 459 638 158.629 136.6 155.0309
46.4599 852418 681346 19500.6 2144.8 24995.86
L. Zhu et al. / Journal of Cleaner Production 166 (2017) 189e196 Table 3 The OLS estimation results of FDI impact on SO2 emissions. ln (SO2)
lnFDIit
model (1)
model (2)
model (3)
model (4)
model (5)
3.27* (-1.96)
1.64*** (-4.825) 1.63*** (7.464)
1.34** (-2.645) 5.92* (1.781) 1.64*** (7.478)
4.09* (-1.612) 7.14* (1.75) 1.85*** (7.736) 1.148 (-0.106)
0.84* (-2.201) 1.69* (1.944) 8.15*** (10.22) 7.96** (-2.358) 1.168 (1.46) 144 0.3179
lnRDit lnECit lnGDPit lnPOPit Obs R2
144 0.2557
144 0.274
144 0.2769
144 0.2978
Note: In parentheses the t-values are given. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.
the spatial fixed effect of the LM spatial lag is 29.69, and it is significant at the 1% level. The robust spatial lag LM test results based on time fixed effects, spatial fixed effects and spatial-time fixed effects are significant. In addition, the LM spatial error test results with spatial fixed effects are significant. The robust LM spatial error test results based on time fixed effects, spatial fixed effects and spatial-time fixed effects are statistically significant at the 5% level. These results show that the spatial models are more appropriate than non-spatial interaction effects of the traditional panel models (Kang et al., 2016). This proves the existence of spatiality and suggests that the spatial models are more appropriate than the traditional panel data in terms of analyzing the impacts of FDI on SO2 emissions. The results shown in Table 4 illustrate that the p-values of most of the tests are lower than 0.05, suggesting that the null hypotheses of no spatially lagged dependent variable and no spatially autocorrelated error term can be definitively rejected. Moreover, we estimate the Spatial Durbin Model and then perform a Wald test
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and Likelihood Ratio (LR) test to select a suitable model. As Table 5 shows, the Wald and LR test results are significant at the 1% level, suggesting that the SDM is more appropriate than the SLM and SEM (Hao et al., 2016). We thus use an SDM model that includes the spatially lagged dependent variable and spatially autocorrelated error term to address issues of spatial dependence. 4.4. Spatial econometric regression results Table 6 presents the maximum likelihood (ML) estimation of the SDM. The estimation results of Log-likelihood can be used to select the most appropriate model of the spatial fixed effects, time fixed effects and spatial-time fixed effects models. As is shown in Table 6, the SDM with spatial fixed effects includes the maximum Loglikelihood value. Therefore, we use the estimation results of the SDM with spatial fixed effects as an interpretative object for interpreting factors affecting SO2 emissions while using the others for reference purposes. It is noteworthy that the spatial correlation parameter r of the SDM estimation is positive and significant at a 10% level. This positive estimated coefficient indicates that SO2 emissions in adjacent areas have a positive effect on local SO2 emissions (Wu and Tian, 2012). As SO2 emissions in this study are estimated based on industrial emissions, this implies that SO2 emissions of cities of the Beijing-Tianjin-Hebei region have spatial spillover effects. Our final estimate of 0.118 suggests that a 1% increase in the SO2 emissions of adjacent areas should result in a 0.118% increase in SO2 emissions in local cities. This reveals an apparent spatial spillover effect of SO2 emissions. Through spatial spillovers, local city air pollution levels can be affected by the air pollution levels of adjacent cities (Cheng, 2016). Our results reflect those of He (2006), who estimated coefficients of spatially lagged independent variables as significant at the 1% level with the exception of those for the coefficient of population. The results also reveal spatial spillover effects between FDI and SO2 emissions. As is shown in Table 6, FDI has a positive effect on SO2 emissions
Table 4 The LM test for choosing the spatial lag model or the spatial error model. Variables
Panel data OLS
Time fixed effects
Spatial fixed effects
Spatial-time fixed effects
b0
9.205*** (4.98) 0.000009** (2.64) 0.000026** (2.37) 0.000157* (1.87) 0.002006** (2.79) 0.01805*** (4.79) 56.2737 0.3601 0.3340 2.0696 525.2638 1.0458 (p ¼ 0.306) 3.6615* (p ¼ 0.056) 4.1852** (p ¼ 0.041) 6.8009** (p ¼ 0.009) Statistic 85.774
e
e
e
0.00003* (-1.78) 0.00045*** (-4.2) 0.000198 (-0.25) 0.004878** (2.08) 0.01022*** (4.07) 37.7756 0.5085 0.4919 2.4350 495.0946 0.4500 (p ¼ 0.502) 3.9289** (p ¼ 0.047) 0.6831 (p ¼ 0.409) 4.1621** (p ¼ 0.041) DOF 6
0.000005** (2.27) 0.00031* (-1.61) 0.001024* (1.91) 0.00762** (-2.13) 0.00082* (1.70) 19.8923 0.1901 0.1627 1.5711 445.7122 29.6920*** (p ¼ 0.000) 86.4289*** (p ¼ 0.000) 4.7735** (p ¼ 0.029) 61.5104*** (p ¼ 0.000) P-value <0.001
0.0001* (-1.83) 0.026* (-1.92) 0.00103* (-1.9) 0.00108 (-1.35) 0.000128* (-1.73) 12.8410 0.0874 0.0566 2.4363 412.0102 8.6191** (p ¼ 0.003) 43.2706*** (p ¼ 0.000) 0.6411 (p ¼ 0.423) 35.2926*** (p ¼ 0.000)
lnFDIit lnRDit lnGDPit lnPOPit lnECit
a2
R2 Adjusted R2 D-W Log likelihood LM spatial lag Robust LM spatial Lag LM spatial error Robust LM spatial error Hausman test
Notes: Numbers in the parentheses represent t-values. ***, ** and * indicate significance at the 1%, 5% and 10% level.
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Table 5 Diagnostic tests of spatial specification. Determinants
Spatial fixed effects
Time fixed effects
Spatial-time fixed effects
Wald test spatial lag LR test spatial lag Wald test spatial error LR test spatial error
124.71*** 180.80*** 112.65*** 101.54***
83.90*** 139.86*** 76.95*** 97.76***
102.34*** 113.09*** 102.89*** 113.16***
Note: Both tests follow a chi-squared distribution with K degrees of freedom. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.
