regional difference in spatial effects: a theoretical and...

Research ArticleRegional Difference in Spatial Effects A Theoretical andEmpirical Study on the Environmental Effects of FDI andCorruption in China

Dengli Tang 1 Shijie Li 2 Yuanhua Yang3 and Lianglie Gu4

1School of Business Administration Guangdong University of Finance and Economics Guangzhou 510320 China2School of Economics Hainan University Haikou 570228 China3School of Public Management Guangdong University of Finance and Economics Guangzhou 510320 China4School of Humanities and Communication Guangdong University of Finance and Economics Guangzhou 510320 China

Correspondence should be addressed to Shijie Li lantingfygmailcom

Received 14 September 2019 Revised 16 December 2019 Accepted 24 December 2019 Published 1 February 2020

Academic Editor Giancarlo Consolo

Copyright copy 2020 Dengli Tang et al is is an open access article distributed under the Creative Commons Attribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Environmental pollution has aroused extensive concern worldwide in recent years Existing studies on the relationship betweenforeign direct investment (FDI) and environmental pollution have however paid little attention to spatial effects and regionalcorruptionrsquos environmental performance from a spatial perspective To address this gap we investigate the spatial agglomerationeffects of environmental pollution in China and the environmental effects of FDI and regional corruption using spatialeconometric analysis methode results indicate significant spatial agglomeration effects in environmental pollutione resultsof spatial panel data models reveal that the estimation coefficient of FDI is significantly negative and FDI inflows reduce Chinarsquosenvironmental pollution Regional corruption is shown to increase environmental pollution thereby contributing further toenvironmental degradation e interaction coefficient of FDI and regional corruption is significantly positive indicating thatregional corruption reduces the environmental benefits derived from FDI In addition regional differences in spatial effects verifythat regional corruption also reduces the environmental performance of FDI in the central regionMeanwhile regional corruptionincreases the environmental aggravation effects of FDI in the eastern region but weakens it in the western region Our findings leadto some policy recommendations with regard to environmental protection and pollution control

1 Introduction

With the rapid development of economy in China envi-ronmental pollution has become more and more fierce Fordeveloping countries the inflow of foreign direct investment(FDI) is not only an effective channel for domestic capitalaccumulation but also an important way to obtain tech-nology spillovers China is a developing country that attractsthe most foreign capital and the inflow of foreign capital willinevitably affect the domestic environmental qualityerefore will FDI enter domestic pollution-intensive in-dustries and become pollution heaven Do foreign com-panies bring more advanced environmental technologiesHow to effectively coordinate the relationship of the

economic and environmental effects of FDI ese issuesdeserve further discussion

In fact there is a large amount of literature on theenvironmental effects of FDI but previous studies has beenplagued by contradictory and ambiguous empirical resultsresulting in three diverging perspectives One popular viewis the Pollution Haven Hypothesis which many scholarsconfirmed using theoretical and empirical analyses [1ndash8]e second perspective is the Pollution Halo Hypothesiswhich states that FDI can be beneficial in improving en-vironmental conditions [9ndash15] FDI can improve regionalenvironmental quality by raising income levels limiting theevolution of pollution haven as being temporary phenom-enon e third view is the comprehensive environmental

HindawiDiscrete Dynamics in Nature and SocietyVolume 2020 Article ID 8654817 12 pageshttpsdoiorg10115520208654817

effect theory which states that FDI can influence environ-mental pollution through multiple channels including scaleeffect structure effect technology effect and environmentalregulation effect [16ndash19]e environmental effects of FDI isa combination resulting from multiple channels e con-tradictory conclusions may be due to differences in methodsvariable selection and sample intervals and it may be relatedto the Chinarsquos unique national conditions

In recent years with the continuous improvement ofChinarsquos anticorruption efforts many government officialsafter the 19th National Congress of the Communist Party ofChina (CPC) are fired including Lu Wei Liu Qiang ZhangYang and Sun Zhengcai etc Meanwhile transnationalbribery incidents in China have been on the rise in the pastten years and 64 of which are related to foreign trade andFDI e purposes of bribery are mainly to obtain businessopportunities and evade environmental supervision whichmean that the threshold for foreign companies will belowered implicitly and then lead to the deterioration ofenvironmental quality erefore when examining the en-vironmental effects of FDI the impact of institutional defectson FDI behavior characteristics cannot be ignored How-ever previous literature on the environmental effects of FDIfrom the institutional level is scarce So a theoretical andempirical study on the environmental effects of FDI con-sidering in the case of government corruption has greatsignificance for China

is article investigates the spatial agglomeration effectsof environmental pollution in China and the environmentaleffects of FDI and regional corruption from a spatial per-spective Specifically this paper analyzes and constructs atheoretical analysis model on the influence paths of FDI andregional corruption on environmental pollution en weinvestigates the spatial agglomeration effects of environ-mental pollution using relevant statistical data from Chinarsquosprovinces municipalities and autonomous regions (ex-cluding Hong Kong Macao Taiwan and Tibet) Further-more using spatial econometric analysis method theenvironmental effects of FDI and regional corruption basedon the national level and regional differences are exploredFinally this paper provides relevant policy recommenda-tions toward environmental protection is paper is con-structed as follows Section 2 introduces the literature reviewon FDI regional corruption and environmental pollutionSection 3 analyzes the theoretical paths of the effects of FDIand regional corruption on environmental pollution Sec-tion 4 introduces the data variables and methods Section 5presents the empirical results Section 6 presents the con-clusions and puts forward related policy implications

2 Literature Review

Corruption is an important influencing factor of foreigncapital inflow and technological progress [20]erefore theissues about the impacts of corruption on technologicalspillover effects and environmental effects of FDI are also thefocus of academic research but related research is limitedBased on a review of existing research research on theimpact of regional corruption on FDI focuses on the

following two aspects one is the discussion of the impactdirection of regional corruption on FDI that is whethercorruption favors or hinders FDI For example Habib andZurawicki Han and Xue and Amarandei showed that theincreased corruption degree of host country will increase theoperating costs of multinational companies thereby re-ducing the attractiveness of local FDI [21ndash23] HoweverEgger and Winnerrsquos investigation in 73 developing coun-tries Bellos and Subasatrsquos study in 14 transition countriesand Liao and Xiarsquos study in 29 provinces in China showedthat corruption is an effective way to circumvent environ-mental control and institutional deficiencies of host countryand do not hinder FDI inflows but instead promote FDIinflows [24ndash26]

e other one is the discussion of the specific impact ofregional corruption on FDI For example Smarzynska andWei and Xue and Han investigated the impact of corruptiondegree on the ownership structure of FDI and believed thatthe increase of corruption degree in the host country willreduce FDI (especially developed country FDI or market-oriented FDI) [27 28] Also Wei [29] and Wooster andBillings [30] examined the impact of regional corruption onFDI entry models and argued that the rising of corruption inthe host country will reduce the possibility of foreign in-vestment entering the local area with greenfield investmentLi and Liu [20] and Cole et al [31] also examined the impactof regional corruption on the environmental effects of FDIand believed that regional corruption in host countriesinhibits the positive effects of FDI on environment qualitysignificantly for investment destinations Research on theimpact of regional corruption on FDI has been fruitfullyexplored by the existing literature However the influencemechanism of regional corruption on FDI are not beensystematically studied Gorodnichenko et al [32] and Meyerand Sinani [33] also simply discussed the degree of hostcountry corruption as an influencing factor when examiningtechnology spillover effects of FDI in the host countryClassical studies by scholars such as Mauro [34] and Dongand Torgler [35] pointed out that corruption behaviors tendto reduce government spending on science educationculture and health which in turn affect the absorptioncapacity of FDI technology spilloverserefore an in-depthdiscussion about the influence mechanism of regionalcorruption on FDI is conducive to the further improvement-related research in this field

e current literature on the relationship of regionalcorruption and environmental pollution can be divided intothree categories e first category study of corruption andpollution is based on the framework of economic growthcorruption and environment pollution Lopez and Mitraused the Stackelberg model to analyze the influence of re-gional corruption on Environmental Kuznets Curve (EKC)which concluded that regional corruption aggravates envi-ronmental quality [36] ey found that regional corruptiondirectly increases pollution level whereas corruption has anindirect inverse effect by impeding income growth [10]Based on the EKC theory Leitao showed that regionalcorruption increases local environmental pollution [37]Chang and Hao found that when the government has high

