foreign direct investment, regional innovation, and green

Research ArticleForeign Direct Investment Regional Innovation and GreenEconomic Efficiency An Empirical Test Based on theInvestigation of Intermediary Effect and Threshold Effect

You-Qun Wu 1 Huai-Xin Lu 1 Xin-Lin Liao 1 Jia-Bao Liu 2 and Jia-Ming Zhu 3

1School of Economics Anhui University of Finance and Economics Bengbu 233030 Anhui China2School of Mathematics and Physics Anhui Jianzhu University Hefei 230601 China3Institute of Quantitative Economics Anhui University of Finance and Economics Bengbu 233030 China

Correspondence should be addressed to Jia-Ming Zhu zhujm1973163com

Received 11 August 2021 Accepted 14 September 2021 Published 30 September 2021

Academic Editor Heng Liu

Copyright copy 2021 You-QunWu et al1is 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

Based on the theoretical mechanism analysis of FDI regional innovation and green economic efficiency this article uses Chinarsquosprovincial panel data to calculate the provincial green economic efficiency level based on the three-stage DEAmethod and uses thesystemGMMmodel intermediary effect model and thresholdmodel to empirically test the specific effects and transmission pathsof FDI on the efficiency of the green economy Research shows that FDI is one of the important factors that promote theimprovement of green economic efficiency Subregional tests have found that FDI has a significant regional heterogeneity inpromoting the efficiency of the green economy 1e mediation effect test found that the mediation effect of regional innovation issignificant and FDI can significantly promote the growth of green economic efficiency through regional innovation 1ethreshold effect analysis found that there are significant and effective double thresholds for regional economic levels and theimpact of FDI on green economic efficiency is heterogeneous within different threshold intervals 1e research conclusionsprovide new inspiration for China to allocate FDI more rationally and efficiently under the new development pattern

1 Introduction

Since the reform and opening up Chinarsquos ldquoSpecial Man-agement Measures for Foreign Investment Access (NegativeList)rdquo project has continued to reduce and the ldquoNotice onSeveral Measures to Actively and Effectively Use ForeignInvestment to Promote High-quality Economic Develop-mentrdquo has continued to expand the scope of opening up In2020 China absorbed total foreign investment to US $1639billion up by 4 from the previous year accounting for 19of the global foreign direct investment (Foreign Direct In-vestment FDI) realizing the ldquothree increasesrdquo in the totalinvestment volume the growth range and the global pro-portion In the new development stage Chinarsquos developmentenvironment is facing profound and complex changes 1e14th five year plan and the outline of long-term objectives for2035 run through a clear main line that is we should

unswervingly implement the new development concept andlist ldquoimplementing high-level opening to the outside worldand promoting the stability and quality improvement offoreign trade and foreign investmentrdquo as one of the key tasksOne belt one road to building a high-quality economy andthe need to participate in global economic governance reformand improvement is the foundation for opening up a neweconomic system at a higher level As an important way ofinternational high-tech technology spillover FDI can pro-mote the reverse spillover of high-tech developing countriesand has a significant impact on the industrial structureunemployment rate and productivity of the host country [1]At present China is in the context of ldquodouble cyclerdquo and thegovernment needs to optimize the level and structure offoreign capital and comprehensively enhance the integrationof foreign capital and local economy [2]What is the impact ofFDI and regional innovation on green economic efficiency

HindawiComputational Intelligence and NeuroscienceVolume 2021 Article ID 7348599 14 pageshttpsdoiorg10115520217348599

What is the impact of FDI on green economic efficiencyheterogeneous A series of problems can provide new en-lightenment for Chinarsquos more reasonable and efficient allo-cation of FDI under the new development pattern

2 Literature Review

1e influence of FDI on the host country has been a key areaof scholars Scholars have discussed the impact of FDI on theeconomic growth resources and environment of the hostcountry from different perspectives mainly with the fol-lowing views (1) FDI can significantly promote the eco-nomic growth of the host country Some scholars believe thatthe most direct effect of FDI on the host country is theaccumulation effect of material capital and the lack of fi-nancial support in the early stage of economic developmentleads to the obstruction of regional development 1e in-troduction of FDI can provide capital for the infrastructureconstruction of the host country improve the capital uti-lization rate and facilitate the industrial structure upgrading[3] Second FDI is the main channel for the cross-borderflow of high and new technology It can promote the im-provement of personnel factors through technology spillovereffect and industry correlation effect make up for the lack ofpersonnel factors in host countries [4] and significantlyimprove the total factor productivity of host countries [5](2) 1e FDI is not conducive to the economic growth of thehost country Relevant studies believe that the large influx ofFDI is one of the reasons for the negative economic growthof the host country which is due to the competition andsubstitution effects between FDI and the domestic capital ofthe host country Initially the introduction of FDI in thehost country will increase its total capital amount when thecontinuous increase of FDI will cause its substitution effectand crowding effect gradually significant making the totaldomestic capital tends to decline eventually resulting in thehost country capital contraction [6]1ere is also a thresholdvalue for the FDI own scale 1e FDI scale is less than thethreshold value and the positive effect on the innovationefficiency when it exceeds the threshold value is not sig-nificant or even has a negative impact [7] For developingcountries excessive reliance on FDI in the early stage ofeconomic development leads to urbanization attached tothe industrialization process resulting in lag or excessiveurbanization [8] Second the technical spillover effect of FDIis not idealized for the input host country 1e main reasonis that it is difficult for the employees of domestic enterprisesin the host country to accept the advanced technology andmanagement experience of foreign enterprises In recentyears foreign-owned enterprises in sole proprietorship orholding form have increased restrictions on technologyspillover reducing the marginal contribution of FDI toeconomic growth [9] 1e spillover effect of FDI is alsolimited by the host countryrsquos own policy environmenteconomic development level human capital level and otherfactors When the policy environment is conducive to theintroduction of foreign investment and the high level ofeconomic development the positive spillover effect of for-eign investment will be strong 1ird as the host country