Table 6 Estimation results of spatial Durbin model. Determinants
Spatial fixed effects
Time fixed effects
Spatial-time fixed effects
r
0.118* (1.932) 0.232*** (2.850) 0.0510* (1.695) 0.731*** (7.448) 0.214 (0.747) 0.715*** (6.509) 0.128* (1.951) 0.115* (1.957) 0.124* (1.542) 0.223 (-0.864) 0.083*** (2.417) 0.228 0.601 51.526
0.108** (2.127) 0.230** (2.087) 0.596** (2.077) 0.733** (2.105) 0.245 (0.129) 0.711** (2.120) 1.22** (2.124) 0.115*** (3.127) 1.41** (2.243) 0.217 (0.275) 0.106** (2.220) 0.251 0.607 65.982
0.127*** (3.127) 0.851** (2.813) 0.733** (2.769) 0.098* (1.923) 0.122** (2.123) 0.110*** (6.120) 0.117*** (3.123) 0.20* (1.986) 0.229** (2.224) 0.258** (2.275) 0.199** (2.219) 0.241 0.706 58.970
lnFDIit lnRDit lnGDPit lnPOPit lnECit W*lnFDIit W*lnRDit W*lnGDPit W*lnPOPit W*lnECit
s2
R2 Log-likelihood
Notes: Numbers in the parentheses represent t-values. ***, ** and * indicate significance at the 1%,5% and 10% level.
in the SDM with spatial fixed effects, and the coefficient implies that an additional 1% increase in FDI in the second industry should lead to an approximate 0.232% increase in SO2 emissions. This indicates that FDI inflows should negatively affect the environment. The spatial spillover effects of FDI on adjacent areas are significantly positive: a 1% increase in FDI should lead to a 0.128% increase in SO2 emissions in adjacent areas. It is clear that regional SO2 emissions have spatial effects and that FDI increases air pollution levels. In addition, the estimated coefficient of R&D is significantly positive, indicating that R&D is another primary cause of SO2 emissions in the Beijing-Tianjin-Hebei region. An increase in R&D of 1% is associated with a 0.051% increase in SO2 emissions. Meanwhile, spatial spillover effects of adjacent areas on R&D investment are positive, and a 1% increase in R&D investment should lead to a 0.115% increase in SO2 emissions in adjacent areas. The impacts of GDP and energy consumption on SO2 emissions are significantly positive, indicating that an increase in GDP and energy consumption should also lead to an aggravation of air pollution levels. The GDP impact coefficient is 0.731, meaning that when a city increases its own GDP by 1%, it should increase SO2 emissions by approximately 0.731%. In addition, a 1% increase in energy consumption can lead to an average 0.715% increase in SO2 emissions. These results show that the rapid economic growth of the Beijing-Tianjin-Hebei region has promoted an increase in energy consumption and SO2 emissions levels over our study period. The economic growth of the Beijing-Tianjin-Hebei region is still heavily shaped by energy-intensive industries, which place considerable pressure on energy demand and pollution control outcomes (Hu
et al., 2014). Spatial spillover effects on adjacent areas on GDP growth are positive. A 1% increase in the GDP of adjacent areas results in a 0.124% increase in SO2 emissions in local areas. Furthermore, spatial spillover effects on energy consumption are significantly positive. Rather, a 1% increase in energy consumption leads to a 0.083% increase in SO2 emissions. Surprisingly, population has a negative effect on SO2 emissions but it is not significant, and spatial spillover effects of adjacent areas on population growth are positive but also not significant. 5. Discussion According to the SDM estimation results, we find significant spatial correlations for industrial SO2 emissions in the BeijingTianjin-Hebei region. Furthermore, FDI inflows into the second industry have a positive effect on SO2 emissions, suggesting that higher levels of FDI result in a deterioration of air quality. Our results show that local SO2 emissions will increase by approximately 0.118% when SO2 emissions levels of adjacent areas increase by 1%. The positive coefficient of r implies that SO2 emissions of the Beijing-Tianjin-Hebei region may exhibit a spatial spillover effect. That is, an increase in SO2 emissions in one city should bring about a significant effect on the SO2 emissions of other cities in the Beijing-Tianjin-Hebei region. This significant positive spatial dependence may be attributed to several causes. Energy consumption in the Beijing-Tianjin-Hebei region is still dominated by coal use, the main source of SO2 emissions. Furthermore, an increase in coal consumption in one city is dependent on
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consumption patterns in other cities. A higher level of energy consumption demand in one city is likely to promote energy consumption in neighboring cities (Hao et al., 2014). Therefore, the SO2 emissions of different cities are closely related due to similar coal consumption levels between cities. Moreover, intensifying economic relationships within the Beijing-Tianjin-Hebei region result in strong spillover effects of regional air pollutants emissions (Tang et al., 2015). Product import and export activities among different cities are frequent and are illustrated as a daily trade model for the Beijing-Tianjin-Hebei region. Such multilateral trade among different cities generates strong spatial lag correlations among regional SO2 emissions. At the same time, levels of economic development in different cities of the Beijing-Tianjin-Hebei region are closely correlated. For example, rapid growth in Beijing's economy normally spurs rapid development in surrounding cities, implying that these cities exhibit similar levels of air pollutant emissions. We find a positive relationship between FDI inflows and SO2 emissions. This finding is consistent with those of Baek Jungho (2016) and is in line with those of Ren et al. (2014) who found that high FDI inflows further aggravate air pollution levels in China. In the SDM with spatial fixed effects, coefficients of FDI are significant at the 10% level, suggesting that air pollution levels increase with an increase in foreign investment. As one major feature of FDI in the Beijing-Tianjin-Hebei region, most FDI is made in the second industry, which in turn has serious impacts on air pollution levels. Moreover, it is important to note that economic development in the Beijing-Tianjin-Hebei region is dominated by heavy industry and the majority of FDI has flowed into capital-intensive enterprises. The positive influence of FDI on SO2 emissions found may imply that local environmental regulations do not significantly affect capital-intensive enterprises. Lin (2008) argued that foreign-owned firms are subject to stricter standards and screening protocols. If this is the case, local governments should pay more attention to the implementation of environmental regulations and should ensure that enterprises with FDI meet strict environmental standards. In addition, R&D investment has a negative rather than positive effect on air quality. This indicates that R&D investment has failed to spur economic growth and air quality improvements at the same time. This contradicts the results of previous studies. Albornoz et al. (2014) and Yu et al. (2014) suggested that advanced and high levels of technology can reduce levels of environmental pollution. This difference may be attributed to the fact that current R&D investment in the Beijing-Tianjin-Hebei region mostly aims to improve manufacturing productivity levels while investment in environmentally friendly and cleaner technologies has not attracted enough attention. Another focus has been the impact of energy consumption levels and GDP on SO2 emissions. The coefficients of energy consumption and GDP are positive and highly significant, suggesting that SO2 emissions increase with increases in energy consumption and GDP. Energy consumption levels certainly influence SO2 emissions. Hence, levels of environment pollution can be reduced by adjusting energy consumption structures and through the use of clean energy sources. 6. Conclusion and policy implications As a result of rapid economic growth and high levels of energy consumption, China currently faces a serious air pollution problem. The Beijing-Tianjin-Hebei region is one of the most heavily polluted regions in China. As cities of the Beijing-Tianjin-Hebei region are closely related in terms of levels of economic development, it is valuable to investigate the spatial effects of FDI on air quality levels in this region. Such studies can allow us to analyze effects of economic activity on this region and to make better decisions in