2 Discrete Dynamics in Nature and Society

corruption and low quality regional corruption reduces theenvironmental performance of economic growth [38] Inaddition Lisciandra and Migliardo extended the empiricaldebate on the effects of corruption on environmentaldegradation and concluded that corruption decreases theenvironmental quality and that environmental qualityimproves with rising income even at an initial level ofdevelopment [39] e second category study mainly ex-plores the relationship of regional corruption on envi-ronmental policy [40ndash42] Fredriksson and Svenssoninvestigated the relationship of political stability corrup-tion and environmental regulation and showed that re-gional corruption decreases the level of environmentalregulations [43] Biswas and um indicated that cor-ruption means tighter environmental regulations level [44]In addition some literature studied the influence of cor-ruption on environmental pollution from an open econ-omy perspective [45ndash47] Cole et al showed that thenegative influence of FDI on environment depends onregional corruption degree the higher the corruption levelthe greater the negative influence [31]

Most of the existing literature make a meaningful ex-ploration on the environmental effects of FDI and regionalcorruption but it also has some defects Most scholars di-rectly investigate the impacts of FDI and regional corruptionon environmental pollution while a theoretical and em-pirical study on the environmental effects of FDI consideringin the case of government corruption is relatively rareMeanwhile in terms of corruption evaluation indicators andresearch objects most studies mainly use subjective cor-ruption evaluation indicators for international-level inves-tigation and few literatures use regional-level objectivecorruption indicators for research which are easily influ-enced by perception and subjective judgment In view ofthis based on relevant statistical data from Chinarsquos prov-inces municipalities and autonomous regions this paperinvestigates the spatial agglomeration effects of environ-mental pollution and the environmental effects of FDI andregional corruption using spatial econometric analysismethod erefore the innovation points of this papermainly reflect in three aspects first we analyze and build atheoretical analysis model on the influence paths of FDI andregional corruption on environmental pollution whichfurther reveals the complex discipline of FDI and envi-ronmental pollution considering in the case of regionalcorruption second we also analyze the spatial agglomera-tion effects of environmental pollution in China by usingexploratory spatial data analysis technique Furthermore weinvestigate the environmental effects of FDI and regionalcorruption based on the national level and regionaldifferences

3 Theoretical Paths of the Effects of FDI andCorruption on Environmental Pollution

e influence of FDI on environmental pollution is thecombined effects of technique scale structure and envi-ronmental regulation [17] is section mainly analyzes theimpact of FDI on environmental pollution under regional

corruption through the following three mechanisms Firstthe mechanism focuses on the impact of regional corruptionon environmental regulation namely corruption degree inthe host country may affect local environmental policies andthus further affect pollution emissions Cole pointed out thatthe environmental regulation effects of FDI mainly dependon the level of local corruption [48] eoretical analysisshows that when corruption degree is low the impact of FDIon environmental regulation is mainly controlled by welfareeffect and FDI will increase environmental regulationstandard when corruption degree is high the bribery effectbrought by FDI will exceed the welfare effect eventuallyleading to a decline in local environmental regulation andthe theoretical points are also confirmed by other scholarsOverall FDI will rise environmental regulation standard andreduce environmental pollution when the level of corruptionis low Once corruption level reaches a higher level FDI willincrease pollution emissions e influence mechanismmainly reflects in bribing the local governments to relaxtheir environmental regulation standard through the inflowsof FDI and then reduce emissions costs

e second path focuses on the negative impact of re-gional corruption on environmental technology spillovereffects From the perspective of the occurrence of FDI en-vironmental technology spillovers foreign companies worryabout judicial fairness and intellectual property protectionsystem due to corruption problem and then more likely touse a wholly owned enterprise instead of a joint venturewhich decreases the entry of high-tech companies into localarea [27ndash29] In other words when the level of corruption islow FDI tends to use joint venture or cooperation methodswhich is conducive to the spillover effects of environmentaltechnology When the level of corruption is high FDI tendsto use wholly owned methods which is not conducive toenvironmental technology From the perspective of envi-ronmental technology spillovers absorption Li and Liufound that FDI environmental technology spillover effectsexist in significant RampD investment and human capitalthresholds [20] Regional corruption significantly reducesgovernment spending on scientific research education andhealth and hinders the increase of RampD investment andhuman capital [34] erefore with the rise of corruptiondegree the absorptive capacity of environmental technologyspillover effects of FDI will gradually weaken

e third path is that corruption causes excessive de-mand for FDI Some foreign companies do not reach theenvironmental standards and should be excluded Howeverdue to regional corruption problem foreign companies maybribe local government to gain access opportunity In thisway FDI threshold will be implicitly lowered leading tomore low-quality and unclean FDI In other words this isequivalent to the increased demand for low-quality FDI byregional corruption which brings environmental qualityproblems Li and Liu found that with low corruption levelFDI is beneficial in improving environmental quality whilehigh corruption level results in the FDI having a negativeimpact and regional corruption will increase directlyChinarsquos regional pollution emissions [20] Meanwhileprevious studies found that environmental pollution

Discrete Dynamics in Nature and Society 3

exhibits clear spatial correlation with regard to their geo-graphic distribution For exampleWang and Xu pointed outthat PM25 pollution in China is mainly distributed aroundthe Beijing-Tianjin-Hebei region the Pearl River Delta re-gion and the Yangtze River Delta region [49] Liu and Dongalso showed that haze pollution has obvious spatial ag-glomeration characteristics [50]

erefore it is necessary to analyze the influencemechanism of the environmental effects of FDI and re-gional corruption from a spatial perspective SpecificallyFDI and region corruption can affect environmental pol-lution in adjacent regions through economic growth andtechnological progress FDI accelerates economic growthand capital accumulation and advances production tech-nology and management experience through cooperationWhen neighboring regions realize that economic opennesscan promote growth they will try to emulate local con-ditions by reformulating economic policy expanding thedegree of economic openness and actively introducingforeign investment In addition environmental pollution isgreatly influenced by adjacent regions depending on windand relative humidity For example when the wind isstrong enough pollutants can spread to adjacent regions[51] Wind and diffusion cause particulates to migratebetween regions resulting in a spatial spillover effectBased on the above analysis we build a theoreticalframework model on the effects of FDI and regionalcorruption on environmental pollution and it is presentedin Figure 1