attaches great importance to high-tech research and de-velopment it promotes the transformation of domesticproduction technology so that the technology spillovereffect of FDI will be further reduced Moreover the tech-nology transfer effect of foreign capital is relatively strongWhen the development of foreign capital in the host countryis hindered it will promptly change the investment envi-ronment At this time the negative effect of FDI will becomemore significant [10] Finally relevant research shows thatthe industrial correlation effect of FDI does not systemati-cally form a positive spillover of upstream and downstreamenterprises but will inhibit the enthusiasm of related en-terprises and leads to the vicious competition [11] (3)Regarding the ldquoPollution of Heavenrdquo hypothesis somescholars believe that FDI will aggravate environmentalpollution in the host country and exchange ldquogreen watersand green mountainsrdquo for economic growth Relevant re-search shows that in order for developing countries to solvethe problem of lack of capital and technology in the earlystage of economic development they will reduce environ-mental regulatory standards to attract foreign capital intotheir own market resulting in excessive exploitation andutilization of domestic natural resources [12] 1e contin-uous reduction of environmental standards will form theldquoenvironmental bottom line competitionrdquo phenomenon1eFDI enters the host country without idealized high-tech in-dustry mostly concentrated on high pollution and highenergy consumption industries and forming the occupationof host natural resources and is not conducive to guide thedevelopment of host green industry but intensifies the en-vironmental pollution problem [13] (4)1e ldquoPollution Aurardquohypothesis states that the effects of FDI on technologyspillover industrial correlation and market competition inthe host country will promote the technological progress ofthe host country improve the utilization of production factorsand reduce pollution emissions and facilitate the treatment ofresource utilization and environmental pollution problems inthe host country1e main reason is that developed countriesare more stringent standards for waste discharge from en-terprises and more perfect environmental regulation stan-dards So FDI has more advanced production technology andpollutant treatment technology which can improve the re-source utilization rate and reduce the damage to the envi-ronment In addition the imitation of domestic enterprises inthe host country forms the improvement of the productionlevel and harmless waste treatment technology in the hostcountry 1e specific study is as follows Wang et al researchfound that FDI significantly promotes the regional green totalfactor efficiency by improving the input efficiency of labor andenergy [14] Liu and Zhao started from the three industrialwastes and found that FDI will effectively encourage thetreatment of traditional pollutants especially for sulfur di-oxide and industrial wastewater [15] Huang and Zhou an-alyzed the Yangtze River Economic Belt and found that FDIcan effectively inhibit wastewater discharge and promote theoptimization of environmental resources [16]

In conclusion the existing literature has recognized theeconomic and environmental impact of FDI and explored itwhich has an important inspiration for this paper1is paper

2 Computational Intelligence and Neuroscience

finds that there is room for further exploration in theempirical research of FDI and economy and environmentmainly in the following aspects first most studies favor theunilateral impact of FDI on economic growth or environ-mental pollution ignoring the quality of economic devel-opment and comprehensively considering the economicefficiency and environmental factors management factorsand statistical noise Second most literature use grouping orintroduce multiplication model to explore the relationshipbetween FDI and economic growth or environmental pol-lution Its subjective factors are large and cannot get aspecific threshold In a few threshold models such as theHansen static panel threshold model the premise is morestringent and did not use the subsequent perfect dynamicpanel threshold model for empirical analysis 1ird mostliterature studies whether there is a threshold for FDIrsquos ownscale and considers less whether the influence of FDI pro-duces heterogeneity at different economic developmentlevels

1erefore the main contributions of this paper are asfollows First based on the nonexpected output in the superSBM-DEA model the environmental factors managementfactors and statistical noise are considered using the three-stage DEA to obtain the green economic efficiency valueSecond considering the ldquoinertiardquo of the economic systemusing the dynamic system GMM model not only examinesthe perspective of FDI but also introduces regional inno-vation into the category of model research and analyzes therole mechanism of FDI on the green economic efficiency1ird using the dynamic panel threshold model the non-linear influence of FDI on green economic efficiency isdiscussed for different economic development levels

3 Analysisof theTheoreticalMechanismofFDIRegional Innovation and GreenEconomic Efficiency

Based on the model of Ji et al [17] this paper constructs atheoretical model of the nonlinear relationship between FDIregional innovation and green economic efficiency First weassume that the green economic efficiency level of a countryor region will be affected by regional innovation produc-tivity and other factors under regional innovation withouttechnological innovation Second it is assumed that greeneconomic efficiency follows the form of C-D productionfunction

GECMit Ait lowast INαit lowastG

βit (1)

Among them i represents the region t represents thetime the GECMit represents the green economic efficiencyof expected output unexpected output environmentalfactors and statistical noise and random noiseAit representsother factors that may affect the green economic efficiencyINit represents a regional innovation 1e coefficient α in-dicates the relationship between regional innovation in-tensity and green economic efficiency When αgt 0 indicatesa positive correlation between the two the stronger theregional innovation the higher the actual green economicefficiency level When αlt 0 there is a negative correlationbetween the two When α 0 this indicates that regionalinnovation is not green economic efficiency Git is the ef-ficiency in the production process without considering re-gional innovation and its value is mainly determined by themarket behavior of enterprise departments in economicactivities 1e coefficient β shows the production depart-ment for the productivity of the importance1e value rangeis 0lt βle 1 β value means the enterprise of the imple-mentation of innovation research and development pro-ductivity 1e greater β value shows the enterpriseinnovation research and development productivity atten-tion and its innovation and development consciousness isweak

Git is closely related to Yit the output of enterprisedepartments whether from the perspective of productionprocess or output results and represents production effi-ciency It is related to Hit the technology input in enterpriseproduction process that is Git is related to Hit 1us theproduction efficiency Git is assumed in obedience to theconstant alternative elastic production function and thenGit can be represented as

Git c1 lowastYminus ρit + c2 lowastH

minus ρit( 1113857

minus (mρ) (2)

Among them 0lt c1le 1 0lt c2le1 ρge minus 1 1e intro-duction of FDI will produce a series of effects such as capitalaccumulation effects and technology spillover effects whichwill directly or indirectly affect Hit 1erefore Hit can berepresented by FDIit

Hit klowast FDIit (3)

Among them kgt 0 Substituting formulas (2) and (3)into formula (1) we have

GECMit Ait lowast INαit lowast c1 lowastY

minus ρit + c2 lowast klowast FDIit( 1113857

minus ρ1113858 1113859

minus (mβρ)

(4)

1en take the logarithm of (4) as a whole

LnGECMit LnAit + αLnINit minusmβρlowastLn c1 lowastY


minus ρ1113858 1113859 (5)

Ln[c1 lowastYminus ρit + c2 lowast (klowast FDIit)

minus ρ] in (5) is extended inorder 2 at ρ 0

Computational Intelligence and Neuroscience 3

LnGECMit LnAit +mβρ

c2

c1 + c2Lnk minus

mβρ

c22

c1 + c2( 11138572(Lnk)

2minus

mβρ

c2

c1 + c2(Lnk)

2+ αLnINit

+mβρ

c1

c1 + c21 minus

2c2

c1 + c2Lnk1113888 1113889lowastLnY

minusmβρ

c1

c1 + c21 minus

c1

c1 + c21113888 1113889lowast (LnY)

2minus

mβρ

c2

c1 + c21 +

c2

c1 + c21113888 1113889 LnFDIit( 1113857

2

+mβρ

c2

c1 + c21 minus Lnk minus

2c2

c1 + c2Lnk1113888 1113889lowastLnFDIit minus

mβρ

2c1c2

c1 + c2( 11138572 LnYit lowastLnFDIit

(6)

From the previously mentioned formula FDI has adirect and indirect impact on green economic efficiency andit is directly affected by LnFDIit 1e coefficient size of theterm is reflected When the coefficient is greater than 0 FDIshows positive effect on the green economic efficiency andwhen the coefficient is less than 0 FDI shows negative effecton the green economic efficiency 1e LnFDIit presents thesquare term When the coefficient is not zero it indicatesthat there is a nonlinear variation relation in the role of FDIon the green economic efficiency 1e direct impact of re-gional innovation on green economic efficiency can beexpressed by the coefficient α