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coordinating the relationship between economic development and environmental protection. This paper examined the impacts of FDI on SO2 emissions in the Beijing-Tianjin-Hebei region based on a spatial economic model. We first analyze the effects of FDI on air pollution in the BeijingTianjin-Hebei region by applying a stepwise regression. Using an LM test, spatial correlation relationships of SO2 emissions among cities of the Beijing-Tianjin-Hebei region are confirmed. We then adopt the Spatial Durbin Model to examine the effects of FDI on SO2 emissions in this region. Our findings have several important policy implications. To control air pollution in the Beijing-Tianjin-Hebei region, municipal governments in the region should cooperate closely to set up air quality control regulations. Due to the spatial effects of air pollution, it is impossible to address air pollution problems without collaborating with other neighboring cities. At the same time, as FDI flowing into the second industry has a considerable impact on air quality levels, it is necessary to adjust the direction of FDI inflows into the region. The government should encourage more FDI into cleaner and more energy efficient industries. Moreover, Beijing and Tianjin cannot attempt to reduce air pollution levels by merely transferring second industry activities to neighboring cities. Although such interventions can have temporary effects, they can also eventually increase the SO2 emissions of neighboring cities and jeopardize local air quality levels. This study presents some limitations. First, although our results suggest that air pollution in a city is spatially correlated with pollution levels in neighboring cities, we did not analyze the direct relationship between any two adjacent cities in this region. Second, our study is based on a geographic distance matrix and can benefit from the use of other spatial weight matrices such as those of economic connectivity. Other spatial weight matrices may highlight new perspectives for investigating the spatial influence of FDI on air quality. In addition, future studies can use dynamic panel data models to better understand the spatial relationship between FDI and SO2 emissions. Acknowledgements This paper was funded through the National Natural Science Foundation of China (Award #: 71272057, 71572013, and 71521002), the Beijing Natural Science Foundation (Award #: 9152015) and the Joint Development Program of the Beijing Municipal Commission of Education. References Abdouli, M., Hammami, S., 2017. Economic growth, FDI inflows and their impact on the environment: an empirical study for the MENA countries. Qual. Quantity 51, 1e26. Al-Mulali, U., Ozturk, I., 2015. The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in the MENA (Middle East and North African) region. Energy 84, 382e389. Albornoz, F., Cole, M.A., Elliott, R.J.R., Ercolani, M.G., 2014. The environmental actions of firms: examining the role of spillovers, networks and absorptive capacity. J. Environ. Manag. 146, 150e163. Baek, J., 2016. A new look at the FDIeincomeeenergyeenvironment nexus: dynamic panel data analysis of ASEAN. Energy Policy 91, 22e27. Chandran, V.G.R., Tang, C.F., 2013. The impacts of transport energy consumption, foreign direct investment and income on CO 2 emissions in ASEAN-5 economies. Renew. Sustain. Energy Rev. 24, 445e453. Chen, G.S., Yao, Y., Malizard, J., 2016. Does foreign direct investment crowd in or crowd out private domestic investment in China? The effect of entry mode. Econ. Model. 61, 409e419. Cheng, Z., 2016. The spatial correlation and interaction between manufacturing agglomeration and environmental pollution. Ecol. Indic. 61, 1024e1032. Chiang, T.-Y., Yuan, T.-H., Shie, R.-H., Chen, C.-F., Chan, C.-C., 2016. Increased incidence of allergic rhinitis, bronchitis and asthma, in children living near a petrochemical complex with SO2 pollution. Environ. Int. 96, 1e7.
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Contents lists available at ScienceDirect
Journal of Cleaner Production journal homepage: www.elsevier.com/locate/jclepro
The impact of foreign direct investment on SO2 emissions in the Beijing-Tianjin-Hebei region: A spatial econometric analysis Lin Zhu a, Qingmei Gan a, Yan Liu a, Zhijun Yan a, b, c, * a
School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China c Sustainable Development Research Institute for Economy and Society of Beijing, Beijing 100081, China b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 12 January 2017 Received in revised form 30 June 2017 Accepted 5 August 2017 Available online 5 August 2017
This paper examines the spatial impacts of foreign direct investment (FDI) on sulfur dioxide (SO2) emissions in the Beijing-Tianjin-Hebei region located in northern China. To avoid making coefficient estimation errors, we use spatial panel data on 10 cities in the Beijing-Tianjin-Hebei region for 2000 to 2013. We first analyze the effects of FDI on air pollution levels in the Beijing-Tianjin-Hebei region from ordinary least squares (OLS). Then, the existence of a spatial correlation is examined via a Lagrange Multiplier (LM) test, and the Spatial Durbin Model (SDM) is adopted to investigate the influence of FDI on SO2 emissions in this region. The results show that FDI has a significantly positive impact on SO2 emissions, suggesting that an increase in FDI inflows would increase air pollution levels in the BeijingTianjin-Hebei region. Furthermore, the air quality of local cities is also influenced by FDI inflows in surrounding areas. © 2017 Published by Elsevier Ltd.
Keywords: Foreign direct investment Sulfur dioxide emissions Spatial econometric analysis Beijing-Tianjin-Hebei region
1. Introduction Since joining the World Trade Organization (WTO), China has become one of the most attractive destinations for foreign direct investment (FDI) in the world. According to the 2014 China statistical yearbook, the volume of FDI inflows into China has reached $119.721 billion. This is much higher than the average FDI of the United States, which achieved an FDI of just $86 million in 2014. Many studies show that FDI plays a critical role in accelerating Chinese economic development not only in terms of promoting technological optimization but also in transforming industrialization processes and business environments in China (Chen et al., 2016). FDI has remarkably promoted China's economic growth over the past few years while influencing local environments significantly at the same time. One of the most intense debates occurring today concerns whether FDI inflows are turning developing countries into “pollution haven” (Wagner and Timmins, 2010). China's economic growth and large FDI inflows seem to be accompanied by serious pollution problems. China's development
* Corresponding author. School of Management and Economics, Beijing Institute of Technology, Beijing 100081, China. E-mail address: [email protected] (Z. Yan). http://dx.doi.org/10.1016/j.jclepro.2017.08.032 0959-6526/© 2017 Published by Elsevier Ltd.
