What,Is,the,Role,of,FDI,in,Environmental,Pollution?:What's in

　　Abstract: This paper analyzes the distribution patterns and spatial dynamic transitions of foreign direct investment (FDI) and pollution from 2000 to 2009 in China’s provinces by using the comprehensive pollution index (CEPI) and exploratory spatial data analysis. Findings suggest that FDI as well as environmental pollution in our provinces exists an obvious spatial autocorrelation, both of them have remarkable characteristics of path dependence and form different accumulation areas. Currently, the accumulations of high-level FDI correspond to low-level environmental pollution, while the accumulations of low-level FDI are associated with high-level environmental pollution. Furthermore, the authors have empirically analyzed the impact of FDI on China’s environmental pollution by spatial error model (SEM) and spatial lag model (SLM) respectively. Findings suggest that the geographical clustering of FDI has a positive impact on China’s environment, in general, “Pollution Haven Hypothesis” is invalid in China. In addition, there are remarkable differences in the impact of FDI on environmental pollution due to different sources, the foreign capital from offshore financial centers has significantly alleviated pollution in China while that from developed countries in East Asia and the West has played an insignificant role in environmental pollution.
　　Key words: foreign direct investment (FDI), environmental pollution, spatial autocorrelation, pollution haven
　　JEL Classifications: F21, O13, R12.
　　1. Introduction
　　Massive FDI inflows into China following its pro-market reforms have remarkably promoted Chinese economic growth. However, economic progress and FDI have brought with worsen environment– air pollution, solid waste, acid rain and other negative side-effects of economic development have become increasingly pervasive and serious. The relationship between FDI and environmental pollution has prompted serious discussion among scholars. One popular argument adheres to “pollution haven hypothesis,” wherein investors from developed countries are attracted to the lax environmental regulations of developing countries. Some overseas scholars have theoretically and empirically studied this hypothesis (Smarzynska and Wei 2001; Keller and Levinson 2002; Dean 2002), some scholars have provided strong evidence to back its relevance for China, they argue that developing countries tend to relax environmental regulations in order to attract FDI and speed up resource exploitation. This in turn encourages the production of relatively pollution-intensive goods (Markusen 1999; List and Co 2000). Continuously lowered environmental standards lead to a ‘Race-to-the Bottom Hypothesis,’ wherein FDI goes to where firms can operate with the greatest environmental freedom. If pollution haven hypothesis were true, then FDI would certainly be responsible for exacerbating a host country’s pollution problems (Dua et al. 1997). 　　Other scholars, however, argue that, rather than exacerbating a host country’s environmental concerns, FDI has a positive effect on its host environment (Antweiler et al. 2001; Feng 2005; He 2006). Compared with local enterprises, foreign enterprises usually impose relatively strict environmental standards which can even lead to environmental benefits (Zarsky 1999; Chudnovsky and Lopez 1999; Wayne and Shadbegian 2002; Feng 2005). This argument holds that FDI improves a host country’s environmental welfare through introducing environment-friendly technologies and products (Letchumanan and Kodama 2000; Wang and Jin 2007).
　　Chinese scholars have analyzed this issue through the lens of the pollution haven hypothesis, some beginning their analysis with industrial structure (Xia 1999; Zhao 2003; Chen and Li 2010). These scholars have tried to probe into the relocation trends of high-pollution industries induced by the inflow of FDI. Some scholars have looked specifically at a given region in order to study the relationship between FDI and pollution. Among these studies, the empirical findings of Ying and Zhou (2006), Wu (2007), and Liu et al. (2007) all have concluded that the phenomenon of pollution heaven hypothesis is found in China. In addition, some scholars hold that a complicated transmission mechanism exists in the way that FDI impacts pollution (Zhou, Ying 2009), and others have performed a decomposition study on FDI’s environmental impact based on scale, structure and technology (Grossman and Krueger 1991; Panayotou 2000; Zhang 2009; Bao 2010).
　　Due to differences in objective, thinking and methodology, the scholars of different countries have failed to reach consensus regarding the impact of FDI on pollution. Here are some shortcomings in the existing research:
　　Firstly, existing literatures mainly select gas emissions such as SO2 and CO2 as indicators for pollution, different selection in environmental index also will lead to different regression results, thus reducing explanatory power of model to a certain extent. In fact, environmental pollution is not only caused by a single pollutant but comprehensive actions of many pollutants, so a comprehensive indicator or set of indicators is therefore needed.
　　Secondly, existing studies mostly use panel data to analyze the relationship between FDI and pollution, with only a few introducing spatial quantitative methodology. Traditional panel regression assumes that the pollution discharges in each region are independent, which is certainly untrue because wind direction and water flow mean that the environmental quality of one region is affected by the situation in neighboring regions. There is a strong and rather obvious spatial linkage in pollution. In addition, high levels of FDI clustering combined with public policy externalities have served to strengthen these spatial correlations (Poon et al. 2006). Ignoring this will result in biased or wrong parametric tests (Anselin 1988). In recent years, some scholars have carried out spatial empirical analysis on FDI location choice factors (Coughlin et al. 2000; Wang 2004; Wang et al. 2005) and the Environmental Kuznets Curve (EKC) as related to FDI (Maddison 2006; Zhu and Yuan 2010) through spatial cross-section data. Since all of these studies adopt cross-sectional data, the estimated results may be heavily influenced by randomness and contingency(Su 2008). 　　For this reason, based on analysis the spatial dynamic transitions of FDI and pollution, this paper focuses on the effects of the geographical clustering of FDI on provincial pollution. This paper will further work to verify or disprove pollution haven hypothesis from the angle of spatial correlation.