Based on the analysis of existing literature mechanismthis paper believes that with the deepening of the globalindustrial chain division and Chinarsquos opening policyChinarsquos FDI introduction reached new highs filled thegap in capital and technology of the economic devel-opment improved domestic innovation and develop-ment level management efficiency and ecologicalindustrial chain and promoted the selective flow ofcapital and labor between enterprises industries andregions reduced enterprise financing constraints andlabor difficulties and realized the improvement ofcapital allocation efficiency [18] Moreover the essenceof profit-seeking capital requires that when FDI in-creases in a certain field the productivity of relatedindustries in this field will inevitably be improvedpromoting the industrial chain capacity upgrading inthis field eliminating backward industries and pro-duction capacity reducing the blind pursuit of capitalwaste of environmental resources and environmentalregulation costs making the high-tech industry favoredby domestic and foreign capital promoting the ex-tensive research and development and application ofhigh-tech in China and optimizing the market struc-ture [19] A more reasonably optimized capital laborratio and the cost of innovation input will significantlypromote green technology progress and have a positiveeffect on green productivity [20] Based on the previ-ously mentioned analysis the following assumption ismade

Hypothesis 1 FDI is conducive to improving theefficiency level of the green economy

Second scientific and technological progress is a pre-requisite for a countryrsquos industrial structure upgradingand sustainable development A countryrsquos developmentnot only depends on the development of its own sci-entific research and experiment but also needs inter-national exchanges of science and technology FDI is animportant carrier of international science and tech-nology exchanges1e importance of FDI is reflected innot only the rectification of the gap in the hostcountryrsquos capital but also the technology spillover ef-fect industrial correlation effect and competition effecton the host country FDI entering the Chinese marketwill bring domestic advanced science and technologyand management experience 1e transnational flow ofhigh-tech will reduce domestic introduction costs andproduce technology spillover effect and accelerate thepromotion and application of high and new technol-ogies in domestic environmental resources and pro-duction capacity [3] With the industrial correlationeffect when the application of advanced technology isin a link of the industrial chain the chain adjustmentsbefore and after the industrial chain will maintain thetechnical consistency and stability of the industrialchain and promote the more efficient operation ofrelevant industries [4] 1e competitive effect of FDIalso promotes the capital flow On the one hand theincrease of FDI will squeeze out and replace the in-efficient domestic capital and optimize the domesticcapital structure 1e guiding role of FDI will lead thedomestic capital to participate in related industries andenhance the ldquodry middle schoolrdquo effect of domesticcapital On the other hand FDI will enhance thecompetition among industries promote industrialcapacity upgrading improve the utilization rate ofresources and be conducive to the improvement ofresearch and development innovation and technolog-ical imitation ability [21] Based on the previouslymentioned analysis the following assumption is made

Hypothesis 2 FDI can indirectly affect the greeneconomic efficiency through regional innovation

Finally in view of Chinarsquos reform and opening upindividual cities take the lead in opening up and in-troducing foreign investment gradually covering all


regions of the country 1e economic developmentlevel is greatly different in different regions On the onehand the level of economic development is the em-bodiment of the comprehensive strength of industrialstructure financial development level and science andtechnology level 1ere are great differences in pref-erential policies and business environment attractingforeign investment with different economic develop-ment levels Areas with high level of economic devel-opment are more powerful and more attractive toforeign investment 1e inclined areas of foreign in-vestors to choose investment will be affected by the levelof regional economic development forming the ldquopathdependencerdquo and further deepening the differencesbetween the capital accumulation effect and technologyspillover effect of foreign capital in different regionsOn the other hand regions with higher level of eco-nomic development can more efficiently absorbtransform and utilize FDI capital accumulation effecttechnology spillover effect and a series of effects withhigher levels of talents more perfect industrial chainhigher-tech research and development capabilitiesBased on the previously mentioned analysis this paperpresents the following assumption

Hypothesis 3 FDI at different levels of economicdevelopment has a nonlinear impact on green eco-nomic efficiency

4 Model Construction andVariable Description

41 Research Method Currently more scholars use dataenvelopment analysis (DEA) in productivity studies Tone

proposed a super SBM-DEA model based on relaxationvariables for early efficiency measures without consideringthe effects of relaxation variables and the defects actuallyused in production activity [22] 1ereafter Fried and Lovellfurther considered the impact of environmental factorsstatistical noise and random noise on the efficiency eval-uation of DMU and separated them to achieve a moreobjective and accurate measure [23] In conclusion based onthe super SBM-DEA model of undesired output this papercarries out a three-stage processing to measure the efficiencyof green economy in Chinarsquos provinces

411 2ree-Stage DEA Model 1e first stage uses thenonexpected output super SBM-DEA model as the basicmodel to calculate the initial green economic efficiencyvalue Assuming that each region is a decision making unit(DMU) for each region the input variable is typem type T1expected output and type T2 nonexpected output expressedby vectors as x isinRm ygRT1 and yb isinRT2 X Yg and Yb aredefined as matrix X [x1 xn] isinRmlowast n Yg [y1g yng] isinRT1lowast n and Yb [y1b ynb] isinRT2lowast n Any elementin the matrix is greater than 0 1is constructs the collectionof environmental technologies Q (x yg yb) |xgeXλybgeYbλ yg leYgλ λge 0Q is a set of production possibilitiesto measure green economic efficiency where λ is the weightvector When its sum is 1 the scale remuneration is variableotherwise the scale remuneration is unchanged

Adding the undesired output to the super SBM-DEAmodel yields a collection of environmental technologies withthe following

Q x yg y

b1113872 1113873|x 1113944

n

j1ne 0λjxj y

b ge 1113944n

j1ne 0λjyj

b y

g le 1113944n

j1ne 0λjyj

g yj

g ge 0 λge 0⎧⎪⎨

⎪⎩

⎫⎪⎬

⎪⎭ (7)

1e super SBM-DEA model considering nonexpectedoutput is shown as follows

τlowast min(1m) 1113936

mi1 xiXi0( 1113857

(1(T1 + T2)) 1113936T1r1 y

gr y

gr0( 1113857 + 1113936

T2r1 y

bry

br01113872 11138731113872 1113873

st

xge 1113944n

j1ne 0λjxj

yg le 1113944

n

j1ne 0λjy

gj

yb le 1113944

n

j1ne 0λjy

bj

xge x0 yleyg0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(8)


Among them λ is the weight vector and the objectivefunction τlowast ge 1 For a specific evaluated DMU the larger τlowastvalue is the more efficient the evaluated unit is

In the second stage the stochastic frontier model wasestablished considering the impact of environmental fac-tors statistical noise and random noise on green economicefficiency Based on formula (9) the stochastic frontiermodel is used to estimate the impact of each environmentalvariable on the input relaxation variable and test the like-lihood ratio Finally the input of other DMU is adjusted tothe most effective DMU as shown in formula (10)