model involves “growth first, cleaning up later,” which has resulted in serious pollution problems (Shen et al., 2017; Zhang et al., 2017). The country's significant achievements in economic development have rendered China the largest single contributor to global sulfur dioxide (SO2) emissions (Liu et al., 2017). High SO2 emissions levels have led to the formation of acid rain and have incurred considerable damage to ecosystems while hindering socioeconomic development (Zhang and Chang, 2012). Meanwhile, SO2 also poses threats to human health, leading to the development of chronic diseases (Chiang et al., 2016). The Beijing-Tianjin-Hebei region includes the provinces of Beijing, Tianjin, and Hebei in northeastern China. Due to its geographical advantages and resources, the region attracts considerable levels of foreign investment that have significantly accelerated its industrialization. At the same time, the Beijing-Tianjin-Hebei region is the most heavily polluted area in China. Emissions of SO2 generated in the Beijing-Tianjin-Hebei region still make up a large proportion of emissions generated in China. However, as one of the most important economic and political regions in China, spatial effects of environment pollution in the Beijing-Tianjin-Hebei region are seldom investigated. Using data on SO2 emissions and FDI in the Beijing-TianjinHebei region, this study examines the relationship between FDI and SO2 emissions. Specifically, we adopt spatial econometric models to explore the spatial effects of FDI on air pollution in the
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capital region. China's air pollution patterns have clear regional spatial characteristics (Xu et al., 2017; Yu et al., 2015), rendering spatial econometric models appropriate in explaining the inherent relationship between SO2 emissions and FDI. We use the Spatial Durbin Model (SDM) to validate the impacts of FDI on SO2 emissions, and our results offer new insight into the control of SO2 emissions in the Beijing-Tianjin-Hebei region. This paper makes three main contributions. First, to the best of our knowledge, this is the first study to evaluate the influence of FDI on SO2 emissions by applying spatial econometric methods to the Beijing-Tianjin-Hebei region. In using the spatial durbin model, estimation biases resulting from ignoring spatial correlations of air pollution can be avoided. Second, most prior studies have analyzed the effects of FDI on air pollution levels at the country level while studies on the regional level have not been conducted. In this paper, we focus on the Beijing-Tianjin-Hebei region, one of the three main industrial regions of China. It contributed to 10.4% of China's GDP in 2014 and is treated as the main development region in China's 13th Five-Year Plan. At the same time, in recent years due to rapid economic development and population increases in this region, air pollution has progressively become a more serious problem. Tianjin, Hebei and Beijing in particular are now facing much greater pressures to control air pollution levels (Ikram et al., 2015). Finally, we explore the effects of FDI in the second industry on SO2 emissions. Urban air pollution levels are strongly influenced by the second industry (Hao and Liu, 2016). As a major economic hub, the Beijing-Tianjin-Hebei region has attracted considerable levels of FDI with the proportion of FDI flowing into the second industry accounting for more than fifty percent. Therefore, studying the impacts of FDI in the second industry on air pollution levels offers new insights necessary to improve investment structures. The rest of this paper is structured as follows. Section 2 reviews related literature in this field. Section 3 introduces the data sources and methodologies used. Section 4 describes the research results. Section 5 presents a discussion. Finally, we summarize the study results and make policy suggestions in Section 6. 2. Literature review With rapid growth in FDI, many studies have focused on the relationship between FDI and air pollution levels to determine whether increases in FDI affect local air quality levels. However, these studies have not yet obtained consistent results (Kim and Baek, 2011; Kirkulak et al., 2011; Wang and Chen, 2014). FDI accelerates industrial development in local areas and typically increases levels of energy consumption. Many research findings show that air quality is compromised by increases in FDI and energy consumption, which are found in developing and developed countries (Abdouli and Hammami, 2017; Hoffmann et al., 2005; Shofwan and Fong, 2012; Zhang, 2011). Results show that FDI has a negative effect on regional air quality levels and leads to high levels of air pollution. Waldkirch and Gopinath (2008) examined the existence of pollution haven effects of FDI in Mexico. Their results reveal a positive and significant correlation between FDI and SO2 emissions. Similarly, Cole et al. (2009) investigated the relationship between economic growth, FDI, and the environment in China. Using data on 112 cities for 2001e2004, they found evidence of the existence of a pollution haven effect in China. In addition, Wang and Chen (2014) also showed that investments from Organization for Economic Co-operation and Development (OECD) countries have led to significant increases in SO2 emissions in 287 Chinese cities, proving the pollution haven hypothesis. Using panel data on five countries of the Association of Southeast Asian Nations for 1981e2010, Baek (2016) estimated the impacts of FDI inflows on energy consumption and environmental quality. Their results show
that FDI deteriorates the environment, supporting the pollution haven hypothesis. However, other studies have made conflicting conclusions that more FDI improves air quality levels. Letchumanan and Kodama (2000) found that FDI improves environmental welfare by spurring the use of cleaner technologies and environmentally friendly products and manufacturing processes. Dean et al. (2005) claimed that FDI benefits the environment of the host country. When FDI from developed countries results in the use of cleaner technologies, it can improve environmental outcomes in developing countries. Zeng and Eastin (2007) investigated the effects of trade openness and FDI on air pollution levels for 1996e2004 in China. Their results show that increased trade openness and FDI are positively correlated with environmental protection in China and with reductions in SO2 emissions. Zheng et al. (2010) used a cross-city dataset to test relationships between ambient air pollution, house prices, wages and FDI. Their results show that higher per-capita FDI inflows result in lower SO2 concentrations. Kirkulak et al. (2011) analyzed the impacts of FDI on China's air quality levels based on panel data on 286 cities for 2001 to 2007. Their results show that the presence of FDI reduces SO2 emissions, as characteristics of FDI are normally driven by the use of advanced technologies. Some studies have also found that FDI had little impact on air quality levels. He (2006) established simultaneous equations to explore relationships between FDI, income and SO2 emissions in China. The results show that while FDI increases SO2 emissions levels, related impacts are marginal. Similar results were found by Kim and Baek (2011), who estimated the impacts of FDI on air pollution levels in 40 developing countries using an autoregressive distributed lag (ARDL) model. They found that FDI has limited effects on environmental quality in both developed and developing countries over the long term. Although previous studies have extensively examined the effects of FDI on air pollution, corresponding results vary considerable. FDI flows into different industries and investments in different industries can have different environmental effects. However, industry characteristics have been largely ignored in previous studies (Kirkulak et al., 2011; Zheng et al., 2010). As 65.7% of FDI flows into the second industry in China, the relationship between FDI in the second industry and air pollution is worth exploring. The second industry is a major choice of short-term FDI, and FDI in the second industry has stronger impacts on the environment than FDI in the first and third industries (Li and Luo, 2012). Recent studies also suggest that it is better to attract FDI inflows in the services industry than in the manufacturing industry for purposes of environmental protection (Baek, 2016). However, quantitative analyses of impacts of FDI inflows into the second industry on air pollution are currently missing. As a new area of economic development, the Beijing-TianjinHebei region has received more attention in recent years (Xu et al., 2017). The Chinese government is trying to interactively coordinate the economic, social and scientific development of the area. Due to its rapid economic development, the region has been reported to be one of the most heavily polluted areas in China (Xu et al., 2017; Zhao et al., 2013). Due to inherently strong connections between cities in this region, air pollution in surrounding areas may contribute to the city's air quality, and thus regional air pollution control strategies have been highly cost-efficient (Wu et al., 2015). Over the past decade, FDI has played a key role in economic development in this area. In 2013, FDI in the Beijing-Tianjin-Hebei region accounted for 28.6% of that of the entire country (CNBS, 2014). As a valuable promoter of local economic development, FDI has a significant spatial effect on regional economic development that also influences environment quality (Ng and Tuan, 2006).