　　This paper hopes to make three key contributions to the preexisting literature:
　　1) By constructing a comprehensive pollution index through employing entropy method, this paper comprehensively measures the degree of pollution in different provinces. 2) This paper will incorporate the spatial correlation of FDI and pollution into quantitative models and empirically analyze FDI’s impact on China’s pollution through the use of spatial panel data. 3) Considering the huge differences among investment motivations and scale of foreign enterprises which come from different sources, the authors chose twelve countries and regions with relatively large amounts of foreign investment and divide FDI into three sources – offshore financial centers (OFCs), developed East Asian economies and developed Western economies, further studying the impact of FDI from different sources on environmental pollution.
　　This paper is structured as follows: Section II includes exploratory spatial data analysis over the distribution pattern of FDI and environmental pollution; Section III constructs a spatial quantitative model to empirically analyze FDI’s impact on pollution in China, further studying the impact of FDI from different sources on environmental pollution; Section IV contains conclusions and policy implications.
　　2. Exploratory Spatial Data Analysis on the Distribution Pattern of FDI and Environmental Pollution
　　To measure the degree of geographical clustering for provincial FDI and environmental pollution, the authors employ exploratory spatial data analysis including Moran’s Index and Moran’s scatter diagram methodology. Furthermore, the authors use the regional Local Indicators of Spatial Associations (LISA) cluster diagram and its significance levels to verify the resultant distribution pattern. Samples come from 2000-2009 data of thirty provinces (Chongqing municipality being integrated into Sichuan Province), with original data sourced from the China Statistical Yearbook for respective years.
　　2.1 Spatial Autocorrelation Test on FDI and Pollution
　　Spatial autocorrelation can be verified through Moran’s Index. As shown in Table 1, the Moran’s Index for FDI is significantly positive, suggesting that FDI’s spatial distribution over China’s thirty provincial regions has a significant positive autocorrelation (i.e. spatial dependency). In other words, the spatial distribution of FDI is not random. Instead, FDI in some provinces tends to cluster together. To test the Moran’s value of environmental pollution, the authors select six categories of pollution indicators1, including industrial wastewater, smoke, sulfur dioxide emissions, waste gas, dust and solid waste. The authors then borrow the entropy method used by Ma et al. (2010) to calculate comprehensive pollution indices. Table 1 shows that, with the exception of 2007, all of the Moran’s values pass significance tests and display wave-shape fluctuations. Moran’s value reached a bottom of 0.0915 in 2007. So environmental pollution in provinces also exist significant spatial autocorrelation, the distribution of environmental pollution in space shows a phenomenon of pollution clustering. 　　A scatter diagram of Moran’s Index divides FDI clustering into four quadrants: Quadrant I (H-H) denotes a high-FDI inflow area surrounded by other high-FDI inflow provinces; Quadrant II (LH) denotes a low-FDI inflow area surrounded by high-FDI inflow provinces; Quadrant III (LL) denotes a low-FDI inflow area surrounded by low-FDI inflow provinces; Quadrant IV (HL) denotes a high-FDI inflow area surrounded by low-FDI inflow provinces. Moran’s scatter diagrams (Figure 1, 2, 3, 4) show that most of the provinces are located in Quadrant I (HH) and Quadrant III (LL). In the Moran’s scatter diagrams showing FDI for 2000 and 2009, provinces located in Quadrants I and III account for 73.33 percent and 76.67 percent, respectively, while in Moran’s scatter diagrams showing pollution for those same years provinces in Quadrants I and III account for 63.33 percent and 66.67 percent, respectively. These results further verify that significant spatial correlation exists between China’s provincial FDI and pollution. Most of the provinces and their neighboring provinces show similar clustering features. Provinces with high levels of FDI are spatially close, while provinces with low FDI also tend to cluster. Similarly, heavily polluted provinces tend to be neighbored by other heavily polluted provinces; lightly polluted provinces are neighbored by other lightly polluted provinces.
　　By borrowing the spatial transition method proposed by Rey (2001), the dynamic transition of FDI can be divided into three categories (Table 2): 1) A transition of both a region and its neighbors to a different province, specifically from Quadrant I to Quadrant IV and from Quadrant II to Quadrant III; 2) A transition of only the neighbors in relative space, specifically from Quadrant I to Quadrant III; 3) The region-neighbor pair remaining at the same level. Over the surveyed time period, twenty-six provinces fit in the third category, representing 86.67 percent of the total. Thus, the geographical distribution of FDI exhibits a clear path dependency and is generally concentrated in coastal areas with low liquidity. FDI relocation shows a trend of movement from high-value accumulation to low-value accumulation. When examining the transition of environmental pollution in terms of provincial area and if neighbors remained at consistent levels over the given timeframe, twenty of the provincial areas exhibit spatial stability – 66.67 percent of the total. Only seven provinces relocated in the category of a transition involving a relative move of only the region, specifically moving from Quadrant HH to Quadrant LH, from Quadrant HL to Quadrant LL, and from Quadrant LH to Quadrant HH. Most of these provinces are located in western China. The transition of both a region and its neighbors to a different province takes place from Quadrant HL to Quadrant HH or from Quadrant LL to Quadrant LH. The transition of only the neighbors in relative space was generally not observed during the survey period, suggesting that China’s provincial pollution enjoys a high level of spatial stability, environmental pollution also shows a high degree of path dependency. 　　2.2 LISA Analysis on FDI and Environmental Pollution
　　LISA is used to verify whether high- or low-value FDI tends more towards spatial clustering. Through the LISA clustering map (Figure 5, Figure 6) and significance testing, FDI is seen to form two different clusters in China. One is Fujian-centered high-level FDI accumulation surrounded by neighboring coastal provinces. Particularly since 2003, the spatial dependency of FDI in Zhejiang, Shanghai and Jiangsu has been relatively more significant. Provinces within this quadrant are more attracted to FDI, and they also push FDI into neighboring regions through cooperation. The other is Qinghai-centered low-level FDI accumulation surrounded by Xinjiang, Gansu, Tibet and Shaanxi. Provinces within this quadrant have low FDI inflows and do not generate strong demonstration effects.