Tni f Pi αn( 1113857 + cni + μni (9)

Among them i 12 I n 12 N Tni representsthe slack value of the n-th input in the i-th decision-makingunit Pi is the environment variable an is the environmentvariable value cni+ μni is the mixed error items and cni isthe random noise and μni is statistical noise which representthe effects of random noise and statistical noise on the inputrelaxation variables respectively where c obeysN(0 σ2) andμ obeys N+(0 σ2)

xniprime xni + max f Pi 1113954αn( 11138571113858 1113859 minus f Pi 1113954αn( 11138571113864 1113865 + max cni( 1113857 minus cni1113858 1113859

(10)

Among them i 12 I n 12 N xprimeni is theadjusted inputs xni is the preadjusted inputs max [f (Pi

αn_hat)]minus f (Piαn_hat) is adjusting the environmental factorsand max(cni)minus cni applies all the decision units to the samecondition level

1e third stage replaces the adjusted input variables intothe super SBM-DEA model containing nonexpected outputresulting in green economic efficiency values consideringenvironmental factors random noise and statistical noiseas shown in Table 1

412 Generalized Method of Moments Estimation 1eadvantage of dynamic system GMM is that it considers theprocess of dynamic change of variables increases thehorizontal equation participation in estimation correctsthe standard errors of deviation estimator and improvesthe accuracy of estimation [21] 1e model is built asfollows

LnGECMit α + β1LnGECMitminus 1 + β2LnFDIit + β3LnINit

+ β4LnSECDit + β5LnRDit + β6LnGGPAYit + vi + εit

(11)

Among them the subscripts i and t represent theregion and the year respectively vi represents the in-dividual fixation effect εit represents the error termsGECM represents the green economic efficiency FDIrepresents the foreign direct investment IN representsthe regional innovation SECD represents the secondaryindustry added value RD represents the RampD spendingand GGPAY represents the fiscal spending Consideringthe ldquoinertiardquo of economic development the lag phase ofgreen economic efficiency is added to the model To solvethe endogenous problem of introducing lag caused byinterpreted variables its lag term is taken as toolvariables

413 2e Mediation Effect Model Using Wen et al [24] forreference regional innovation is taken as a mediating var-iable 1e model is set as follows

LnGECMit β0 + β1LnFDIit + 1113944j

βjLnControlit + vi + εit

(12)

LnINit β0 + λ1LnFDIit + 1113944jminus 1

βjminus 1LnControlit + vi + εit

(13)

LnGECMit β0 + λ2LnFDIit + αLnINit

+ 1113944jminus 1

βjminus 1LnControlit + vi + εit(14)

Among them the subscripts i and t represent the regionand the year respectively vi represents the individual fix-ation effect εit indicates error terms GECM represents greeneconomic efficiency FDI represents foreign direct invest-ment IN represents regional innovation and Controlrepresents control variables

Table 1 Green economic efficiency

DMU 2010 2014 2017 MeanBeijing 1058 1005 1107 1067Tianjin 0688 0723 1027 0772Hebei province 0640 0666 0611 0651Shanxi province 0604 0619 0550 0602Inner Mongolia 0674 0693 0625 0688Liaoning province 0696 0705 0646 0694Jilin province 0671 0753 0704 0723Heilongjiang province 0689 0729 0682 0713Shanghai 1045 0887 1074 0973Jiangsu province 1065 1104 1085 1085Zhejiang province 0929 0948 0897 0925Anhui province 0707 0781 0733 0763Fujian province 0876 0844 0802 0870Jiangxi province 0863 0870 0822 0904Shandong province 0731 0774 0755 0754Henan province 0600 0636 0598 0616Hubei province 0734 0763 0746 0747Hunan province 0712 0765 0754 0741Guangdong province 1000 1000 1000 1000Guangxi province 1179 0707 0614 0764Hainan province 1328 1289 1420 1375Chongqing 0738 0811 0870 0794Sichuan province 0639 0732 0718 0700Guizhou province 0656 1039 1075 0907Yunnan province 0566 0651 0687 0652Shaanxi province 0687 0921 0805 0848Gansu province 0716 1007 0963 0965Qinghai province 1157 1213 1236 1211Ningxia province 1017 0872 0845 0906Xinjiang province 0778 0767 0671 0751Due to space limitation only green economic efficiency of some years wasincluded


4142e Dynamic Panel2resholdModel In order to studywhether FDI has a heterogeneity impact on green economicefficiency due to the change of economic development levelthe dynamic panel threshold model proposed by Caner and

Hansen is used for the empirical test [25] 1e advantage isthe assumption that explanatory variables are strictly ex-ogenous considering the endogenous and individual fixa-tion 1e model is set as follows

LnGECMit α + β2LnFDIitI LnGDPit lt λ1( 1113857 + β3LnFDIitI λ1 le LnGDPit le λ2( 1113857

+ β4LnFDIitI LnGDPit gt λ2( 1113857 + β5LnControlit + vi + εit(15)

Among them the subscripts i and t represent the regionand the year respectively vi represents the individual fix-ation effect εit represents the error term λ represents thethreshold value and LnGDP is the pair value of the regionalactual GDP and the threshold variable Control represents

the control variable and LnFDI represents the pair value ofthe actual amount of foreign capital 1e problem related toerror term sequence in individual effect elimination (15) usesforward orthogonal discrepancy transformmethod [26] Weconverted formula (15) to

LnGECMlowastit α + β2LnFDIlowastitI LnGDPit lt λ1( 1113857 + β3LnFDIlowastitI λ1 leLnG DPit le λ2( 1113857

+ β4LnFDIlowastitI LnGDPit gt λ2( 1113857 + β5LnControllowastit + vlowast

i + εlowastit(16)

1e fitting value of LnGECMlowastit is calculated and thensubstituted into (15) to obtain the threshold fitting valueFinally the threshold effect is verified based on the thresholdfitting value

42 Description of the Variables

421 Variable Selection for the 2ree-Stage DEA ModelExpected output is measured by actual GDP Unexpectedoutput is borrowed from Yang and Hu [27] Using themethod the five pollutants are as follows industrialwastewater discharge industrial sulfur dioxide emissionsmoke (powder) dust emission industrial solid waste pro-duction and nitrogen oxide emission 1e entropy methodof the previously mentioned five pollutants is used to build acomprehensive index of environmental pollution as thenonexpected output 1e input variables are divided intolabor input capital input and energy investment respec-tively measured by the total number of three industrialemployees at the end of the year capital stock and energyconsumption [28] 1e capital stock data of each provinceover the years are calculated by the perpetual inventorymethod [29] 1e selection basis of environmental variablesis able to affect the economy and pass the LR test [30] 1ecomprehensive empirical test results are measured by theurbanization rate and the number of patent applications①