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In summary, studies have extensively examined the relationship between FDI and SO2 emissions. However, spatial effects of FDI on SO2 emissions have been explored less often. At the same time, the Beijing-Tianjin-Hebei region is one of most heavily industrialized and polluted areas in China. Spatial relationship analyses of FDI and SO2 emissions for this region can help policy makers control regional air pollution levels. Finally, studies have largely disregarded variations in FDI across industries, which may have biased research results. Therefore, we use FDI in the second industry to evaluate the impacts of FDI on SO2 emissions. 3. Data and method 3.1. Study areas According to China's National Development and Reform Commission, the Beijing-Tianjin-Hebei region includes 10 cities, namely, Beijing, Tianjin, Shijiazhuang, Tangshan, Qinhuangdao, Langfang, Baoding, Cangzhou, Chengde and Zhangjiakou (Fig. 1). Data show that in 2014, this area accounted for 2.2% of China's land and 7.6% of China's population and yielded 10.4% of the national GDP and consumed more than 10% of the country's coal resources. According to 2013 air quality status reports on 74 cities in China (Tang et al., 2016), 7 of China's 10 cities with the worst air quality are located in Hebei, thus rendering the Beijing-Tianjin-Hebei region one of the most heavily polluted regions in China. 3.2. Data source High levels of coal consumption demand lead to serious levels of air pollution (Yan et al., 2016; Yu et al., 2015). In 2015, coal consumption levels in the Beijing-Tianjin-Hebei region were 30 times
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as high as average global consumption levels. Of air pollutants generated from coal consumption, SO2 is the main pollutant generated in the Beijing-Tianjin-Hebei region. Moreover, industrial SO2 emissions levels are consistently recorded in the China City Statistics Yearbook. Therefore, we use total annual industrial SO2 emissions for the ten cities from the China City Statistical Yearbook as pollution levels in our analysis. As the second industry is the main generator of air pollution, we focus on exploring the spatial relationship between annual FDI flowing into the second industry and air pollution (Chandran and Tang, 2013). Annual FDI in the second industry was drawn from the China City Statistical Yearbook (CNBS, 2014). According to previous studies, the following other factors are used as explanatory variables: R&D investment (RD), gross domestic product (GDP), population, and energy consumption (EC). R&D investment refers to investment in technology and science. As previous studies show that technologies have a positive impact on environment protection (Kirkulak et al., 2011), we propose that R&D investment may have a positive impact on air pollution reduction outcomes. GDP is a proxy for economic growth (Lee, 2013), and this variable captures the impacts of economic activities on air pollution. A population is defined as the total number of permanent residents in a city, and Liddle (2015) found that population size is the largest driver of air pollutant emissions. Energy consumption is a key influencing factor of air pollution levels, and Al-Mulali and Ozturk (2015) found that energy consumption and air pollution levels exhibit a positive long-run bi-directional relationship. These factors are thus also used as explanatory variables and are included in the models used, and data were obtained from China Statistical Yearbook for 2000e2013. More detailed information on the key variables used is shown in Table 1. 3.3. Methods We first explore the association between FDI and SO2 emissions by applying a traditional method without taking spatial correlations between adjacent cities into consideration. By introducing FDI, GDP, R&D investment, population size, and energy consumption into the model, we establish the OLS model as follows:
lnðSO2 Þit ¼ b0 þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit þ b4 lnPOPit þb5 lnECit þ εit (1)
Fig. 1. The Beijing-Tianjin-Hebei region.
where i represents a city and where t denotes a year. (SO2)it is the SO2 emissions of a city i for year t. FDIit represents FDI made in the second industry of city i in year t. GDPit, POPit, RDit and ECit represent the factors given in Table 1, influencing SO2 emissions in a given city at time t. εit is the error term. Before building the spatial econometric model of FDI and SO2 emissions, we first test the existence of a spatial effect. We apply the improved LM test method developed by Elhorst for spatial panel data (Elhorst, 2003). When using the LM test method, when the results of the LM-err and LM-lag are not statistically significant, the traditional panel model should be used. If any of them are significant, the spatial econometric model should be used to describe spatial effects. We then re-examine the relationship between FDI and SO2 by considering spatial correlations. Following Elhorst (2003), three econometric models can be used to examine spatial correlations. The first model is referred to as the Spatial Lag Model (SLM). The SLM hypothesizes that the value of the dependent variable for area i is affected by neighbors' dependent variables. This means that SO2 emissions in area i are affected by the SO2 emissions of neighboring areas.