　　According to Figure 7 and Figure 8, China’s provincial pollution has also formed two accumulations. One area of heavy pollution centered as Inner Mongolia and Shanxi and surrounded by Ningxia, Gansu and Xinjiang. The other is a low-pollution accumulation centered as Shanghai and surrounded by eastern coastal provinces such as Zhejiang and Jiangsu. Central provinces such as Jiangxi, Hunan, Hubei and Anhui are also located in this low-pollution accumulation. In addition, Henan has consistently remained in Quadrant LH, though its significance level is decreasing and it is approaching Quadrant LL. Meanwhile, Shanxi has relocated from Quadrant HH to Quadrant LH. The current trends show that China’s low-pollution cluster is gradually moving towards northern of China.
　　From the spatial distribution and clustering results we can conclude that FDI as well as environmental pollution in our provinces exists an obvious spatial autocorrelation, both of them have remarkable characteristics of path dependence and form different accumulation areas. Currently, the accumulations of high-level FDI correspond to lower-level environmental pollution, while the accumulations of low-level FDI are associated with high-level environmental pollution. Thus it can be preliminarily concluded that the high-level clustering of FDI benefits China’s regional environmental quality. To prove this argument, the authors then empirically analyze the impact of FDI on environmental pollution by using spatial econometric model.
　　3. Spatial Econometric Test of the Impact of FDI on Environmental Pollution
　　3.1 Model Specification and Empirical Method
　　FDI directly or indirectly affects regional environmental quality through promoting economic growth; therefore, we can test its impact on pollution by incorporating FDI into the Environmental Kuznets Curve. Using for reference to general equilibrium model presented by Antweiler et al(2001),we establish the following econometric model: 　　
　　(1)
　　Here, i and t denote data of province i at t year, p denotes the comprehensive environmental pollution index, GDP is output level, FDI is foreign direct investment, and X denotes other control variables that affect pollution.
　　The environmental quality of one province is affected not only by its own economic growth, but also by the environmental quality of neighboring regions. Industrial patterns, energy consumption structures and public policies have all served to strengthen the spatial association between regional environmental quality and economic growth (Yang 2005; Zhou 2007). Pollution shows strong spatial autocorrelation (Maddison 2006; Zhu 2010) and the higher-level geographical clustering of FDI has strengthened the spatial dependency of pollution, so spatial autocorrelation should be taken into consideration when we analyze on the relationship between FDI and environmental pollution.
　　As for the spatial econometric model, we established a spatial lag model (SLM) and a spatial error model (SEM) to test the environmental impact of FDI.
　　SLM can be expressed as:
　　(2)
　　Wherein
　　The spatial regression coefficient observes the direction and degree of the effect of (the observed value of pollution in neighboring provinces) on P (the observed value of pollution in the host province); W is the adjacent weight matrix of size ; denotes the dependent variable of spatial lag, reflecting the degree of the effect of spatial distance on provincial pollution; is the vector for the random error item.
　　SEM can be expressed as:
　　(3)
　　Wherein ,
　　In the above equation, is the spatial error coefficient, measuring the degree and direction of a neighboring region’s pollution on the observed value P in the host province. As opposed to SLM, spatial dependency in SEM exists in the error item, measuring the degree of the dependent variable’s error shock on the host province’s observed value. is the random error vector of normal distribution.
　　Since SLM and SEM calculate spatial correlation within a whole region, endogeneity problems may exist within the spatial regression model. If we use ordinary least squares (OLS) with the above model, then coefficient estimation may be biased or null (Anselin 1088; Wu 2007; Zhong 2010). Anselin (1988) suggests applying the maximum likelihood method (ML) to estimate factors for SEM and SLM. Borrowing from the above scholars, we adopt the ML method to estimate our spatial regression model. This method overcomes the variable endogeneity problem that exists in traditional OLS and also effectively overcomes the estimation error generated by endogeneity (Anselin 1988; Blonigen et al. 2007). At the same time, this method scientifically reflects the degree of spatial dependency for pollution among different provinces, precisely measuring the effect of a neighboring province’s environmental quality on the host province. 　　3.2 Variable Selection and Data Sources
　　For this paper, we have selected thirty provinces to study between 2000 and 2009, with original data coming mainly from the China Compendium of Statistics and the China Statistical Yearbook for relevant years. For FDI sources, The statistical data of FDI’s main sources are concentrated on the following three areas:1) Offshore financial centers (OFCs) including Hong Kong, the Virgin Islands, the Cayman Islands, Samoa and Mauritius; 2) East Asian developed countries including South Korea, Japan and Singapore; 3) Western developed countries, including the US, the UK, Germany and France. Relevant variable indexing is as follows:
　　Pollution index (P): six types of indicators – industrial wastewater emissions, gas emissions, sulfur dioxide emissions, smoke, dust, and solid waste – are studied and analyzed with entropy methodology in order to calculate a comprehensive environmental pollution index.