4 Materials and Methods

41 Methods

411 Exploratory Spatial Data Analysis Method Spatialweight matrix expresses the variablesrsquo spatial layout betweendifferent regions often denoting spatial contiguity Gener-ally the spatial weight matrix needs to be standardized asexogenous characteristic and its formulate can be expressedas follows

w

w11 middot middot middot w1n

⋮ ⋱ ⋮

wn1 middot middot middot wnn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ (1)

where n denotes the total number of regions and wij rep-resents contiguity relationship between regions i and j In thebinary spatial weight matrix all values in the diagonal are 0If regions i and j have a common vertex or edge then wij iseither equal to 1 or 0

e global spatial autocorrelation analysis refers to theassessment of distribution patterns of environmental pol-lution for the entire research area Local indicators of spatialassociation (LISA) were used to test the local spatial rela-tionship including local Moranrsquos I index and local Gearyindex is paper uses Moranrsquos I index method to verify theglobal spatial autocorrelation and the local Moranrsquos I indexto measure the local spatial autocorrelation and are definedas follows

Moranrsquos I n1113936

ni11113936

nj1wij xi minus x( 1113857 xj minus x1113872 1113873

1113936ni11113936

nj1wij1113936

ni1 xi minus x( 1113857

2

1113936

ni11113936


s21113936ni11113936

nj1wij

Ii xi minus x( 11138571113936

nj1wij xj minus x1113872 1113873

s2

(2)

where xi represents the observed value of region i n is thetotal number of regions wij represents spatial weight matrixIi denotes local Moranrsquos I and s2 denotes deviation

412 Spatial Panel Data Models and Variable Descriptionsis paper utilizes the EKC theory and general equilibriummodel theory of Antweiler et al [9] to construct the basicmeasurement models which is defined as

ln EPit α0 + α1 ln PGDPit + α2ln2PGDPit + α3 ln FDIit

+ α4 ln RCit + α5 ln FDIit lowast ln RCit + α6 lnXit + εit

(3)

e main limitation of the classic pollution model ig-nores spatial correlation of research subject According toAnselin [52] the Spatial Lag Model (SLM) and the SpatialError Model (SEM) can be set based on variablesrsquo spatialcorrelation We established the spatial models by addingspace dimension

ln EPit ρwij ln EPit + α0 + α1 ln PGDPit + α2ln2PGDPit

+ α3 ln FDIit + α4 ln RCit + α5 ln FDIit lowast ln RCit

+ α6 lnXit + εit εit sim N 0 σ2it1113872 1113873



εit cwεit + φit φit sim N 0 σ2it1113872 1113873

(4)

where wij represents spatial weight matrix εit representsindependent random error wij lnPij represents spatial lagvariable ρ represents spatial lag coefficient λ representsspatial error autocorrelation coefficient and φit representsrandom error

Drawing on most research this paper utilizes thecomprehensive index of environmental pollution (EP) tomeasure pollution level based on entropy weights methodincluding industrial wastewater discharge industrial wastegas discharge industrial sulfur dioxide emissions industrialsmoke and dust emissions industrial dust emissions andindustrial solid waste emissions

For the measurement of regional corruption (RC) thereare two main approaches to measure the index of regionalcorruption subjective evaluation indicator (eg corruptionperception index) and proxy value (eg the number ofcorruption cases) Given that the subjective indicator utilizesquestionnaires and personnel evaluation it would be


difficult to obtain research data at the regional level ispaper uses the number of corruption cases as proxy value forregional corruption level

Foreign direct investment (FDI) is measured by theactual utilized foreign capital Industrialized countries tendto transfer pollution-intensive industries into developingcountries for lower labor costs and competitiveness edeveloping countries acquire advanced production tech-nology and management experience through technologyspillover effects Production factors are improved andpollution emissions are reduced which benefit the envi-ronment us incorporating the multiple effects of FDI onenvironment pollution will be critical in the analysis

Gross domestic product per capita (PGDP) is a measureof economic growth level EKC theory points that in theearly stage of economic development there is low demandfor resources and the environment is in good conditionHowever as the economy grows people produce andconsume more resources which puts tremendous pressureon the environment With further development the econ-omy then transitions into cleaner production and energy-saving technologies that greatly reduce pollution iscreates an inverted ldquoUrdquo-type relationship between economyand pollution

In addition the proportion of secondary industry toGDP is used to measure industrial structure (IS) In theoryeconomic development in the early stages of industrializa-tion often requires more resources which leads resourceexploitation and pollution When there is a development ineconomy growth patterns gradually change from extensivegrowth into intensive growth e proportion of secondaryindustries to GDP eventually decreases while the proportionof primary and tertiary industries to GDP gradually in-creases Total investment in pollution control is used tomeasure environmental regulation (ER) level Populationscale (PS) is measured by the number of permanent residentsat the end of the year Table 1 presents descriptive statisticalresults for these variables

42 Data Sources Compared with sectional data panel datahave the advantage of large sample size and can control theerror caused by heteroscedasticity between regions

erefore we make the use of Chinarsquos provincial panel datafrom 2005 to 2015 for this study e data come from theChina Statistical Yearbook China Environmental StatisticsYearbook China Inspection Yearbook and other provincialstatistical yearbook e GeoDa Software is used in thespatial autocorrelation analysis and the Matlab R2018aSoftware is used for estimating spatial panel data models

5 Results and Discussions

51 Spatial Autocorrelation Test Results Table 2 shows theglobal Moranrsquos I values of environmental pollution in Chinae global Moranrsquos I values are all greater than 0 indicatingspatial autocorrelation for environmental pollution andclear path dependence characteristics in their geographicaldistribution Except for 2012 and 2013 all global Moranrsquos Ivalues are positive at the 20 significance level ese resultsshow that spatial factors cannot be ignored and spatialeffects should be introduced into econometric models

Figure 2 presents the Moran scatterplots of environ-mental pollution for 2005 2010 and 2015 It can be seen thatof environmental pollution of most regions are located inquadrant I and quadrant III e results confirm the exis-tence of spatial autocorrelation and spatial agglomerationeffects in environmental pollution Adjoining regions showsimilar agglomeration characteristics areas with highamounts of environmental pollution are shown to be ad-jacent with high pollution areas

Table 1 Variables definition and descriptive statistical results

Variables Unit Min Max Mean Std devlnEP 1 00255 00433 00333 00045

lnFDI 100 millionyuan 52181 150897 124090 16593

lnRC Piece 48363 82093 68642 07433lnPGDP Yuan 312773 1193706 857745 172349ln2PGDP Yuan 85275 115895 102754 06240lnIS 727189 1343169 1059717 127803

lnER 100 millionyuan 27468 49730 36913 02792

lnPS Ten thousandpeople 16677 72557 48222 10011

Environmental pollution inlocal regions

Environmental pollution in

adjacent regions

FDI in local

regions

Corruption in local regions

Technology spillovers

Environmental regulation

Excessivedemand

Scaleeffect

Technique effect

Structural effect

Regulationeffect

FDI inadjacentregions

Corruption in adjacent regions



Excessive demand

Scaleeffect

Technique effect

Structural effect

Regulationeffect

Spatial effect

Figure 1 e effects of FDI and regional corruption on environmental pollution from a spatial perspective


Specifically Henan Hebei Shaanxi Shandong etc arelocated in quadrant I (H-H) showing a spatial distributionof highly polluted areas and a positive spatial autocorrelationwith the other regions Ningxia Gansu HeilongjiangQinghai etc are located in quadrant III (L-L) having lowenvironmental pollution and a negative spatial autocorre-lation with the other regions Quadrants II and IV have anL-H and H-L aggregation patterns respectively whereHainan Fujian Anhui Shanghai etc are located inQuadrant II while Xinjiang Zhejiang etc are located inquadrant IV Meanwhile we also find spatial dynamicevolution of environmental pollution manifesting in threetypes the first type refers to observation regions moving toadjacent quadrants the second type refers to observationregions moving to nonadjacent quadrants and the third typerefers to observation regions that never change and there are18 regions belonging to this type