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Table 1 Definition of the variables. Variables
Definition
Unit of measurement
Sulfur dioxide emissions (SO2) Foreign direct investment (FDI) R&D investment (RD) Gross domestic product (GDP) Population size (POP) Energy consumption (EC)
Total industry sulfur dioxide emissions of individual cities Total FDI in the second industry Technology and science level Per city's gross domestic product Total number of people at the end of a year Per city's energy consumption
10,000 10,000 10,000 10,000 10,000 10,000
4. Results
The Spatial Lag Model (SLM) is defined as:
lnðSO2 Þit ¼ r
n X
tons RMB RMB RMB person tons coal
4.1. Descriptive statistics
Wij lnðSO2 Þjt þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit
j¼1
þb4 lnPOPit þ b5 ECit þ mi þ li þ εit (2) where i and j represent cities i and j, respectively (i sj) and where t represents the year. r is the corresponding spatial parameter reflecting the spatial dependence of the sample observations. It thus evaluates the impacts of adjacent area SO2 emissions on local SO2 emissions. Wij is an element in the (N N) spatial weight matrix, which is the geographic distance matrix used in our study. mi denotes the time fixed effect of spatial units. li represents spatially fixed specific effects. εit is the random disturbance term. The second model is the spatial error model (SEM), which assumes that spatial correlation stems from the error term of dependent variables for adjacent areas. The SEM is expressed as:
lnðSO2 Þit ¼ b0 þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit þ b4 lnPOPit þb5 lnECit þ mi þ li þ 4it (3)
Table 2 presents detailed information on all variables for the 10 cities for our study period (2000e2013). Maximum SO2 emissions levels reach 46.4599 while the minimum value is 2.39. FDI in cities of the Beijing-Tianjin-Hebei region ranges from 459 to 852,418. 4.2. The impacts of FDI on SO2 emissions We first examine the impacts of FDI on SO2 emissions by running the Ordinary Least Squares (OLS) estimation. Table 3 presents estimation results of the OLS based on different specifications. The impacts of FDI, RD, EC and GDP are statistically significant. When we do not consider spatial effects, FDI has negative impacts on SO2 emissions. This observation coincides with the results of Kirkulak et al. (2011), who found that FDI reduces air pollution levels. In addition, the coefficient of GDP is also negative, indicating that the impacts of GDP on environment are positive. EC and the RD have a significantly positive impact on SO2 emissions. Population also has a positive impact on SO2 emissions though it is not significant. The coefficients shown in Table 3 imply that a 1% increase in FDI leads to a 0.84% decrease in SO2 emissions. 4.3. Spatial autocorrelation test
4it ¼ d
n X
Wij 4jt þ εit
j¼1
where 4it is the spatial autocorrelation error term. d is the spatial autoregressive coefficient, which reflects the effects of residuals of adjacent areas on residuals of the local area, and εit is an i.i.d (independent and identically distributed) residual. The third model is the Spatial Durbin model (SDM). The SDM assumes that the dependent variable of region i is spatially dependent on the independent and dependent variables of other adjacent regions, which are specified as:
lnðSO2 Þit ¼ r
n X
Wij lnðSO2 Þjt þ b1 lnFDIit þ b2 lnRDit þ b3 lnGDPit
j¼1
þb4 lnPOPit þ b5 ECit þ q1
q3
n X j¼1
Wij lnGDPjt þ q4
n X j¼1
n X
Wij lnFDIjt þ q2
j¼1
Wij lnPOPjt þ q5
n X
n X
Wij lnRDjt þ
The aim of the Hausman test is to determine whether a model with fixed or random effects is superior. The Hausman test results show that the model with fixed effects is significant at the 1% level (Chi-sq. statistic of 85.774 with 6 degrees of freedom, p < 0.01), suggesting that the model with fixed effects is more appropriate. To identify potential spatial effects, we test spatial correlations for the observations and compare SLM, SEM and SDM values through an LM test. As spatial dependence exists within a certain distance, we use a geographic distance matrix to measure spatial adjacency in the Beijing-Tianjin-Hebei region. Geographic distance matrices are widely used in the spatial effect analysis literature (Hao et al., 2016; Keller, 2001; Wang et al., 2013). Our LM test results are presented in Table 4. According to the results, LM spatial lag test results based on the geographic distance matrix are statistically significant (at the 5% level) in terms of spatial fixed effects and spatial time fixed effects. The coefficient of
j¼1
Wij lnECjt þ mi þ li þεit
j¼1
(4) where q is the spatial autocorrelation coefficient of independent variables. The spatial econometric models are estimated based on maximum likelihood values. All of the analyses are conducted using R 3.2.5 software.
Table 2 Descriptive statistics. Variable
Unit
SO2 FDI RD GDP POP EC
10,000 10,000 10,000 10,000 10,000 10,000
tons RMB RMB RMB person tons coal
Mean
Std. Dev
Min
Max
11.2574 116315.5 52527.31 2540.99 748.375 2565.822
8.6733 207101.7 130861.2 3552.262 428.778 3209.867
2.39 459 638 158.629 136.6 155.0309
46.4599 852418 681346 19500.6 2144.8 24995.86
L. Zhu et al. / Journal of Cleaner Production 166 (2017) 189e196 Table 3 The OLS estimation results of FDI impact on SO2 emissions. ln (SO2)
lnFDIit
model (1)
model (2)
model (3)
model (4)
model (5)
3.27* (-1.96)
1.64*** (-4.825) 1.63*** (7.464)
1.34** (-2.645) 5.92* (1.781) 1.64*** (7.478)
4.09* (-1.612) 7.14* (1.75) 1.85*** (7.736) 1.148 (-0.106)
0.84* (-2.201) 1.69* (1.944) 8.15*** (10.22) 7.96** (-2.358) 1.168 (1.46) 144 0.3179
lnRDit lnECit lnGDPit lnPOPit Obs R2
144 0.2557
144 0.274
144 0.2769
144 0.2978
Note: In parentheses the t-values are given. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.
the spatial fixed effect of the LM spatial lag is 29.69, and it is significant at the 1% level. The robust spatial lag LM test results based on time fixed effects, spatial fixed effects and spatial-time fixed effects are significant. In addition, the LM spatial error test results with spatial fixed effects are significant. The robust LM spatial error test results based on time fixed effects, spatial fixed effects and spatial-time fixed effects are statistically significant at the 5% level. These results show that the spatial models are more appropriate than non-spatial interaction effects of the traditional panel models (Kang et al., 2016). This proves the existence of spatiality and suggests that the spatial models are more appropriate than the traditional panel data in terms of analyzing the impacts of FDI on SO2 emissions. The results shown in Table 4 illustrate that the p-values of most of the tests are lower than 0.05, suggesting that the null hypotheses of no spatially lagged dependent variable and no spatially autocorrelated error term can be definitively rejected. Moreover, we estimate the Spatial Durbin Model and then perform a Wald test