　　Output level (GDP): Environmental issues are always somehow associated with economic growth (Grossman and Krueger 1991). With regards to different countries, regions and pollutants, the relationship between them can be represented by an uprising, inverted U-shape or a cubic shape (Shafik and Bandyopadhyay 1992; Fridel and Getzner 2002). In this paper, we use both quadratic and cube forms to describe the impact of economic growth to environmental pollution, using the GDP deflator to avoid the price effect.
　　Foreign direct investment (FDI): In total sample surveys, this paper has taken actually utilized foreign capital to measure the level of foreign investment. However, due to data availability and consistent,, this paper has used contracted foreign capital to measure the level of FDI from three main sources. Besides, a couple of provincial data are absent in individual years, we use the estimated method adopted by Yao (2007) and Wang (2007)as a reference to compensate for data deficiencies. The above statistics were converted into renminbi (RMB) by using the annual average exchange rate of the RMB to the American dollar (USD), and the price effect was eliminated by using the GDP deflator for different provinces and years.
　　To minimize estimation bias due to omitted variables, we added another control variable X that affects pollution. These variables include:
　　1) Environmental protection awareness (invest), measured by input to pollution treatment and also applied the GDP deflator.
　　2) Industrial structure (S), measured by the output value of secondary industry respective to local GDP. 　　3) Technical progress (K/L), measured by the ratio of capital to labor. The amount of capital inventory was estimated by the perpetual inventory method used by Zhang (2004), the depreciation rate was set at 9.6 percent, and the price factor was eliminated by using the GDP deflator.
　　For the statistical description, refer to Table 3. Regression analysis was done with Geoda and Matlab software.
　　3.3 Spatial Econometric Test of the Impact of FDI on Environmental Pollution: Empirical Analysis Based on Overall Samples
　　Before carrying out regression analysis, it needs to be verified whether or not there is spatial autocorrelation between provincial pollution and its influencing factors. To guarantee the robustness and effectiveness of results, we have carried out autocorrelation tests using five methods: Moran’s, Walds, Lratios, Lmsar, and Lmerr. According to verification results, all tests reject the null hypothesis at 1 percent significance, suggesting that significant spatial autocorrelation exists between China’s provincial pollution and its influencing factors. The Hausman test shows that fixed effects are stronger than random effects, and, in some special cases, the fixed effect model is a better choice (He 2006). In accordance with different restriction to spatial effects and time effects, the fixed effects can be classified into four categories, namely non-fixed effect (nonF), the spatial fixed effect (sF), the time fixed effect (tF), and the spatial and time fixed effect (stF). According to the model judgment criteria used by Anselin et al. (1996), we find that Robust Lmerr (234.4311) is significant at a 1 percent, while Robust Lmsar (0.0205) fails a 10 percent significance test. Log Likelihood and Adjusted R2 Value for the SEM spatial fixed effect model are obviously larger than in other models, and therefore this model is a better choice.These findings suggest that overt spatial characteristics exist in China’s provincial pollution. Changes in pollution levels mainly originate from differences in a cross-section of individuals, and pollution in one region is not only subject to pollution in neighboring provinces, but also the error shock of structural differences among different regions. These structural differences are reflected in economic growth levels, FDI, levels of environmental protection awareness, industrial structures, and technical progress.
　　According to Table 4, the estimated value of spatial error coefficient is positive, suggesting that significant spatial dependency exists in provincial pollution. This means that poor environmental quality in one province is connected with similar quality in neighboring provinces. In this case, negative industrial and environmental policies such as the relocation of pollution industries will become priorities for local governments (Yang 2008). 　　The estimated coefficient of FDI is significantly negative, suggesting that FDI has, to some extent, improved China’s environmental quality. This implies that, generally speaking, the “pollution haven hypothesis” is not valid in China. On the one hand, FDI tends to use relatively advanced technology with superior pollution discharge systems (Wang and Jin 2007; Huang 2010). Among the enterprises that have received ISO14001 Environmental Management System Certification, two-thirds are foreign-invested. Among those firms that have obtained a Certification for Environment Mark, half are foreign-invested. Therefore, FDI has contributed to better environmental protection technology and equipment. On the other hand, clean FDI inflows have promoted industrial upgrading, structural upgrading, resource allocation optimization, and utilization efficiency. All of these factors have further lowered per-unit output resource consumption and pollution discharge (Zhang 2009; Xu 2009).
　　The authors have also conducted further study of the impacts of structural error shock on pollution. Estimates show that the regression coefficient of output levels passed hypothesis testing at 1 percent significance, with the estimated values of a1, a2 and a3 being positive, negative and negative, respectively. This describes an N-curve relationship between output and pollution. In this situation, there would be a certain level of environmental exacerbation that increased with output levels until a certain point at which the environmental situation would start to improve. That would continue until another point, after which the environmental situation would be further exacerbated. The two turning points here are seen to be at 64.74 billion RMB for the first and 750.65 billion RMB for the second. The latter turning point indicates the start of a platform period during which environmental pollution is not sharply changed with an increase in output level. This plateau can be exceeded, however, with pollution rising sharply again after a certain high level of output.