Figure 3 shows LISA cluster maps of environmentalpollution for 2005 2010 and 2015 As shown by the imagefour spatial agglomeration regions are formed e H-Hagglomeration regions are located in Hebei Heinan Shanxiand Shandong in 2005 and then Hebei and Shanxi exit theH-H agglomeration regions is suggests that environ-mental pollution can be affected by adjoining areas and theH-H agglomeration regions are mainly distributed in northChina e L-H agglomeration regions are concentratedaround Anhui and remain unchanged in three yearse L-Land H-L agglomeration regions are centered in Xinjiang andSichuan in 2005 respectively Although Xinjiang exits the

L-L agglomeration region it is in the the H-L agglomerationregion in 2010 and in 2015

52 Traditional Panel Model Estimation Results Table 3shows the traditional panel model estimation results ofFDI regional corruption and environmental pollution eR2 values for the models (1)ndash(4) are 06702 06710 06719and 06871 indicating moderate goodness of fite F valuesare 1093861 1097802 942177 and 881273 which all passthe 1 significance level test indicating all the linear rela-tionships to be significant e DW values are 1636816353 16226 and 15265 suggesting that the residual termin traditional panel models do not have a sequence corre-lation problem e Model (1) estimation coefficient of FDIis negative is means that FDI is conducive in reducingenvironmental pollution but not significant e estimationcoefficient of regional corruption in Model (2) is positiveindicating that regional corruption leads to increased en-vironmental pollution to a certain degree e estimationcoefficients of FDI and regional corruption in Model (3) arenegative and positive respectively e Model (4) interac-tion coefficient of FDI and regional corruption is positiveand significant which indicates that regional corruptiondiminishes the environmental performance of FDI ismeans that with higher levels of corruption in a region FDIwill increase pollution rates In addition the estimationcoefficients of lnPGDP and ln2PGDP in model (1)ndash(3) arepositive and negative suggesting that economic growth and

EP_2005

Moranrsquos I = 0171823

Lagg

ed E

P_20

05

ndash140

ndash060

100

ndash220020 180100ndash140 ndash060ndash220

020

180

EP_2010


Lagg

ed E

P_20

10

ndash150

ndash060

120


030

210

EP_2015


Lagg

ed E

P_20

15

ndash160

ndash060

140


040

240

Figure 2 Moran scatterplots of environmental pollution in China

Table 2 Moranrsquos I values of environmental pollution in China from 2005 to 2015

Years Moranrsquos I E (I) Sd (I) P values2005 01718 minus 00345 01104 004002006 01575 minus 00345 01134 004002007 01427 minus 00345 01142 007002008 01835 minus 00345 01133 003002009 01153 minus 00345 01180 014002010 01343 minus 00345 01168 009002011 01406 minus 00345 01251 012002012 00745 minus 00345 01228 023002013 00737 minus 00345 01265 025002014 01139 minus 00345 01267 016002015 00928 minus 00345 01279 01800


environmental pollution are nonlinearly related ese var-iables have a reverse ldquoUrdquo-type relationship supporting theEKC hypothesis is means that environmental pollutiontends to increase in the early stages of economic developmentslows down until reaching a turning point and then begin tosubside with further economic growth Other estimationcoefficients including industrial structure environmentalregulation and population scale are all positive

53 SpatialPanelModelEstimationResults We first examinewhether spatial autocorrelation exists for environmentalpollution Table 4 shows the spatial autocorrelation testresults of regional corruption FDI and environmentalpollution e Moranrsquos I index values of Models (1)ndash(4) are

00991 01031 01029 and 01201 respectively which allpass the 1 significance level test indicating that significantspatial autocorrelation exists for environmental pollutione LM-Lag values of Models (2)ndash(4) are 44051 62127 and71937 while the LM-Error values are 66523 66192 and90204 respectively All values pass the 5 significance testand all LM-Error values are greater than their LM-Lagvalues In addition Robust LM-Error values of model (2)model (3) and model (4) are also more significant thanRobust LM-Lag values erefore the spatial error model ismore suitable for explaining the environmental effects ofFDI and regional corruption for models (2)ndash(4) Howeverthe spatial lag model is more suitable for model (1) accordingto the LM-Lag values and LM-Error values

Table 3 Traditional panel model estimation results of FDI regional corruption and environmental pollution

Variables Model 1 Model 2 Model 3 Model 4

Constant minus 00117 00058 minus 00007 00727lowast(minus 03281) (01628) (minus 00204) (18052)

lnFDI minus 00002 minus 00002 minus 00031lowastlowastlowast(minus 12623) (minus 09738) (minus 40693)

lnRC 00010 00008 minus 00046lowastlowastlowast(15418) (13146) (minus 30429)

lnFDIlowastlnRC 00005lowastlowastlowast(39493)

lnPGDP 00039 00011 00020 minus 00047(05725) (01542) (02881) (minus 06725)

ln2PGDP minus 00003 minus 00002 minus 00003 00001(minus 10468) (minus 06610) (minus 07499) (01824)

lnIS 00038lowastlowastlowast 00035lowastlowastlowast 00036lowastlowastlowast 00033lowastlowastlowast(66840) (59351) (60122) (54678)

lnER 00028lowastlowastlowast 00029lowastlowastlowast 00028lowastlowastlowast 00029lowastlowastlowast(75716) (79214) (75282) (79855)

lnPS 00019lowastlowastlowast 00008 00011 00008(43611) (12087) (15333) (11097)

F value 1093861lowastlowastlowast 1097802lowastlowastlowast 942177lowastlowastlowast 881273lowastlowastlowastDW statistic 16368 16353 16226 15265R2 06702 06710 06719 06871Log L 14957000 14961000 14966000 15044000Note lowastlowastlowastSignificant level at 1 lowastlowastsignificant level at 5 and lowastsignificant level at 10 and the values in parentheses indicate t statistic for each estimatedcoefficient

N

0 500 1000km

Low-lowHigh-highNot significant

High-lowLow-high

(a)

0 500 1000km


High-lowLow-high

N

(b)


High-lowLow-high

N

0 500 1000km

(c)

Figure 3 Lisa cluster maps of environmental pollution in China


We use the time effect of spatial panel data models toexplain the environmental effects of FDI and regionalcorruption From Table 5 the spatial lag coefficient ρ inModel (5) is 01450 the spatial error coefficients λ in Models(6)ndash(8) are 01990 01710 and 01820 respectively which areall significant at the 5 level is means that there existsspatial spillover effects of environmental pollution that is tosay environmental pollution in a given region influences thepollution degree of the surrounding areas ForModel (5) theestimation coefficient of FDI is negative and significantindicating that the rising of FDI results in a positive impacton the environmental quality Specifically when othervariables remain constant a 1 increase in FDI will result inan 0005 decrease in environmental pollution is sup-ports the theory of Pollution Halo Hypothesis For Model(6) the estimation results show that the regression coeffi-cient of regional corruption is positive indicating thatcorruption aggravates environmental pollution For Model(7) the estimation coefficients of FDI and regional cor-ruption are negative and positive respectively In Model (8)an interaction term of FDI and regional corruption is addedon the basis of model (7) e interaction coefficient ispositive and significant which suggests that regional cor-ruption reduces the environmental performance of FDIisconclusion supports the theoretical framework model fromSection 3 which proposes that corruption reduces FDI entry

barriers steers toward low-quality FDI and leads to morebribery in government In addition the coefficients forindustrial structure environmental regulation and pop-ulation scale are all positive