422 Variable Selection of Dynamic System GMM andDynamic 2reshold Model 1e interpreted variable is thegreen economic efficiency (GECMit) based on productionefficiency Consider the quality and quantity of economicgrowth and measure the relationship between input ex-pected output and nonexpected output in production ac-tivities 1e core interpretation variable is foreign directinvestment (FDIit) measured by the actual amount of

foreign investment Regional innovation (INit) is measuredby the number of patent applications 1e control variablecontains the financial expenditure (GGPAYit) measured bythe total fiscal expenditure 1e value of secondary industry(SECDit) is measured by the added value of the secondaryindustry RampD expenditure (RDit) is measured by RampDexpenditure Green economic efficiency lags behind phase 1(GECMitminus 1) measured by lagging efficiency of greeneconomy 1e threshold variable is the level of economicdevelopment (GDPit) measured by the actual GDP value

For descriptive data statistics as shown in Table 2logarithms of measurement model variants are taken toreduce the effect of heterovariance 1e data comes fromChina Statistical Yearbook China Environmental StatisticalYearbook the provincial Statistical Yearbook and the Na-tional Bureau of Statistics Due to the change of somepollutant statistical caliber and project occurring in 2017the study sample period was selected from 2010ndash2017 Datafrom Taiwan and Tibet is missing so they were not includedin the investigation scope

5 Empirical Test and Analysis

51 Results and Analysis of Green Economic EfficiencyTable 1 and Figure 1 show the efficiency value of greeneconomy in China

Table 1 shows that the mean green economic efficiency of1 and above are Hainan Beijing Jiangsu and Guangdongrespectively and the mean green economic efficiency is1375 1067 1085 and 1000 respectively 1e mean greeneconomic efficiency in the bottom provinces are Henan andShanxi and the mean green economic efficiency is 0616 and0602 respectively 1e samples are further divided intoeastern central and western regions and significant re-gional differences are found compared with the mean re-gional green economic efficiency (Figure 1) 1e national


average green efficiency of the economy is between 0810 and0850 showing the overall trend of stable and risingAccording to the subregional comparison the average ef-ficiency of eastern green economy is the largest between0888 and 0936 and is located above the national averagelevel of green economy efficiency 1e efficiency value ofgreen economy in western China is between 0773 and 0895Before 2012 the green economy efficiency level of thewestern region was lower than the national average After2012 the efficiency level of green economy was higher thanthe national average and showed a stable trend 1e greeneconomic efficiency value of the central region is between0690 and 0745 indicating that the green economic effi-ciency in the central region has great potential forimprovement

52 Benchmark Regression Test and Analysis To verify theimpact of FDI on the green economic efficiency the dynamicsystem GMM model is used for empirical tests as shown inTable 3 From the test results the P value of the AR(2)estimation coefficient is greater than 005 indicating thatthere is no second order autocorrelations in the residualsequence 1e P value of the Hansen statistics is greater than

005 accepting the original assumption that the tool variableis reasonably valid 1e estimation coefficients ofLnGECMitminus 1 are all less than the estimation coefficient of thelag term in column 1 that has the mixed regression resultsand greater than the lag term estimation coefficient incolumn 2 that has the fixed effect results indicating that theGMM estimation results are robust and effective

From columns 3 to 5 in Table 3 the following is con-cluded 1e coefficient of LnGECMitminus 1 is significantly pos-itive indicating that the efficiency of Chinarsquos early greeneconomic efficiency is sustainable and significantly posi-tively promoting the green economic efficiency in the future1e coefficient of LnSECDit is robust and significantlynegative indicating that the secondary industry plays anegative role on green economic efficiency 1e reason isthat the nonexpected output mostly comes from the sec-ondary industry 1e secondary industry leads to the ex-cessive increase of pollutant emissions while promotingeconomic growth which is not conducive to the greendevelopment of economy Add the LnFDIit LnINit in thecolumns 3 and 4 respectively and add both LnFDIit andLnINit items to column 5 for verification It can be foundthat the regression coefficient of LnFDIit is always signifi-cantly positive indicating that FDI can significantly promotethe improvement of the efficiency of green economy whichpartially confirms the ldquopollution halordquo hypothesis Mean-while the estimation coefficient of LnINit is always signif-icantly positive suggesting that regional innovation can alsopromote growth in green economic efficiency 1e reasonsare as follows First the number of patent applications is theembodiment of high-tech innovation achievements whichcan directly improve production efficiency and achieve thegrowth of industrial competitiveness which plays an im-portant and positive role in the economy Second is theinnovation-driven development 1e wide application ofhigh-tech can drive the transformation of industrial struc-ture from low level to high level enhance the correlationeffect between industries and promote the harmless treat-ment of waste generated in production activities so as torealize the green development of economic economy Inconclusion the test results verify hypothesis 1 of this paper

Table 2 Variable descriptive statistics

Variables Number of observations Minimum value Maximum value Mean Standard deviationExpected output 240 135043 7997642 2057187 mdash

Unexpected output 240 645564 22000000 5759517 mdash240 30765 676700 273528 mdash

Investment variable 240 1057900 25000000 8130636 mdash240 135851 3889925 1455174 mdash

Urbanization rate 240 3381 8960 5593 mdashNumber of patent applications 240 60200 62783400 7605188 mdashLnGECMit 240 minus 023 052 le0001 010LnFDIit 240 minus 004 774 543 165LnINit 240 640 1335 1037 146LnGGPAYit 240 632 962 818 059LnSECDit 240 635 1059 892 092LnRDit 240 1096 1676 1413 133LnGDPit 240 963 085 721 1129

095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest

Figure 1 Green economic efficiency trend


53 Regional Heterogeneity Test and Analysis It has beenverified that FDI has a positive impact on Chinarsquos overallgreen economic efficiency 1is section divides China intoeastern central and western regions to further examine the

possible heterogeneity impact of FDI on the green economicefficiency of the east center and west 1e empirical resultsare shown in Table 4 Both the AR(2) and Hansen tests havepassed the test so the empirical results are robust andeffective

According to the test results of columns 1 and 2 inTable 4 the estimated coefficient of LnFDIit in the easternregion was 00345 and passed the significance level test of10 while the estimated coefficient of LnFDIit in the centralregion was 01441 and passed the significance level test of 1According to column 3 the estimated coefficient of LnFDIitin the western region is 00342 but it fails to pass the sig-nificance test It can be seen that FDI has a positive andsignificant promoting effect on the green economic effi-ciency in the eastern and central regions of China while thepositive effect on the green economic efficiency in thewestern regions is not significant indicating that FDIrsquospromoting effect on the green economic efficiency hassignificant regional heterogeneity Further investigation ofthe number of FDI patent applications and industrialstructure in the eastern central and western regions showsthat the average value of FDI in the eastern region is 84billion yuan the average number of patent applications is137249 and the average value added of the secondary in-dustry is 14790 billion yuan In the central region the av-erage FDI was 443 billion yuan the average number ofpatent applications was 41508 and the average value addedof the secondary industry was 10334 billion yuan In thewestern region the average FDI was 139 billion yuan theaverage number of patent applications was 28998 and theaverage value added of the secondary industry was 5247