　　The estimated coefficient of environmental protection awareness is positive, suggesting that higher inputs in pollution control actually worsened pollution. Possible explanations for this are: 1) Compared with sustained GDP, input in pollution treatment is woefully inadequate. Input in pollution protection accounts for less than 0.03 percent of GDP in some provinces and at most 0.83 percent. These meager inputs are simply not sufficient to cur worsening environmental pollution.2) Inputs in environmental protection lack sustainability, and speculation also plays a part in pollution treatment. 3) Government inputs into pollution control mainly target enterprise pollutants, and these kinds of inputs are ineffective for enterprises to adopt cleaner technology (Li and Shen 2008). 　　The regression coefficients of industrial structure and technological progress fail to pass 10 percent significance tests. Industrial structure has a negative impact on pollution, meaning that a higher ratio of secondary industry in local GDP is associated with less pollution. This is because secondary industrial restructuring involves a shift away from extensive production towards intensive production, or from highly-polluting products to cleaner ones. The estimated coefficient of technological progress is positive, suggesting that, despite growth in stock of capital per capita, expenditures that are directed towards technological R&D for eliminating pollution are still limited. Growth in capital stock per capita, in other words, does not directly lead to corresponding clean technology.
　　3.4 Spatial Econometric Test on the Impact of FDI on Environmental Pollution: Empirical Analysis Based on main Sources
　　To further test the pollution haven hypothesis, this paper has tested the relationship between pollution and FDI coming from three different sources: OFCs, East Asian developed countries and western developed countries.
　　Looking at Table 5 and Table 6, it can be seen that differently sourced FDI tends to have different impacts on pollution. In general, regression coefficients in the three regions are all negative, but only FDI from OFCs passes a 5 percent significance test. The explanation may be that funds from the British Virgin Islands, Samoa and the Cayman Islands actually originate in China. Most of this kind of FDI can be described as hot money, and a significant chunk of this funding belongs to reflux of domestic funs (China Council for the Promotion of International Trade, 2007). Chinese scholars agree that one-third of actually utilized foreign capital comes from the return investment of domestic funds through offshore financial centers (Xiao 2004). Therefore and to some extent, a large portion of foreign capital in OFCs is the “fake foreign capital” formed by domestic return investment. Its contribution to pollution alleviation is the result of strict regulations, clean technologies and lowered energy consumption by domestic enterprises. Besides, transnational corporations invested in local enterprises with funds from OFCs are the main carriers of international funds. This situation demonstrates clear returns to scale, and this effect will raise local enterprise efficiency and decrease per-unit output energy consumption and pollution discharge (Copeland and Taylor 2004). Meanwhile, funds from OFCs also create favorable conditions for transnational corporations to carry out strategic mergers and acquisitions, which lead to more advanced technologies being brought to bear on local enterprises. This in turn will increase efficiency further (Bresman, Birkinshaw and Nobel 1999; Hagedoorn and Duysters 2002) and generate positive environmental benefits. 　　The regression coefficient of foreign capital from East Asian developed countries and western developed countries is negative but fails to pass a 10 percent significance test, suggesting that foreign capital from the above sources tends to improve regional environmental quality. The international relocation of pollution-intensive industries via FDI therefore does not seem to be prevalent in China. Many empirical tests show that the environmental performance of foreign enterprises is closely associated with an enterprise’s scale and capital sources (Ge and Zhang 2006; Xu 2007). Investment from East Asian developed countries mainly goes to SMEs, and the main purpose of such FDI is usually to find cheap labor and resources and to expand market share. This kind of investment usually flows to labor-intensive industries, and the associated level of technology tends to be very low (Zhang 2010). These kinds of enterprises are generally not motivated to engage in technological upgrading or even to adhere to environmental protection standards. In regions with defective regulations, FDI has been lured by the prospect of cheaper manufacturing. Cost cutting is the primary motivation here, and as such the technology that is used in these regions tends to be outdated (Andreoni and Levion 2001; Luke and Stares 2005; Zhang and Guo 2009). Therefore, the amount of clean, new technology that has been introduced via FDI from developed East Asian countries has been relatively low. Additionally, East Asian developed countries mainly use China as a production base, and most of the manufactured products are for export or selling back to the originator country. In this situation, the lion’s share of R&D and technical innovation occur at the headquarters of the parent company where technology security can be assured and Chinese imitation or innovation can be discouraged. The technical spillover effects brought by FDI are therefore very limited in this case, and there is little resultant contribution to regional environmental quality.
　　FDI from western developed countries, however, is keener to enter the Chinese marketplace and build significant international production relationships (Wang et al. 2007; Buckley et al. 2002). As they work to build a global production network, these foreign corporations tend to build R&D and marketing centers in China and adopt optimal environmental standards in order to achieve economies of scale across their transnational businesses (Kogut 1985; Hansen 1999). In this way, FDI originating from western developed economies tends to reflect higher levels of environmental consciousness. Regions that absorb this beneficial FDI are able to absorb a limited amount of this environmentally conscious technology via spillover effects (Chudnovsky and Pupato 2005; Albornoz et al. 2009). Confined by China’s current levels of economic development, basic infrastructure and human capital, local enterprises are incapable of providing capital- and technology-intensive intermediate goods, which in turn impedes the potential localization of western enterprises and the resultant technology spillover effects (He 2000; Lai 2005). This limits the environmental benefits. 　　The N-curve relationship between output and pollution is statistically significant. The regression coefficient of industrial structure remains negative and passes a 10 percent significance test, and the estimated value of spatial error coefficient remains at 0.70 and passes a 1 percent significance test. The spatial dependency of pollution remains very high. In general, output level, FDI source and industrial structure all have significant and varying impacts on pollution, while environment awareness and technological progress have insignificant effects on environmental pollution .