54 Regional Difference in Spatial Effects e results of re-gional difference in spatial effects of FDI regional corrup-tion and environmental pollution are shown in Table 6 Forthe eastern region the spatial lag coefficients ρ in Models(5)ndash(8) are positive and significant at the 5 level and forthe central region and the western region the spatial lagcoefficients ρ are negative and significant at the 5 levelindicating the regional spatial spillover effects of environ-mental pollution For the eastern region and the westernregion linear increasing relationships between FDI andenvironmental pollution are found in models (5)ndash(7) that isstrengthening FDI inflows fail to effectively reduce envi-ronmental pollution and Pollution Haven Hypothesis isverified Meanwhile the regression coefficients for regionalcorruption are positive in models (5)ndash(7) indicating thatregional corruption aggravates environmental pollutionHowever the interaction coefficients of FDI and regionalcorruption for the two regions are different that is regionalcorruption increases the environmental aggravation effectsof FDI in the eastern region but weakens it in the western

Table 4 Spatial autocorrelation test of FDI regional corruption and environmental pollution

Test Model 1 Model 2 Model 3 Model 4Moranrsquos I (Z value) 00991lowastlowastlowast (27001) 01031lowastlowastlowast (28116) 01029lowastlowastlowast (28202) 01201lowastlowastlowast (32599)LM-lag (P value) 65958 (00100) 44051 (00360) 62127 (00130) 71937 (00070)Robust LM-Lag (P value) 10469 (03060) 00891 (07650) 05910 (04420) 03659 (05450)LM-error (P value) 61437 (00130) 66523 (00100) 66192 (00100) 90204 (00030)Robust LM-Error (P value) 05948 (04410) 23363 (01260) 09975 (03180) 21926 (01390)Note lowastlowastlowastSignificant level at 1

Table 5 Spatial panel model estimation results of FDI regional corruption and environmental pollution


lnFDI minus 00005lowastlowastlowast minus 00003lowast minus 00035lowastlowastlowast(minus 28913) (minus 17734) (minus 48628)

lnRC 00015lowastlowast 00013lowastlowast minus 00046lowastlowastlowast(22606) (20295) (minus 31619)


lnPGDP 00129lowast 00081 00100 00017(17955) (10413) (12791) (02135)

ln2PGDP minus 00007lowastlowast minus 00005 minus 00006 minus 00002(minus 21396) (minus 14317) (minus 15977) (minus 05427)




ρλ 01450lowastlowastlowast 01990lowastlowastlowast 01710lowastlowast 01820lowastlowastlowast(29376) (28417) (24080) (25767)

Adjust-R2 06836 06811 06855 07039Note lowastlowastlowastSignificant level at 1 lowastlowastsignificant level at 5 and lowastsignificant level at 10 and the values in parentheses indicate t statistic for each estimatedcoefficient


Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012

minus00060lowastlowastlowast



00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004

minus00049lowastlowast

00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS

00031lowastlowastlowast





minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)

ρ00980lowastlowastlowast

00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908

NotelowastlowastlowastSign

ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues

inparenthesesindicate

tstatistic

foreach

estim

ated

coeffi

cient


region For the central region a linear decreasing rela-tionship between FDI and environmental pollution are alsofound in models (5)ndash(7) indicating that FDI inflows reducethe degree of environmental pollution Moreover the in-teraction coefficient is positive and significant which sug-gests that regional corruption reduces the environmentalperformance of FDI

6 Conclusions and Policy Implications

is study investigates the spatial agglomeration effects ofenvironmental pollution and the environmental effects ofFDI and regional corruption in China using spatial econo-metric analysis method e results show that environmentalpollution in China exists spatial agglomeration effects En-vironmental pollution in a region is not only related to itsenvironmental quality but also affected by the surroundingregions For national level the estimation coefficient of FDI issignificantly negative FDI inflows reduce Chinarsquos environ-mental pollution Regional corruption is shown to increaseenvironmental pollution thereby contributing further toenvironmental degradatione interaction coefficient of FDIand regional corruption is significantly positive indicatingthat regional corruption reduces the environmental benefitsderived from FDI

In addition regional differences in spatial effects verifythat regional corruption also reduces the environmentalperformance of FDI in the central region Meanwhile re-gional corruption increases the environmental aggravationeffects of FDI in the eastern region but weakens it in thewestern region Based on these findings some policy rec-ommendations with regard to environmental protection andpollution control are proposed

e spatial dimensions of environmental pollution shouldnot be ignored particularly in developing strategies to addressthe problem e unbounded characteristics and spillovereffects of environmental pollution make it impractical for alocal government to fundamentally address environmentalpollution unitarily A unified approach is required that breaksthrough geopolitical restrictions that should establish a well-coordinated and long-term management scheme whichmainly proceed from the following three aspects First it isnecessary to clear the governance mechanism of responsiblesubjects for environmental pollution cooperative governanceDefining the responsibilities of administrative managementdepartments and the positioning of environmental protectionorganizations and the public are the main promotion mea-sures Second it is necessary to strengthen regional coop-eration Such as an interest linkage mechanism or benefitcompensation mechanism should be established based oncommon interests ird the restriction mechanism of pol-lution governance must be improved Unilateral governmentsupervision or unilateral nongovernment supervision orpublic supervision are all incomplete supervision penaltiesshould be imposed on enterprises that exceeding the emissionstandards

Based on the empirical results it is important to increasethe environmental performance of FDI On one hand theCentral Peoplersquos Government must focus on improving

regional corruption problem such as preventive educationinstitution construction and official governance so as tobetter utilize the positive environmental effects of FDI onthe other hand if it is difficult to improve corruption in ashort period the entry barriers to FDI must be strictlyregulated In addition without considering FDI the esti-mation results find that regional corruption also increasesenvironmental pollution e implication is that FDI willbribe the government and domestic enterprises will alsobribe the government to obtain loose environmental su-pervision erefore corruption prevention mechanismspunitive mechanisms and supervision mechanisms shouldbe established to increase the cost of corruption and reducethe incidence and benefits of corruption Special laws onanticorruption should be formulated which provide pow-erful legal weapons for combatting corruption Anticor-ruption efforts are not only a practical issue related topolitical reform and economic growth but also an importantissue related to sustainable development Especially for theeastern and central regions we must take countermeasuresto combat regional corruption such as strengtheningideological education and improving the moral standards ofthe public and public officials Meanwhile it is necessary tochange the mode of economic growth optimize the in-dustrial structure promote the export of goods and servicesand shift the structure of goods to a cleaner directionMeanwhile in order to better absorb the technology spill-over effects of FDI and play the role of FDI in improving theenvironmental quality through structural and technologicaleffects local government should increase investment inresearch and development deepen financial market reformand improve the level of human capital and financialdevelopment

Data Availability

e data used to support the findings of this studyhave been deposited at httpspanbaiducoms1Nwbbwm5t8XbwJjJDG7avuQ (password cnxy)

Conflicts of Interest

e authors declare that they have no conflicts of interest

Acknowledgments

is study was supported by Guangdong Philosophy andSocial Science Planning Fund (Grant no GD18YGL01)National Natural Science Foundation of China (Grant no41361029) Guangdong Natural Science Fund (Grant no2018A030313842) and Foshan City Philosophy and SocialScience Fund (Grant nos 2019-QN17)

References

[1] W Keller and A Levinson ldquoPollution abatement costs andforeign direct investment inflows to US statesrdquo Review ofEconomics and Statistics vol 84 no 4 pp 691ndash703 2002

[2] J M Dean M E Lovely and HWang ldquoAre foreign investorsattracted to weak environmental regulations evaluating the


evidence from Chinardquo Journal of Development Economicsvol 90 no 1 pp 1ndash13 2009