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and Likelihood Ratio (LR) test to select a suitable model. As Table 5 shows, the Wald and LR test results are significant at the 1% level, suggesting that the SDM is more appropriate than the SLM and SEM (Hao et al., 2016). We thus use an SDM model that includes the spatially lagged dependent variable and spatially autocorrelated error term to address issues of spatial dependence. 4.4. Spatial econometric regression results Table 6 presents the maximum likelihood (ML) estimation of the SDM. The estimation results of Log-likelihood can be used to select the most appropriate model of the spatial fixed effects, time fixed effects and spatial-time fixed effects models. As is shown in Table 6, the SDM with spatial fixed effects includes the maximum Loglikelihood value. Therefore, we use the estimation results of the SDM with spatial fixed effects as an interpretative object for interpreting factors affecting SO2 emissions while using the others for reference purposes. It is noteworthy that the spatial correlation parameter r of the SDM estimation is positive and significant at a 10% level. This positive estimated coefficient indicates that SO2 emissions in adjacent areas have a positive effect on local SO2 emissions (Wu and Tian, 2012). As SO2 emissions in this study are estimated based on industrial emissions, this implies that SO2 emissions of cities of the Beijing-Tianjin-Hebei region have spatial spillover effects. Our final estimate of 0.118 suggests that a 1% increase in the SO2 emissions of adjacent areas should result in a 0.118% increase in SO2 emissions in local cities. This reveals an apparent spatial spillover effect of SO2 emissions. Through spatial spillovers, local city air pollution levels can be affected by the air pollution levels of adjacent cities (Cheng, 2016). Our results reflect those of He (2006), who estimated coefficients of spatially lagged independent variables as significant at the 1% level with the exception of those for the coefficient of population. The results also reveal spatial spillover effects between FDI and SO2 emissions. As is shown in Table 6, FDI has a positive effect on SO2 emissions
Table 4 The LM test for choosing the spatial lag model or the spatial error model. Variables
Panel data OLS
Time fixed effects
Spatial fixed effects
Spatial-time fixed effects
b0
9.205*** (4.98) 0.000009** (2.64) 0.000026** (2.37) 0.000157* (1.87) 0.002006** (2.79) 0.01805*** (4.79) 56.2737 0.3601 0.3340 2.0696 525.2638 1.0458 (p ¼ 0.306) 3.6615* (p ¼ 0.056) 4.1852** (p ¼ 0.041) 6.8009** (p ¼ 0.009) Statistic 85.774
e
e
e
0.00003* (-1.78) 0.00045*** (-4.2) 0.000198 (-0.25) 0.004878** (2.08) 0.01022*** (4.07) 37.7756 0.5085 0.4919 2.4350 495.0946 0.4500 (p ¼ 0.502) 3.9289** (p ¼ 0.047) 0.6831 (p ¼ 0.409) 4.1621** (p ¼ 0.041) DOF 6
0.000005** (2.27) 0.00031* (-1.61) 0.001024* (1.91) 0.00762** (-2.13) 0.00082* (1.70) 19.8923 0.1901 0.1627 1.5711 445.7122 29.6920*** (p ¼ 0.000) 86.4289*** (p ¼ 0.000) 4.7735** (p ¼ 0.029) 61.5104*** (p ¼ 0.000) P-value <0.001
0.0001* (-1.83) 0.026* (-1.92) 0.00103* (-1.9) 0.00108 (-1.35) 0.000128* (-1.73) 12.8410 0.0874 0.0566 2.4363 412.0102 8.6191** (p ¼ 0.003) 43.2706*** (p ¼ 0.000) 0.6411 (p ¼ 0.423) 35.2926*** (p ¼ 0.000)
lnFDIit lnRDit lnGDPit lnPOPit lnECit
a2
R2 Adjusted R2 D-W Log likelihood LM spatial lag Robust LM spatial Lag LM spatial error Robust LM spatial error Hausman test
Notes: Numbers in the parentheses represent t-values. ***, ** and * indicate significance at the 1%, 5% and 10% level.
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Table 5 Diagnostic tests of spatial specification. Determinants
Spatial fixed effects
Time fixed effects
Spatial-time fixed effects
Wald test spatial lag LR test spatial lag Wald test spatial error LR test spatial error
124.71*** 180.80*** 112.65*** 101.54***
83.90*** 139.86*** 76.95*** 97.76***
102.34*** 113.09*** 102.89*** 113.16***
Note: Both tests follow a chi-squared distribution with K degrees of freedom. ***, ** and * indicate significance at the 1%, 5% and 10% levels respectively.
Table 6 Estimation results of spatial Durbin model. Determinants
Spatial fixed effects
Time fixed effects
Spatial-time fixed effects
r
0.118* (1.932) 0.232*** (2.850) 0.0510* (1.695) 0.731*** (7.448) 0.214 (0.747) 0.715*** (6.509) 0.128* (1.951) 0.115* (1.957) 0.124* (1.542) 0.223 (-0.864) 0.083*** (2.417) 0.228 0.601 51.526
0.108** (2.127) 0.230** (2.087) 0.596** (2.077) 0.733** (2.105) 0.245 (0.129) 0.711** (2.120) 1.22** (2.124) 0.115*** (3.127) 1.41** (2.243) 0.217 (0.275) 0.106** (2.220) 0.251 0.607 65.982
0.127*** (3.127) 0.851** (2.813) 0.733** (2.769) 0.098* (1.923) 0.122** (2.123) 0.110*** (6.120) 0.117*** (3.123) 0.20* (1.986) 0.229** (2.224) 0.258** (2.275) 0.199** (2.219) 0.241 0.706 58.970
lnFDIit lnRDit lnGDPit lnPOPit lnECit W*lnFDIit W*lnRDit W*lnGDPit W*lnPOPit W*lnECit
s2
R2 Log-likelihood
Notes: Numbers in the parentheses represent t-values. ***, ** and * indicate significance at the 1%,5% and 10% level.
in the SDM with spatial fixed effects, and the coefficient implies that an additional 1% increase in FDI in the second industry should lead to an approximate 0.232% increase in SO2 emissions. This indicates that FDI inflows should negatively affect the environment. The spatial spillover effects of FDI on adjacent areas are significantly positive: a 1% increase in FDI should lead to a 0.128% increase in SO2 emissions in adjacent areas. It is clear that regional SO2 emissions have spatial effects and that FDI increases air pollution levels. In addition, the estimated coefficient of R&D is significantly positive, indicating that R&D is another primary cause of SO2 emissions in the Beijing-Tianjin-Hebei region. An increase in R&D of 1% is associated with a 0.051% increase in SO2 emissions. Meanwhile, spatial spillover effects of adjacent areas on R&D investment are positive, and a 1% increase in R&D investment should lead to a 0.115% increase in SO2 emissions in adjacent areas. The impacts of GDP and energy consumption on SO2 emissions are significantly positive, indicating that an increase in GDP and energy consumption should also lead to an aggravation of air pollution levels. The GDP impact coefficient is 0.731, meaning that when a city increases its own GDP by 1%, it should increase SO2 emissions by approximately 0.731%. In addition, a 1% increase in energy consumption can lead to an average 0.715% increase in SO2 emissions. These results show that the rapid economic growth of the Beijing-Tianjin-Hebei region has promoted an increase in energy consumption and SO2 emissions levels over our study period. The economic growth of the Beijing-Tianjin-Hebei region is still heavily shaped by energy-intensive industries, which place considerable pressure on energy demand and pollution control outcomes (Hu
et al., 2014). Spatial spillover effects on adjacent areas on GDP growth are positive. A 1% increase in the GDP of adjacent areas results in a 0.124% increase in SO2 emissions in local areas. Furthermore, spatial spillover effects on energy consumption are significantly positive. Rather, a 1% increase in energy consumption leads to a 0.083% increase in SO2 emissions. Surprisingly, population has a negative effect on SO2 emissions but it is not significant, and spatial spillover effects of adjacent areas on population growth are positive but also not significant. 5. Discussion According to the SDM estimation results, we find significant spatial correlations for industrial SO2 emissions in the BeijingTianjin-Hebei region. Furthermore, FDI inflows into the second industry have a positive effect on SO2 emissions, suggesting that higher levels of FDI result in a deterioration of air quality. Our results show that local SO2 emissions will increase by approximately 0.118% when SO2 emissions levels of adjacent areas increase by 1%. The positive coefficient of r implies that SO2 emissions of the Beijing-Tianjin-Hebei region may exhibit a spatial spillover effect. That is, an increase in SO2 emissions in one city should bring about a significant effect on the SO2 emissions of other cities in the Beijing-Tianjin-Hebei region. This significant positive spatial dependence may be attributed to several causes. Energy consumption in the Beijing-Tianjin-Hebei region is still dominated by coal use, the main source of SO2 emissions. Furthermore, an increase in coal consumption in one city is dependent on
L. Zhu et al. / Journal of Cleaner Production 166 (2017) 189e196