　　4. Conclusions and Policy Implications
　　After analyzing the distribution pattern and spatial dynamic transition of China’s provincial FDI and pollution from 2000 to 2009 and conducting further empirical analysis on the impact of FDI on China’s pollution using SEM and SLM, the authors have obtained the following conclusions and policy implications:
　　(1) FDI as well as environmental pollution in our provinces exists an obvious spatial autocorrelation, both of them have remarkable characteristics of path dependence and form different accumulation areas. Currently, the accumulations of high-level FDI correspond to low-level environmental pollution, while the accumulations of low-level FDI are associated with high-level environmental pollution. Thus it can be seen that regional ability to attract foreign capital as well as environmental pollution is closely related to geographical location and its neighbors. Under these circumstances, strengthening transregional environmental protection cooperation and adopting a “rich neighbor” policy should be the top policy choices for regional economic growth. For one, wealthier provinces should strengthen their cooperation with poorer neighboring regions. This cooperation should prove mutually beneficial. Secondly, the regional governments should break administrative monopoly and promote transregional environmental cooperation with emission trading as a core.A reasonable compensation mechanism can be established for this cooperation that can actively respond to transregional pollution.
　　(2) China’s provincial pollution is not only influenced by the pollution of neighboring provinces, but also by the error shock of structural differences among different regions. These structural differences are reflected in economic growth levels, FDI, levels of environmental protection awareness, industrial structures, and echnical progress. Therefore, local governments should engage in a comprehensive analysis of current economic levels, taking into consideration impacts from neighboring provinces. In this way, local governments can learn strategies for investment and environmental protection from more developed regions. 　　(3) China’s environment has benefited from FDI, as FDI tends to bring with it relatively advanced technology and emissions standards. FDI promotes industrial structural optimization, reducing per-unit resource consumption and pollution discharge. Overall, Pollution Haven Hypothesis is invalid in China. Therefore, China should continue to encourage the introduction of FDI. The regional governments should try to attract relatively high-quality foreign capital, transitioning their local economies from low-end processing to high-end design manufacturing.
　　(4) FDI impacts regional environmental pollution differently depending on the source of the FDI. FDI from OFCs has significantly lowered pollution levels in China, and FDI from developed countries in East Asia and the West has brought some positive contributions to the environment. So local government should thoroughly change their unilateral pursuit the flat-out achievement view and strengthen the environmental monitoring intensity of the existing foreign-funds enterprises, further intensify guide efforts to attract cleaner FDI from western developed countries so as to achieve win-win scenarios of economic growth and environmental protection.
　　References
　　[1] Albornoz, F. Cole, M.A., Robert, J.R., and Elliott, M. 2009. “In Search of Environmental Spillover.” the Word Economy 10:136-163.
　　[2] Andreoni, J. and Levinson, A. 2001. “The Simple Analytics of the Environmental Kuznets Curve.” Journal of Public Economics 80(2):269-286.
　　[3] Anselin, L. 1988. Spatial Econometrics: Methods and Models, Dordrecht. Dordrecht: Kluwer Academic Publishers.
　　[4] Anselin, L., Bera, A. K, and Florax, R.1996. “Simple Diagnostic Tests for Spatial Dependence.” Regional Science and Urban Economics 26(1):77-104.
　　[5] Antweiler, Werner, Copeland, B., and Taylor M. 2001. “Is Free Trade Good For the Environment?” American Economic Review 91(4):877-908.
　　[6] Bao, Qun, Yuanyuan Chen, and Ligang Song. 2010. “Foreign Direct Investment and Environmental Pollution in Host Country: Being an Inverted U-shaped Curve?” World Economy (1):3-17.
　　[7] Blonigen, B.A., Ronald. B. D., and Naughton. H. 2007. “FDI in Space: Spatial Autoregressive Relationship in Foreign Direct Investment.” European Economic Review 51(5):1303-1325.
　　[8] Buckley, P.J., Clegg. J., and C.Q. Wang. 2002. “The Impact of Inward FDI on the Performance of Chinese Manufactory Firms.” Journal of International Business Studies 33(4):637-655. 　　[9] Chen, Yuanyuan, and Kunwang Li. 2010. “The Influencing Factors of Emission Intensity of Industrial Pollutants under Open Economy.” Journal of Shanghai University of Finance and Economics 12(4):90-97.
　　[10] Chudnovsky, D., and Pupato, G. 2005. “Environmental Management and Innovation in the Argentine Industry: Determinants and Policy Implications.” International Institute for Sustainable Development, Manitoba, Canada.
　　[11] Cletus. C. C., and Eran, S. 2000. “Foreign Direct Investment in China: a Spatial Econometric Study.” World Economy 23(1):1-23.
　　[12] Copeland, B., and Taylor, M. 2004. “Trade, Growth and Environment.” Journal of Economic Literature 42(1):7-18.
　　[13] Chudnovsky, Daniel, Andrés López.1999. “TNCs and the Diffusion of Environmentally Friendly Technologies to Developing Countries.” Copenhagen Business School Cross Border Environmental Management Project, No.9, Geneva.
　　[14] Maddison, David. 2006. “Environmental Kuznets Curves: a Spatial Econometric Approach.” Journal of Environmental Economics and Management 51(2):218-230.