[3] A A Rezza ldquoFDI and pollution havens evidence from theNorwegian manufacturing sectorrdquo Ecological Economicsvol 90 pp 140ndash149 2013

[4] S Chung ldquoEnvironmental regulation and foreign direct in-vestment evidence from South Koreardquo Journal of Develop-ment Economics vol 108 pp 222ndash236 2014

[5] J P Tang ldquoPollution havens and the trade in toxic chemicalsevidence from US trade flowsrdquo Ecological Economicsvol 112 pp 150ndash160 2015

[6] S A Solarin U Al-Mulali I Musah and I Ozturk ldquoIn-vestigating the pollution haven hypothesis in Ghana anempirical investigationrdquo Energy vol 124 pp 706ndash719 2017

[7] M A Cole R J R Elliott and J Zhang ldquoGrowth foreign directinvestment and the environment evidence from Chinese cit-iesrdquo Journal of Regional Science vol 51 no 1 pp 121ndash138 2011

[8] R Rana and M Sharma ldquoDynamic causality testing for EKChypothesis pollution haven hypothesis and internationaltrade in Indiardquogte Journal of International Trade amp EconomicDevelopment vol 28 no 3 pp 348ndash364 2018

[9] W Antweiler B R Copeland and M S Taylor ldquoIs free tradegood for the environmentrdquo American Economic Reviewvol 91 no 4 pp 877ndash908 2001

[10] J He ldquoPollution haven hypothesis and environmental im-pacts of foreign direct investment the case of industrialemission of sulfur dioxide (SO2) in Chinese provincesrdquoEcological Economics vol 60 no 1 pp 228ndash245 2006

[11] N A Neequaye and R Oladi ldquoEnvironment growth and FDIrevisitedrdquo International Review of Economics amp Financevol 39 pp 47ndash56 2015

[12] C F Tang and B W Tan ldquoe impact of energy con-sumption income and foreign direct investment on carbondioxide emissions in Vietnamrdquo Energy vol 79 pp 447ndash4542015

[13] F H Liang ldquoDoes foreign direct investment harm the hostcountryrsquos environment evidence from Chinardquo Academy ofManagement Journal vol 14 pp 38ndash53 2005

[14] A Kearsley and M Riddel ldquoA further inquiry into the pol-lution haven hypothesis and the environmental Kuznetscurverdquo Ecological Economics vol 69 no 4 pp 905ndash919 2010

[15] A A Rafindadi I M Muye and R A Kaita ldquoe effects ofFDI and energy consumption on environmental pollution inpredominantly resource-based economies of the GCCrdquoSustainable Energy Technologies and Assessments vol 25pp 126ndash137 2018

[16] G M Grossman and A B Krueger ldquoEnvironmental impactsof a North American Free Trade Agreementrdquo NBERWorkingPaper p 3914 National Bureau of Economic ResearchCambridge MA USA 1991

[17] Q Bao Y Chen and L Song ldquoForeign direct investment andenvironmental pollution in China a simultaneous equationsestimationrdquo Environment and Development Economicsvol 16 no 1 pp 71ndash92 2011

[18] J Lan M Kakinaka and X Huang ldquoForeign direct invest-ment human capital and environmental pollution in ChinardquoEnvironmental and Resource Economics vol 51 no 2pp 255ndash275 2012

[19] Q Liu S Wang W Zhang D Zhan and J Li ldquoDoes foreigndirect investment affect environmental pollution in Chinarsquoscities a spatial econometric perspectiverdquo Science of gte TotalEnvironment vol 613-614 pp 521ndash529 2018

[20] Z Li and H F D I Liu ldquoRegional corruption and envi-ronmental pollution an empirical research based on

threshold effectsrdquo gte Journal of International Trade ampEconomic Development vol 7 pp 50ndash61 2017

[21] M Habib and L Zurawicki ldquoCorruption and foreign directinvestmentrdquo Journal of International Business Studies vol 33no 2 pp 291ndash307 2002

[22] B Han and Q Xue ldquoImpact of host country corruption onFDI and its sourcesrdquo Contemporary Finance vol 2 pp 99ndash105 2008

[23] C M Amarandei ldquoCorruption and foreign direct investmentevidence from central and eastern European statesrdquo Centre forEuropean Studies Working Papers vol 3 pp 311ndash322 2013

[24] P Egger and H Winner ldquoEvidence on corruption as anincentive for foreign direct investmentrdquo European Journal ofPolitical Economy vol 21 no 4 pp 932ndash952 2005

[25] S Bellos and T Subasat ldquoGovernance and foreign directinvestment a panel gravity model approachrdquo InternationalReview of Applied Economics vol 26 no 3 pp 303ndash3282012

[26] X Liao and E Xie ldquoWhy China attracts FDI inflows aperspective of environmental stringency and the degree ofcorruptibilityrdquo World Economic Situation amp Prospects vol 1pp 112ndash119 2005

[27] B K Smarzynska and S J Wei ldquoCorruption and compositionof foreign direct investment firm-level evidencerdquo NBERWorking Paper No w7969 p 7969 NBER Cambridge MAUSA 2000

[28] Q Xue and B Han ldquoe impact of corruption in host countryon multinationalrsquos entry moderdquo Economics Research Journalvol 4 pp 88ndash98 2008

[29] S-J Wei ldquoLocal corruption and global capital flowsrdquoBrookings Papers on Economic Activity vol 2000 no 2pp 303ndash346 2000

[30] R B Wooster and J Billings Foreign Direct InvestmentPolicies Economic Impacts and Global Perspectives NovaScience Publishers Inc New York NY USA 2013

[31] M A Cole R J R Elliott and P G Fredriksson ldquoEndog-enous pollution havens does FDI influence environmentalregulationsrdquo Scandinavian Journal of Economics vol 108no 1 pp 157ndash178 2006

[32] Y Gorodnichenko J Svejnar and K Terrell ldquoWhen does FDIhave positive spillovers evidence from 17 emerging mar-keteconomiesrdquo Journal of Comparative Economics vol 4pp 954ndash969 2007

[33] K E Meyer and E Sinani ldquoWhen and where does foreigndirect investment generate positive spillovers a meta-anal-ysisrdquo Journal of International Business Studies vol 40 no 7pp 1075ndash1094 2009

[34] P Mauro ldquoCorruption and the composition of governmentexpenditurerdquo Journal of Public Economics vol 69 no 2pp 263ndash279 1998

[35] B Dong and B Torgler ldquoe consequences of corruptionevidence from Chinardquo QUT School of Economics and Fi-nanceWorking Paper p 456 QUT Brisbane Australia 2010

[36] R Lopez and S Mitra ldquoCorruption pollution and theKuznets environment curverdquo Journal of EnvironmentalEconomics and Management vol 2 pp 137ndash150 2000

[37] A Leitatildeo ldquoCorruption and the environmental Kuznets curveempirical evidence for sulfurrdquo Ecological Economics vol 69no 11 pp 2191ndash2201 2010

[38] C P Chang and Y Hao ldquoEnvironmental performancecorruption and economic growth global evidence using a newdata setrdquo Applied Economics vol 5 pp 1ndash17 2016

[39] M Lisciandra and C Migliardo ldquoAn empirical study of theimpact of corruption on environmental performance


evidence from panel datardquo Environmental and ResourceEconomics vol 68 no 2 pp 297ndash318 2017

[40] L Pellegrini and R Gerlagh ldquoAn empirical contribution to thedebate on corruption democracy and environmental policyrdquoFEEMWorking Paper Fondazione Eni Enrico Mattei MilanItaly 2005