Table 3 Benchmark regression test

Variables PLS PLS_FE GMM GMM GMM1 2 3 4 5

LnGECMitminus 108749lowastlowastlowast 03441lowastlowastlowast 06892lowastlowastlowast 06340lowastlowastlowast 05170lowastlowastlowast[2968] [557] [736] [516] [516]

LnFDIit00029 minus 00075 00669lowastlowastlowast mdash 00587lowastlowast[058] [minus 060] [306] mdash [238]

LnINit00227lowastlowastlowast minus 00531lowastlowast mdash 00507lowastlowastlowast 00394lowast[260] [minus 200] mdash [378] [173]

LnSECDitminus 00478lowastlowastlowast 01285 minus 01456lowastlowastlowast minus 01019lowastlowastlowast minus 01792lowastlowastlowast[minus 306] [138] [minus 374] [minus 311] [minus 424]

LnRDit00034 00011 00006 00014 minus 00001[094] [032] [011] [034] [minus 003]

LnGGPAYitminus 00033 minus 00064 00472 minus 00026 00253[minus 015] [minus 010] [118] [minus 007] [057]

CONS 01334 minus 06437 04876lowastlowast 0324 05823lowastlowast[108] [minus 162] [225] [156] [241]

AR(2) mdash mdash 02100 minus 03000 minus 00700mdash mdash (0834) (0766) (0945)

Hansen mdash mdash 257700 245500 237100mdash mdash (0174) (0219) (0208)

Individual effects mdash Control Control Control Control

F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)

(1)1e values in [] are t statistical value of regression coefficient in the same table below the values in () are P value of corresponding test statistics in the sametable below (2) lowastlowastlowast lowastlowast and lowast indicate significant significance at 1 5 and 10 respectively All tables are the same

Table 4 Empirical test of regional heterogeneity

Interpretation variables East Center West1 2 3

LnGECMitminus 106397lowastlowastlowast 05934lowastlowastlowast 09231[319] [428] [128]

LnFDIit00345lowast 01411lowastlowastlowast 00342[210] [351] [020]

LnINit00362lowast minus 00175 00443[207] [minus 123] [022]

LnSECDitminus 00852lowastlowast 00024 minus 01450[minus 246] [002] [minus 040]

LnRDitminus 00011 minus 00136lowast minus 00192[minus 014] [minus 207] [minus 025]

LnGGPAYit00028 minus 03051lowast minus 00414[010] [minus 209] [minus 009]

CONS 01084 19121lowastlowastlowast 12626(0011) [339] [025]

AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)

Individual effects Control Control Control

F 578200 122600 56600(0000) (0001) (0019)


billion yuan In the comprehensive analysis the reasons forthe regional heterogeneity impact of FDI on green economyefficiency are as follows First of all the economic devel-opment level in the eastern and central regions is higher theindustry sector development is relatively perfect and thetransformation of science and technology ability is strongwhich is able to advance the experience of the foreign sideand introduce high and new technology to efficiently absorbtransformation promote regional technology progressimprove the utilization ratio of capital and better achieve theefficiency of the green economy growth Second the numberof foreign capital in the eastern and central regions is rel-atively sufficient Due to the limitation of geographical lo-cation and the small stock of FDI in the western region it isdifficult to produce an effective capital accumulationMeanwhile the estimated coefficient of LnINit in the easternregion is significantly positive while the central and westernregions are not significant as a whole indicating that theinnovation in the eastern region has a stronger role inpromoting the efficiency of green economy

6 Empirical Test of Influence Mechanism

61 Mediating Effect Test and Analysis 1e test results areshown in Table 5 It can be seen from column 1 that theregression coefficient of LnFDIit is 00669 and has passed the1 significance test From column 2 the regression coef-ficient of LnFDIit is 03037 and has passed the 1 signifi-cance test 1e results indicate that FDI has a significantpositive promotion effect on regional innovation Fromcolumn 3 the regression coefficient of LnINit is 00394 andhas passed the 10 significance level test indicating that themediating effect of regional innovation is significant 1ereasons are as follows On the one hand the technologyspillover effect of FDI promotes the scientific and techno-logical innovation research and development among in-dustries through capital flow personnel factor flow andtechnology exchange and promotes the popularization ofhigh and new technologies so as to realize the positivepromotion of green economic efficiency On the other handFDI into the domestic market will cause competition effectand demonstration effect especially for domestic high-techenterprises advanced production technology to squeezedomestic enterprises forcing domestic high-tech enterprisesto imitate foreign enterprises to increase the independentscientific research and innovation experiment and improveindependent innovation ability so as to promote thetechnological progress of the whole region [31] Finally itcan be seen from column 1 that the regression coefficient ofLnFDIit is significant and from column 3 that the regressioncoefficient of both LnFDIit and LnINit is significantly pos-itive which verifies the significance and robustness of themediation effect In conclusion the test results verify hy-pothesis 2 of this paper

62 2reshold Effect Test and Analysis To further verifywhether the impact of FDI on green economy efficiencychanges with the level of economic development this section

will build a dynamic panel threshold model for empiricaltesting

Repeat 400 calculation threshold F inspection andconfidence interval by self-inspection method and the re-sults are shown in Table 6 It is seen from Table 6 that boththe single threshold value and the double threshold valuesare significant and the three threshold values do not pass thesignificance test 1e single threshold is included in thedouble threshold In order to ensure the validity of thethreshold value the threshold value is tested and thelikelihood ratio test results (Figure 2) can know that thedouble threshold values are valid so the double panelthreshold model is selected

1e test results are shown in Table 7 According to thethreshold value it is seen from Table 7 that when thethreshold is less than 8748 the estimated coefficient of FDIis minus 00554 and has passed the 1 significance level testindicating that FDI has a negative impact on green economicefficiency When the threshold is between 8748 and 9373the estimated coefficient of FDI is 00347 failing the sig-nificance test When the threshold is greater than 9373 theestimated coefficient of FDI is 00628 and has passed the 1significance level test indicating that FDI can positivelypromote green economic efficiency Further comparison ofthe interval regression samples shows that the western re-gion occupied the vast majority of the samples where theeconomic level was under the first threshold 1e samplesbetween the double thresholds are samples of the westernand central regions mainly Shanxi Inner Mongolia JilinHeilongjiang Guizhou Yunnan Shaanxi Gansu andXinjiang 1e sample above the second threshold containsthe entire eastern a few central and a very few westernregions In addition compared with the number of foreign

Table 5 Empirical test of the mediating effect

Variables 1 2 3LnGECMit LnINit LnGECMit

LnFDIit00669lowastlowastlowast 03037lowastlowastlowast 00587lowastlowast[306] [434] [238]

LnINitmdash mdash 00394lowastmdash mdash [173]

LnGECMitminus 106892lowastlowastlowast 11262 05170lowastlowastlowast[736] [148] [516]

LnSECDitminus 01456lowastlowastlowast 04393lowast minus 01792lowastlowastlowast[minus 374] [192] [minus 424]

LnGGPAYit00472 10455lowastlowastlowast 00253[118] [324] [057]