　　[15] Dean, J.M. 2000. “Does Trade Liberalization Harm the Environment? A New Test.” The Canadian Journal of Economics 35(4):819-842.
　　[16] Dua, A., and Daniel C.E. 1997. Sustaining the Asia Pacific Miracle: Environmental Protection and Economic Integration. Washington, D.C.: Institute for International Economics.
　　[17] Feng, Helen Liang. 2005. “Does Foreign Direct Investment Harm the Host Country’s Environment? Evidence from China.” Academy of Management, Honolulu.
　　[18] Fridel, B., and Getzner, M. 2002. “Environment and Growth in a Small Open Economy: An EKC Case-study for Austrian CO2 Emissions.” Discussion Paper of the College of Business Administration University of Klagenfurt, Austria.
　　[19] Ge, Aijing, and Shiqiu Zhang. 2006. “Assessment and Analysis of Environmental Management Status and the Driving Forces of Transnational Corporations’ Investment.” China Environmental Science 26(1):106-110.
　　[20] Grossman, G., and Krueger, A. 1991. “Environmental Impacts of a North American Free Trade Agreement.” NBER Working Paper No. 3914, National Bureau of Economics Research, Cambridge, MA.
　　[21] Hagedoorn, J. and Duysters,G. 2002. “The Effect of Mergers and Acquisitions on the Technological Performance of Companies in a High-tech Environment.” Technology Analysis and Strategic Management 14(1):67-85
　　[22] Hansen, M.W. 1999. “Cross Border Environmental Management in Transnational Corporations: An Analytical Framework.” UNCTAD Occasional Paper No.5, Geneva. 　　[23] He, Jiang, and Xinzhi Zhang. 2006. “Chinese Regional Economic Growth and Convergence: Spatial Panel Data Analysis.” South China Journal of Economics (5):44-51.
　　[24] He, Jie. 2000. “The Further Analysis on the Spillover Effects of FDI to Chinese industrial sector.” World Economy (12):5-11.
　　[25] Bresman, Henrik, Julian, Birkinshaw, and Robert Nobel. 1999. “Knowledge Transfer in International Acquisitions.” Journal of International Business Studies 30:439-462.
　　[26] Huang, Jin. 2010. “Empirical Investigation on Foreign Direct Investment and Environmental Pollution: A Simultaneous Equations Approach.” World Economy Study (2):80-85.
　　[27] Javorcik, Beata Smarzynska, and Shangjin Wei. 2001. “Pollution Havens and Foreign Direct Investment: Dirty Secret or Popular Myth? World Bank Policy Research Working Paper No.2673.
　　[28] Jie, He. 2006. “Pollution Haven Hypothesis and Environmental Impacts of Foreign Direct Investment: The Case of Industrial Emission of Sulfur Dioxide in Chinese Provinces.” Ecological Economics 60:228-245.
　　[29] Keller, W., and Levinson A. 2002. “Pollution Abatement Costs and Foreign Direct Investment Inflow to the U.S.” Review of Economics and Statistics 84(4):691-703.
　　[30] Kogut, B.1985. “Designing Global Strategies: Profiting From Operational Flexibility.” Sloan Management Review 27(1):27-38.
　　[31] Lai, Mingyong, et al. 2005. “Foreign Direct Investment and Technology Spillover: A Theoretical Explanation of Absorptive Capability.” Economic Research Journal (8):95-104.
　　[32] Letchumanan, R., and Kodama, F. 2000. “Reconciling the Conflict Between the Pollution-haven Hypothesis and an Emerging Trajectory of International Technology Transfer.” Research Policy 29(1):59-79.
　　[33] Li, Yongyou, and Kunrong Shen.2008. “The Effects of Emission Reduction in Pollution Control-Empirical Analysis on Provincial Data of Industry Pollution.” Management World (7):7-17.
　　[34] List, J.A., and Catherine Y. Co. 2000. “The Effects of Environmental Regulation on Foreign Direct Investment.” Journal of Environmental Economics and Management 40(1):1-20.
　　[35] Liu, Yulin, and Huaide Wen. 2007. “FDI, Human Resources and Environmental Pollution Loss in Chinese Economic Growth.” World Economy Study (11):48-55.
　　[36] Luke, R., and Stares,R. 2005. “Small Business Responsibility in Developing Countries, a Threat or an Opportunity.” Business Strategy and the Environment 67(3):38-53.
　　[37] Zarsky, Lyuba. 1999. “Havens, Halos and Spaghetti: Untangling the Evidence about the Relationship Between Foreign Direct Investment and the Environment.” OECD Environment Directorate. Hague, the Netherlands. 　　[38] Ma, Jianqin, Jinjin Guo, and Xiaojie Liu. 2010. “Water Quality Evaluation Model Based on Principal Component Analysis and Information Entropy: Application in Jinshui River.” Journal of Resources and Ecology, 1:248-251.
　　[39] Markusen, J.R. 1999. “Foreign Direct Investment as a Catalyst for Industrial Development.” European Economic Review 42(2):335-356.
　　[40] Ministry of Economic Communication in China Council for the Promotion of International Trade. 2007. The Investigation of Investment Environment in British Virgin Islands, Cayman Islands and Samoa Islands. Beijing: China Social Sciences Press.
　　[41] Panayotou, Angelos. 2000. “Globalization and Environment.” CID Working Paper No.53, Center for International Development at Harvard University.