[41] P Oliva ldquoEnvironmental regulations and corruption auto-mobile emissions in Mexico cityrdquo Journal of Political Econ-omy vol 123 no 3 pp 686ndash724 2015

[42] K Ivanova ldquoCorruption and air pollution in Europerdquo OxfordEconomic Papers vol 63 no 1 pp 49ndash70 2011

[43] P G Fredriksson and J Svensson ldquoPolitical instabilitycorruption and policy formation the case of environmentalpolicyrdquo Journal of Public Economics vol 87 no 7-8pp 1383ndash1405 2003

[44] A K Biswas and M um ldquoCorruption environmentalregulation and market entryrdquo Environment and DevelopmentEconomics vol 22 no 1 pp 66ndash83 2017

[45] B Smarzynska and S Wei ldquoPollution havens and foreigndirect investment dirty secret or popular mythrdquo NBERWoring Paper NBER Cambridge MA USA 2001

[46] R Damania P G Fredriksson and J A List ldquoTrade liber-alization corruption and environmental policy formationtheory and evidencerdquo Journal of Environmental Economicsand Management vol 46 no 3 pp 490ndash512 2003

[47] F Candau and E Dienesch ldquoPollution haven and corruptionparadiserdquo Journal of Environmental Economics and Man-agement vol 85 pp 171ndash192 2017

[48] M A Cole ldquoCorruption income and the environment anempirical analysisrdquo Ecological Economics vol 62 no 3-4pp 637ndash647 2007

[49] S B Wang and Y Z Xu ldquoEnvironmental regulation and hazepollution decoupling effect based on the perspective of en-terprise investment preferencesrdquo China Industrial Economicsvol 4 pp 18ndash30 2015

[50] Y Liu and F Dong ldquoHow industrial transfer processes impacton haze pollution in China an analysis from the perspective ofspatial effectsrdquo International Journal of Environmental Re-search and Public Health vol 16 no 3 pp 423ndash438 2019

[51] B Diao L Ding P Su and J Cheng ldquoe spatial-temporalcharacteristics and influential factors of NOx emissions inChina a spatial econometric analysisrdquo International Journalof Environmental Research and Public Health vol 15 no 7pp 1405ndash1423 2018

[52] LAnselinExploring SpatialDatawithGeoDaAWorkbook 2005httpsgeodacenterasuedusystemfilesgeodaworkbookpdf


effect theory which states that FDI can influence environ-mental pollution through multiple channels including scaleeffect structure effect technology effect and environmentalregulation effect [16ndash19]e environmental effects of FDI isa combination resulting from multiple channels e con-tradictory conclusions may be due to differences in methodsvariable selection and sample intervals and it may be relatedto the Chinarsquos unique national conditions

In recent years with the continuous improvement ofChinarsquos anticorruption efforts many government officialsafter the 19th National Congress of the Communist Party ofChina (CPC) are fired including Lu Wei Liu Qiang ZhangYang and Sun Zhengcai etc Meanwhile transnationalbribery incidents in China have been on the rise in the pastten years and 64 of which are related to foreign trade andFDI e purposes of bribery are mainly to obtain businessopportunities and evade environmental supervision whichmean that the threshold for foreign companies will belowered implicitly and then lead to the deterioration ofenvironmental quality erefore when examining the en-vironmental effects of FDI the impact of institutional defectson FDI behavior characteristics cannot be ignored How-ever previous literature on the environmental effects of FDIfrom the institutional level is scarce So a theoretical andempirical study on the environmental effects of FDI con-sidering in the case of government corruption has greatsignificance for China

is article investigates the spatial agglomeration effectsof environmental pollution in China and the environmentaleffects of FDI and regional corruption from a spatial per-spective Specifically this paper analyzes and constructs atheoretical analysis model on the influence paths of FDI andregional corruption on environmental pollution en weinvestigates the spatial agglomeration effects of environ-mental pollution using relevant statistical data from Chinarsquosprovinces municipalities and autonomous regions (ex-cluding Hong Kong Macao Taiwan and Tibet) Further-more using spatial econometric analysis method theenvironmental effects of FDI and regional corruption basedon the national level and regional differences are exploredFinally this paper provides relevant policy recommenda-tions toward environmental protection is paper is con-structed as follows Section 2 introduces the literature reviewon FDI regional corruption and environmental pollutionSection 3 analyzes the theoretical paths of the effects of FDIand regional corruption on environmental pollution Sec-tion 4 introduces the data variables and methods Section 5presents the empirical results Section 6 presents the con-clusions and puts forward related policy implications

2 Literature Review

Corruption is an important influencing factor of foreigncapital inflow and technological progress [20]erefore theissues about the impacts of corruption on technologicalspillover effects and environmental effects of FDI are also thefocus of academic research but related research is limitedBased on a review of existing research research on theimpact of regional corruption on FDI focuses on the

following two aspects one is the discussion of the impactdirection of regional corruption on FDI that is whethercorruption favors or hinders FDI For example Habib andZurawicki Han and Xue and Amarandei showed that theincreased corruption degree of host country will increase theoperating costs of multinational companies thereby re-ducing the attractiveness of local FDI [21ndash23] HoweverEgger and Winnerrsquos investigation in 73 developing coun-tries Bellos and Subasatrsquos study in 14 transition countriesand Liao and Xiarsquos study in 29 provinces in China showedthat corruption is an effective way to circumvent environ-mental control and institutional deficiencies of host countryand do not hinder FDI inflows but instead promote FDIinflows [24ndash26]

e other one is the discussion of the specific impact ofregional corruption on FDI For example Smarzynska andWei and Xue and Han investigated the impact of corruptiondegree on the ownership structure of FDI and believed thatthe increase of corruption degree in the host country willreduce FDI (especially developed country FDI or market-oriented FDI) [27 28] Also Wei [29] and Wooster andBillings [30] examined the impact of regional corruption onFDI entry models and argued that the rising of corruption inthe host country will reduce the possibility of foreign in-vestment entering the local area with greenfield investmentLi and Liu [20] and Cole et al [31] also examined the impactof regional corruption on the environmental effects of FDIand believed that regional corruption in host countriesinhibits the positive effects of FDI on environment qualitysignificantly for investment destinations Research on theimpact of regional corruption on FDI has been fruitfullyexplored by the existing literature However the influencemechanism of regional corruption on FDI are not beensystematically studied Gorodnichenko et al [32] and Meyerand Sinani [33] also simply discussed the degree of hostcountry corruption as an influencing factor when examiningtechnology spillover effects of FDI in the host countryClassical studies by scholars such as Mauro [34] and Dongand Torgler [35] pointed out that corruption behaviors tendto reduce government spending on science educationculture and health which in turn affect the absorptioncapacity of FDI technology spilloverserefore an in-depthdiscussion about the influence mechanism of regionalcorruption on FDI is conducive to the further improvement-related research in this field

e current literature on the relationship of regionalcorruption and environmental pollution can be divided intothree categories e first category study of corruption andpollution is based on the framework of economic growthcorruption and environment pollution Lopez and Mitraused the Stackelberg model to analyze the influence of re-gional corruption on Environmental Kuznets Curve (EKC)which concluded that regional corruption aggravates envi-ronmental quality [36] ey found that regional corruptiondirectly increases pollution level whereas corruption has anindirect inverse effect by impeding income growth [10]Based on the EKC theory Leitao showed that regionalcorruption increases local environmental pollution [37]Chang and Hao found that when the government has high