consumption patterns in other cities. A higher level of energy consumption demand in one city is likely to promote energy consumption in neighboring cities (Hao et al., 2014). Therefore, the SO2 emissions of different cities are closely related due to similar coal consumption levels between cities. Moreover, intensifying economic relationships within the Beijing-Tianjin-Hebei region result in strong spillover effects of regional air pollutants emissions (Tang et al., 2015). Product import and export activities among different cities are frequent and are illustrated as a daily trade model for the Beijing-Tianjin-Hebei region. Such multilateral trade among different cities generates strong spatial lag correlations among regional SO2 emissions. At the same time, levels of economic development in different cities of the Beijing-Tianjin-Hebei region are closely correlated. For example, rapid growth in Beijing's economy normally spurs rapid development in surrounding cities, implying that these cities exhibit similar levels of air pollutant emissions. We find a positive relationship between FDI inflows and SO2 emissions. This finding is consistent with those of Baek Jungho (2016) and is in line with those of Ren et al. (2014) who found that high FDI inflows further aggravate air pollution levels in China. In the SDM with spatial fixed effects, coefficients of FDI are significant at the 10% level, suggesting that air pollution levels increase with an increase in foreign investment. As one major feature of FDI in the Beijing-Tianjin-Hebei region, most FDI is made in the second industry, which in turn has serious impacts on air pollution levels. Moreover, it is important to note that economic development in the Beijing-Tianjin-Hebei region is dominated by heavy industry and the majority of FDI has flowed into capital-intensive enterprises. The positive influence of FDI on SO2 emissions found may imply that local environmental regulations do not significantly affect capital-intensive enterprises. Lin (2008) argued that foreign-owned firms are subject to stricter standards and screening protocols. If this is the case, local governments should pay more attention to the implementation of environmental regulations and should ensure that enterprises with FDI meet strict environmental standards. In addition, R&D investment has a negative rather than positive effect on air quality. This indicates that R&D investment has failed to spur economic growth and air quality improvements at the same time. This contradicts the results of previous studies. Albornoz et al. (2014) and Yu et al. (2014) suggested that advanced and high levels of technology can reduce levels of environmental pollution. This difference may be attributed to the fact that current R&D investment in the Beijing-Tianjin-Hebei region mostly aims to improve manufacturing productivity levels while investment in environmentally friendly and cleaner technologies has not attracted enough attention. Another focus has been the impact of energy consumption levels and GDP on SO2 emissions. The coefficients of energy consumption and GDP are positive and highly significant, suggesting that SO2 emissions increase with increases in energy consumption and GDP. Energy consumption levels certainly influence SO2 emissions. Hence, levels of environment pollution can be reduced by adjusting energy consumption structures and through the use of clean energy sources. 6. Conclusion and policy implications As a result of rapid economic growth and high levels of energy consumption, China currently faces a serious air pollution problem. The Beijing-Tianjin-Hebei region is one of the most heavily polluted regions in China. As cities of the Beijing-Tianjin-Hebei region are closely related in terms of levels of economic development, it is valuable to investigate the spatial effects of FDI on air quality levels in this region. Such studies can allow us to analyze effects of economic activity on this region and to make better decisions in
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coordinating the relationship between economic development and environmental protection. This paper examined the impacts of FDI on SO2 emissions in the Beijing-Tianjin-Hebei region based on a spatial economic model. We first analyze the effects of FDI on air pollution in the BeijingTianjin-Hebei region by applying a stepwise regression. Using an LM test, spatial correlation relationships of SO2 emissions among cities of the Beijing-Tianjin-Hebei region are confirmed. We then adopt the Spatial Durbin Model to examine the effects of FDI on SO2 emissions in this region. Our findings have several important policy implications. To control air pollution in the Beijing-Tianjin-Hebei region, municipal governments in the region should cooperate closely to set up air quality control regulations. Due to the spatial effects of air pollution, it is impossible to address air pollution problems without collaborating with other neighboring cities. At the same time, as FDI flowing into the second industry has a considerable impact on air quality levels, it is necessary to adjust the direction of FDI inflows into the region. The government should encourage more FDI into cleaner and more energy efficient industries. Moreover, Beijing and Tianjin cannot attempt to reduce air pollution levels by merely transferring second industry activities to neighboring cities. Although such interventions can have temporary effects, they can also eventually increase the SO2 emissions of neighboring cities and jeopardize local air quality levels. This study presents some limitations. First, although our results suggest that air pollution in a city is spatially correlated with pollution levels in neighboring cities, we did not analyze the direct relationship between any two adjacent cities in this region. Second, our study is based on a geographic distance matrix and can benefit from the use of other spatial weight matrices such as those of economic connectivity. Other spatial weight matrices may highlight new perspectives for investigating the spatial influence of FDI on air quality. In addition, future studies can use dynamic panel data models to better understand the spatial relationship between FDI and SO2 emissions. Acknowledgements This paper was funded through the National Natural Science Foundation of China (Award #: 71272057, 71572013, and 71521002), the Beijing Natural Science Foundation (Award #: 9152015) and the Joint Development Program of the Beijing Municipal Commission of Education. References Abdouli, M., Hammami, S., 2017. Economic growth, FDI inflows and their impact on the environment: an empirical study for the MENA countries. Qual. Quantity 51, 1e26. Al-Mulali, U., Ozturk, I., 2015. The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in the MENA (Middle East and North African) region. Energy 84, 382e389. Albornoz, F., Cole, M.A., Elliott, R.J.R., Ercolani, M.G., 2014. The environmental actions of firms: examining the role of spillovers, networks and absorptive capacity. J. Environ. Manag. 146, 150e163. Baek, J., 2016. A new look at the FDIeincomeeenergyeenvironment nexus: dynamic panel data analysis of ASEAN. Energy Policy 91, 22e27. Chandran, V.G.R., Tang, C.F., 2013. The impacts of transport energy consumption, foreign direct investment and income on CO 2 emissions in ASEAN-5 economies. Renew. Sustain. Energy Rev. 24, 445e453. Chen, G.S., Yao, Y., Malizard, J., 2016. Does foreign direct investment crowd in or crowd out private domestic investment in China? The effect of entry mode. Econ. Model. 61, 409e419. Cheng, Z., 2016. The spatial correlation and interaction between manufacturing agglomeration and environmental pollution. Ecol. Indic. 61, 1024e1032. Chiang, T.-Y., Yuan, T.-H., Shie, R.-H., Chen, C.-F., Chan, C.-C., 2016. Increased incidence of allergic rhinitis, bronchitis and asthma, in children living near a petrochemical complex with SO2 pollution. Environ. Int. 96, 1e7.
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