LnRDit00006 00071 minus 00001[011] [038] [minus 003]

CONS 04876lowastlowast minus 36150lowastlowast 05823lowastlowast[225] [minus 272] [241]

AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)


investment the number of patent applications and thesecond output value of different sections it is seen that inthe areas with the economic level below the first thresholdthe corresponding average number of foreign investment is37 billion yuan the average number of patent applications is3771 and the average output value of the secondary industryis 1455 billion yuan When economic level is between thedouble thresholds the average number of foreign capital is144 billion yuan the average patent applications is 15659and the average output value of the secondary industry is4705 billion yuan For areas with economic level higher thanthe second threshold the corresponding average number offoreign capital is 689 billion yuan the average number ofpatent applications is 104269 and the average output valueof the secondary industry is 13751 billion yuan In

conclusion regions with economic level higher than thesecond threshold value have more sufficient scale of foreigncapital and stronger regional innovation ability It can beinferred that the heterogeneity impact of FDI on greeneconomic efficiency at different economic levels is as followsFirst the insufficient number of FDI in the middle and lowerreaches of the economic level leads to the insignificantpromotion effect of FDI capital accumulation effect Secondthe demonstration and guiding role of foreign investment inthe middle and downstream economic areas is weak 1ereare few high-tech talents in these areas and the productiontechnology is relatively backward so the technology spill-over cannot absorb and transform foreign investment in atimely and effective manner To sum up the test resultsverify hypothesis 3 of this paper

Table 6 1e threshold value estimation and test

1e threshold is set up variables Number of thresholds 1reshold 1e F value 10 5 1 1e 95 confidence interval

LnGDPitSingle 8748 429450lowastlowastlowast 8484 15724 32288 [87488819]Double 8748 9373 202960lowastlowastlowast 3474 4628 7604 [87488819] [92509373]1ree 9118 00000 0000 0000 0000 [90279124]

25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11

Figure 2 1e LR test results of the double threshold values

Table 7 Empirical test of the threshold effect

Variables 1 2 31reshold variable LnGDPitlt 8748 8748le LnGDPitle 9373 LnGDPitgt 9373

LnFDIitLnGDPitlt 8748 8748le LnGDPitle 9373 LnGDPitgt 9373

minus 00554lowastlowastlowast 00347 00628lowastlowastlowast

LnGECMitminus 1[minus 297] [060] [567]03135lowastlowastlowast 01832 03468lowastlowastlowast

Control variable [280] [027] [754]Individual effects Control Control Control

Sargan Yes Yes Yes197496 11150 161430

AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500

When the LnGDPitlt 8748 since the two-step estimator variance-covariance matrix is not full rank the one-step estimation is taken and the results are stillrobust and without the AR(2) test


63 Test of Robustness To further test the robustness of thethreshold results this paper draws on the robustness testmethod of Li et al [32] 1e panel threshold model is similarin a sense to adding virtual variables which is structurallysimilar to the cross term model where the panel thresholdmodel can get exactly the structural change point and thecross term cannot 1e model set below is to assume thenonlinear influence of FDI on green economic efficiency andthen introduce the quadratic term of FDI set as follows

LnGECMit α + β2FDI2it lowastLnFDIit + β3FDIit lowast LnFDIit

+ β4LnControlit + vi + εit

(17)

In (17) the meaning of each variable remains unchangedfrom the previous one in this paper and the marginal effectof LnFDIit on LnGECMit can be obtained

zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)

In conclusion if β2 and β3 can pass the significance testit is verified that there is a nonlinear relationship between theeffect of FDI on green economic efficiency

1e test results are shown in Table 8 and the test resultsshow that the GMM estimation result of the cross model isreasonable and effective [33] First both the quadratic andprimary term coefficients in the model are significantconfirming the nonlinear relationship between FDI and thegreen economic efficiency and further confirming the ro-bustness of the threshold effect results [34]

7 Research Conclusions andPolicy Implications

At the time of the transformation of Chinarsquos old drivers ofeconomic growth improving the level of foreign investmentand domestic economic allocation is an important directionfor improving the level of participation in the internationalcycle and an important link in the process of building a newpattern of development in which domestic and internationalcycles mutually promote Based on the basis of existingresearch results the influence mechanism of FDI and re-gional innovation on green economic efficiency we calcu-lated the basis of 30 provincial-level panel data from 2010 to2017 tested the driving effect of FDI regional innovation ongreen economic efficiency and tested the driving status ofFDI on green economic efficiency by regions On this basis

the intermediary effect model is constructed to test theinfluence mechanism of regional innovation as the inter-mediary variable on this driving action 1e dynamic panelthreshold model was used to examine the influence of FDIon green economic efficiency under different levels ofeconomic development 1e main research conclusions areas follows (1) FDI has a significant driving effect on greeneconomic efficiency Further regional heterogeneity analysisfound that FDI has a significant positive driving effect ongreen economic efficiency in the eastern and central regionsand the driving effect in the western region is not significant(2) 1e analysis of the influence mechanism has found thatthe role path of regional innovation is significantly positiveand FDI can improve the efficiency of green economy bypromoting regional innovation FDI has a significant ef-fective double threshold for the level of economic devel-opment When the economic level is below the firstthreshold value FDI promotes the green economic efficiencybetween the first threshold value and the second thresholdvalue when the economic level crosses the second thresholdvalue FDI has a significant positive effect on the greeneconomic efficiency [35]

Based on the previously mentioned conclusions thispaper puts forward the following two policy inspirations (1)At present China should increase the policy support forintroducing foreign investment in backward regions andcoordinate the reasonable allocation of foreign investment invarious regions At present there are great differences in theamount of foreign investment in various provinces andcities In order to promote the rational allocation of foreigncapital and further promote regional coordination and greendevelopment the government should adjust the introduc-tion policy and intensity of foreign investment on the basisof opening up combined with the characteristics of regionaldistribution and the level of economic development Forprovinces and cities with low economic level the govern-ment needs to formulate more attractive and biased policiesto attract foreign capital and at the same time improve thesupporting facilities of regional economic developmentrealize the improvement of regional comprehensive eco-nomic strength make foreign capital attract stay and livefor a long time and strengthen the positive effect of thegreen economic efficiency of the region For provinces andcities with high economical level the governmentrsquos in-vestment policy needs to take dimension and stability as themain goal and stabilize the introduction of foreign in-vestment to continue to play the role of FDI in promotingthe green economic efficiency (2) 1e regional innovationand the transformation of original achievements should beencouraged From the perspective of regional innovationinnovation is conducive to the improvement of greeneconomy efficiency and FDI can improve the efficiency levelof green economy by promoting regional innovation 1egovernment should improve the support of new technologyand new knowledge innovation policy strengthen thepertinence and support of regional foreign investment solvethe shortage of enterprise research and development fundshigh and new technology efficiency and achievementtransformation difficulties realize science and technology

Table 8 Robustness test results

Variables GMM Variables GMM

LnGECMitminus 104157lowastlowast AR(2) minus 05900[207] (0554)