41 Methods


w


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ (1)




ni11113936


1113936ni11113936

nj1wij1113936


2

1113936

ni11113936


s21113936ni11113936

nj1wij

Ii xi minus x( 11138571113936


s2

(2)





(3)








(4)






















adjacent regions

FDI in local

regions




Excessivedemand

Scaleeffect

Technique effect

Structural effect

Regulationeffect





Excessive demand

Scaleeffect

Technique effect

Structural effect

Regulationeffect

Spatial effect







EP_2005


Lagg

ed E

P_20

05

ndash140

ndash060

100


020

180

EP_2010


Lagg

ed E

P_20

10

ndash150

ndash060

120


030

210

EP_2015


Lagg

ed E

P_20

15

ndash160

ndash060

140


040

240




















N

0 500 1000km


High-lowLow-high

(a)

0 500 1000km


High-lowLow-high

N

(b)


High-lowLow-high

N

0 500 1000km

(c)













lnPGDP 00129lowast 00081 00100 00017(17955) (10413) (12791) (02135)








Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012




00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004


00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS






minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)


00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908


ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues


tstatistic

foreach

estim

ated

coeffi

cient









Data Availability




Acknowledgments


References






































































41 Methods


w


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦ (1)




ni11113936


1113936ni11113936

nj1wij1113936


2

1113936

ni11113936


s21113936ni11113936

nj1wij

Ii xi minus x( 11138571113936


s2

(2)





(3)








(4)






















adjacent regions

FDI in local

regions




Excessivedemand

Scaleeffect

Technique effect

Structural effect

Regulationeffect





Excessive demand

Scaleeffect

Technique effect

Structural effect

Regulationeffect

Spatial effect







EP_2005


Lagg

ed E

P_20

05

ndash140

ndash060

100


020

180

EP_2010


Lagg

ed E

P_20

10

ndash150

ndash060

120


030

210

EP_2015


Lagg

ed E

P_20

15

ndash160

ndash060

140


040

240




















N

0 500 1000km


High-lowLow-high

(a)

0 500 1000km


High-lowLow-high

N

(b)


High-lowLow-high

N

0 500 1000km

(c)













lnPGDP 00129lowast 00081 00100 00017(17955) (10413) (12791) (02135)








Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012




00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004


00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS






minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)


00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908


ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues


tstatistic

foreach

estim

ated

coeffi

cient









Data Availability




Acknowledgments


References












































































adjacent regions

FDI in local

regions




Excessivedemand

Scaleeffect

Technique effect

Structural effect

Regulationeffect





Excessive demand

Scaleeffect

Technique effect

Structural effect

Regulationeffect

Spatial effect







EP_2005


Lagg

ed E

P_20

05

ndash140

ndash060

100


020

180

EP_2010


Lagg

ed E

P_20

10

ndash150

ndash060

120


030

210

EP_2015


Lagg

ed E

P_20

15

ndash160

ndash060

140


040

240




















N

0 500 1000km


High-lowLow-high

(a)

0 500 1000km


High-lowLow-high

N

(b)


High-lowLow-high

N

0 500 1000km

(c)













lnPGDP 00129lowast 00081 00100 00017(17955) (10413) (12791) (02135)








Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012




00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004


00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS






minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)


00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908


ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues


tstatistic

foreach

estim

ated

coeffi

cient









Data Availability




Acknowledgments


References































































EP_2005


Lagg

ed E

P_20

05

ndash140

ndash060

100


020

180

EP_2010


Lagg

ed E

P_20

10

ndash150

ndash060

120


030

210

EP_2015


Lagg

ed E

P_20

15

ndash160

ndash060

140


040

240




















N

0 500 1000km


High-lowLow-high

(a)

0 500 1000km


High-lowLow-high

N

(b)


High-lowLow-high

N

0 500 1000km

(c)













lnPGDP 00129lowast 00081 00100 00017(17955) (10413) (12791) (02135)








Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012




00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004


00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS






minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)


00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908


ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues


tstatistic

foreach

estim

ated

coeffi

cient









Data Availability




Acknowledgments


References










































































N

0 500 1000km


High-lowLow-high

(a)

0 500 1000km


High-lowLow-high

N

(b)


High-lowLow-high

N

0 500 1000km

(c)













lnPGDP 00129lowast 00081 00100 00017(17955) (10413) (12791) (02135)








Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012




00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004


00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS






minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)


00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908


ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues


tstatistic

foreach

estim

ated

coeffi

cient









Data Availability




Acknowledgments


References





































































lnPGDP 00129lowast 00081 00100 00017(17955) (10413) (12791) (02135)








Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012




00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004


00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS






minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)


00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908


ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues


tstatistic

foreach

estim

ated

coeffi

cient









Data Availability




Acknowledgments


References



























































Tabl

e6

Region

alDifference

inSpatialE

ffectsof

FDIregion

alcorrup

tionandenvironm

entalp

ollutio

n

Variables

Easternregion

Central

region

Western

region

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

Mod

el5

Mod

el6

Mod

el7

Mod

el8

lnFD

I00001

00001

minus00012




00005lowastlowast

00006lowastlowast

00030lowastlowast

(05868)

(05523)

(minus10699)

(minus80878)

(minus69369)

(minus28113)

(23137)

(25040)

(21754)

lnRC

00003

00002

minus00025

00017lowast

00026lowast

minus00205lowast

00017

00020lowast

00058lowastlowast

(03789)

(03274)

(minus10079)

(18741)

(17133)

(minus17550)

(15682)

(18586)

(24258)

lnFD

IlowastlnRC

00002

00018lowastlowast

minus00004lowast

(11586)

(19968)

(minus17844)

lnPG

DP

00053

00041

00039

00029

00895lowastlowast

minus00061

00889lowastlowast

00835lowastlowast

minus00117

minus00086

minus00100

minus00058

(04816)

(03535)

(03311)

(02446)

(21891)

(minus03767)

(22104)

(21190)

(minus11776)

(minus08498)

(minus10130)

(minus05811)

ln2 PGDP

minus00005

minus00005

minus00005

minus00004


00006



00004

00004

00003

00001

(minus10233)

(minus08361)

(minus08250)

(minus07467)

(minus23909)

(07005)

(minus24824)

(minus23943)

(08371)

(07034)

(07062)

(02793)

lnIS






minus00001



00009

00013lowast

00006

00006

(55758)

(50173)

(49523)

(50957)

(50527)

(minus00871)

(49336)

(49373)

(12200)

(18236)

(08043)

(07878)

lnER





00017lowast

00009lowastlowast

00018lowastlowast

00020lowastlowast





(62192)

(59964)

(60231)

(61239)

(18744)

(20132)

(20012)

(22518)

(62495)

(51155)

(57000)

(53109)

lnPS









minus00003

minus00006

minus00019lowast

minus00019lowast

(72337)

(44780)

(44322)

(40059)

(79583)

(minus59123)

(35347)

(37900)

(minus04101)

(minus05910)

(minus17550)

(minus17770)


00920lowastlowast

00930lowastlowast

00900lowastlowast









(26399)

(24268)

(24547)

(23707)

(minus30359)

(minus25202)

(minus30483)

(minus29937)

(minus24332)

(minus25515)

(minus25025)

(minus24969)

Adjust-R2

09313

09314

09315

09323

06313

04319

06398

06618

06713

06581

06849

06908


ificant

levela

t1lowastlowastsig

nificantlevela

t5

andlowastsig

nificantlevel

at10a

ndthevalues


tstatistic

foreach

estim

ated

coeffi

cient









Data Availability




Acknowledgments


References


































































Data Availability




Acknowledgments


References



























































regional difference in spatial effects: a theoretical and...

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