　　[42] Poon, J. Casas, I., and He, C. 2006. “The Impact of Energy, Transport and Trade on Air Pollution in China.” Eurasian Geography and Economics 47:568-584.
　　[43] Rey, S. J. 2001. “Spatial Empirics for Economic Growth and Convergence.” Geographical Analysis 33(3):195-214.
　　[44] Shafik, N., and Bandyopadhyay.S. 1992. “Economic Growth and Environmental Quality: Time Series and Cross-country Evidence.” World Bank Policy Research Working Paper, World Bank Policy Research, Washington, D.C.
　　[45] Su, Zhifang, and Ridong Hu. 2008. “Dynamic Evolution of the Regional Determinants of FDI in China and its Spillover-Empiric Study Based on Spatial Panel Data.” Economic Geography 28(1):16-20.
　　[46] Wang, C., Y. Wei, andX. Liu. 2007. “Does China Rival its Neighboring Economies for Inward FDI?” Transnational Corporation 16(3):35-60.
　　[47] Wang, Hua, and Yanhong Jin. 2007. “Industrial Ownership and Environmental Performance: Evidence from China.” Environmental and Resource Economics 36:255-273.
　　[48] Wang, Jian. 2004. “The Decisive Factor of FDI Regional Distribution-Based on Empirical Analysis of Spatial Econometrics.” Economic Science (5):116-125.
　　[49] Wang, Liping, Jinian Peng, and Zhi’an Ren. 2006. “The Empirically Analysis of Spatial Econometrics to Local Factors of Chinese FDI Regional Distribution and to the Extent to Geographical Spillovers.” Economic Geography (2):32-43.
　　[50] Wang, Liwen. 2007. “The Comparison Analysis of Foreign Direct Investment from Different Countries and Districts.” Journal of Finance and Economics 33(6):29-42.
　　[51] Wayne, B.G., Ronald, J., and Shadbegian. 2004. “Optimal Pollution Abatement-Whose Benefits Matter, and How Much?” Journal of Environmental Economics and Management 47(3):510-534. 　　[52] Wu, Yuming. 2007. “A Panel Data Study on Correlation Mechanism between FDI and Environmental Regulations.” Economic Geography 27(1):11-14.
　　[53] Xia, Youfu.1999. “Analysis on the Status and Consequence of Pollution-intensive Industry Caused by Foreign Investors.” Management World (5):109-121.
　　[54] Xian, Guoming, and Cheng Zhang. 2001. “Environmental Management of European TNCs: Practices and the Impact in China.” Nankai Business Review (5):50-55.
　　[55] Xiao, Geng. 2004. “People’s Republic of China’s Round-Tripping FDI: Scale, Causes and Implications.” Asian Development Bank discussion paper No.7, Asian Development Bank Institute, Tokyo.
　　[56] Xu, He, Haiying Chen, and Zhuoling Liao.2007. “Research of Foreign Direct Investment and Environmental Safety in China.” Journal of China University of Geosciences (Social Sciences Edition) 7(4):21-26.
　　[57] Xu, Shichun, and Yingying Zhuang. 2009. “The Empirical Analysis on the Effects of Openness to Environment-Jiangsu Province as an Example.” Finance and Trade Economics (3):107-112.
　　[58] Yang, Haisheng, Chen Shaoling, and Yongzhang Zhou. 2008. “Local Government Competition and Environmental Policy-Empirical Evidence from Province’s Governments in China.” South China Journal of Economics (6):15-30.
　　[59] Yang, Haisheng, et al. 2005. “Impact on EKC by Trade and FDI in China.” China Population, Resources and Environment 15(3):99-103.
　　[60] Yao, Zhanqi. 2007. “The Incentive and Behaviors of Local Multinational Corporations from Different Countries.” Finance and Trade Economics (5):18-25.
　　[61] Ying Ruiyao, and Li Zhou. 2006. “FDI, Industrial Pollution and Environmental Regulation: Based on Chinese Data.” Finance and Trade Economics (1):76-81.
　　[62] Zhang, Jifeng. 2010. “Post-crisis New Opportunities and Perspectives for Japanese Investment to China.” Contemporary Economy of Japan (2):38-44.
　　[63] Zhang, Jun, Guiying Wu, and Jipeng Zhang. 2004. “The Estimation of China’s Provincial Capital Stock: 1952-2000.” Economic Research Journal (1):35-44.
　　[64] Zhang, Xian, and Yong Zhou. 2007. “Spatial Effects Analysis of FDI on Energy Intensity in China.” Journal of Quantitative and Technical Economics 24(1):101-108.
　　[65] Zhang, Yanbo, and Yajun Guo.2009. “Effects of FDI on Environment and Environmental Protection Policy in Using FDI in China.” China Population, Resources and Environment 19(2):7-12.
　　[66] Zhao, Xikang. 2003. Environmental Pollution and International Competitiveness of Industry-Theoretical and Empirical Analysis. Beijing: China Social Sciences Press.
　　[67] Zhong, Changbiao. 2010. “Empirical Evidence on the Regional Spillover Effects of FDI in China.” Economic Research Journal (1):79-89.
　　[68] Zhou, Li, and Ruiyao Ying. 2009. “Foreign Direct Investment and Industrial Pollution.” China Population, Resources and Environment 19(2):42-49.
　　[69] Zhu, Pinghui, Jiajun Yuan, and Wuyi Zeng. 2010. “Analysis of Chinese Industry Environmental Kuznets Curve-Empirical Study Based on Spatial Panel Model.” China Industrial Economics (6):65-73.

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