FDIit2lowast LnFDIitminus 567eminus 08lowast Hansen 211100[minus 171] (0133)

FDIitlowast LnFDIit00001lowast F 95300[191] (le0001)

Control variable Control mdash mdash


from ldquofollowrdquo to ldquoand runrdquo to the hierarchy of ldquoleadingrdquochange and further strengthen the FDI and regional in-novation promoting green economic efficiency

8 Research Deficiencies and Prospects

(1) 1is paper measures the impact of FDI capital onChinarsquos green economic efficiency from the per-spective of factor input and uses the three-stage DEAmodel to put forward the impact of environmentalfactors statistical factors and other factors which isa relatively cutting-edge exploration Due to theparticularity of research perspective and level thecontinuity and timeliness of environmental pollutantdata published in relevant databases are limitedwhich makes it difficult to timely and comprehen-sively reveal the recent changes of FDI on Chinarsquosgreen economy efficiency After the database updatedata and the statistical classification of core indus-tries of digital economy are officially published in thefuture more samples and perspectives can be usedfor empirical testing from a more accurate digitalindustry classification

(2) Green economic efficiency has rich practical con-notation 1is paper makes a preliminary explora-tion from the threshold effect model andintermediary effect model by using the provincialpanel data 1e follow-up research can also startfrom the municipal panel data and use the spatialeconometric model to verify the spatial spillovereffect of FDI on green economic efficiency from theperspective of spatial correlation and explorewhether there is a ldquoclub agglomeration phenome-nonrdquo between FDI and green economic efficiency inspace so as to enrich the perspective of the impact ofFDI on green economic efficiency

Data Availability

1e data used to support the findings of this study are in-cluded within the article

Conflicts of Interest

1e authors declare that there are no conflicts of interestregarding the publication of this paper

Acknowledgments

1is study was funded by the Major Projects of the NationalSocial Science Fund (15ZDA009) Anhui Natural ScienceFoundation Project (1808085MG218 1908085MG231)Support Plan for Outstanding Young Talents in Colleges andUniversities in 2019 (gxyq2019024) 2020 Anhui ProvincialFederation of Social Sciences Research Projects(2020CX066) and Anhui University of Finance and Eco-nomics School-Level Scientific Research Fund Project(ACYC2019154)

References

[1] M Li and S C Liu ldquoRegional differences and threshold effectof reverse technology overflow in FDI-threshold regressionanalysis based on China interprovincial panel datardquo Man-agement World vol 66 no 1 pp 21ndash32 2012

[2] J L Liu ldquo70 Years of utilizing foreign investment in newChina history effect and main experiencerdquo ManagementWorld vol 35 no 11 pp 19ndash37 2019

[3] Z P Zhang ldquoFDI facilitate the empirical study of effi-ciencymdashbased on a sample of 285 prefecture-level cities inChinardquo International Business (Journal of the University ofForeign Economics and Trade) no 1 pp 85ndash97 2018

[4] J Li and Y Tang ldquoTrade opening FDI and total factorproductivityrdquoMacroeconomic Studies no 9 pp 67ndash79 2019

[5] Y Q He ldquoOpening and TFP growth experience researchbased on Chinardquo Economics (Quarterly) no 4 pp 1127ndash11422007

[6] L J Sun ldquoFinancial development FDI and economicgrowthrdquo Quantitative Economic Technical and EconomicResearch no 1 pp 3ndash14 2008

[7] X S Gong and Y C Li ldquoFDI entry speed scale stock andregional innovation efficiencymdashan empirical analysis basedon the panel threshold modelrdquo Industrial Technical Economyvol 38 no 10 pp 83ndash91 2019

[8] J Friedmann ldquo1e World city hypothesisrdquo Development andChange vol 17 no 1 pp 69ndash83 1986

[9] J Z Xu F S Li and J H Huang ldquoForeign trade promotesChinarsquos regional economic growthmdashbased on FDIrdquo Man-agement Modernization vol 38 no 2 pp 21ndash24 2018

[10] C Z Fan Y F Hu and H L Zheng ldquo1eoretical andempirical study of FDIrsquos impact on technology innovation indomestic enterprisesrdquo Economic Studies no 1 pp 89ndash1022008

[11] R Wang B Yan and W G Deng ldquoFDI on Chinarsquos industrialindependent innovation ability and mechanismmdashbased onthe perspective of industrial correlationrdquo China IndustrialEconomy no 11 pp 16ndash25 2010

[12] D B Zhu and L Ren ldquoEnvironmental regulation foreigndirect investment and Chinese industrial green transforma-tionrdquo International Trade Issues no 11 pp 70ndash81 2017

[13] Z H Yang and L Tian ldquoChinese interprovincial study of theldquopollution heavenrdquo hypothesis and influencing factorsrdquo 2eWorld Economy vol 40 no 5 pp 148ndash172 2017

[14] L Wang J X Yu and Y H Shao ldquoForeign direct investmentand regional green total factor efficiencyrdquo Financial Researchvol 30 no 10 pp 17ndash30 2019

[15] F Y Liu and A Q Zhao ldquoEffect test of foreign direct in-vestment on urban environmental pollutionmdashan empiricalstudy based on the panel data of 285 cities in Chinardquo In-ternational Trade Issues no 5 pp 130ndash141 2016

[16] R Y Huang and W J Zhou ldquoWill foreign direct investmentcause environmental pollution in the host countrymdashtheempirical evidence from the Yangtze River economic beltrdquoContemporary Financial Studies no 5 pp 41ndash53 2019

[17] Z Y Ji J Mao and X F Lai ldquoEffect of FDI on environmentalpollution in Chinamdashbased on the empirical test of 30 pro-vincial panel data modelsrdquo World Economic Studies vol 128no 3 pp 56ndash64 2015

[18] G W Cai and H Yang ldquoForeign direct investment canimprove Chinarsquos market factor distortionrdquo China IndustrialEconomy no 10 pp 42ndash60 2019


[19] G Li and X Fan ldquoTrade opening foreign direct investmentand green total factor productivityrdquo Journal of Nanjing AuditUniversity vol 16 no 4 pp 103ndash111 2019

[20] B R Cao and S H Wang ldquoOpening up international tradeand green technology progressrdquo Journal of Cleaner Produc-tion vol 142 no 2 pp 1002ndash1012 2016

[21] K J Yang and C Z Min ldquoResearch on the driving role oftechnological innovation on the quality of economicgrowthmdashtakes the Guangdong-Hong Kong-Macao greaterbay area as an examplerdquo Contemporary Economic Manage-ment vol 41 no 12 pp 29ndash37 2019

[22] K Tone ldquoA slacks-based measure of super-efficiency in dataenvelopment analysisrdquo European Journal of Operational Re-search vol 143 no 1 pp 32ndash41 2002

[23] H O Fried C A K Lovell S S Schmidt and S YaisawarngldquoAccounting for environmental effects and statistical noise indata envelopment analysisrdquo Journal of Productivity Analysisvol 17 no 1-2 pp 157ndash174 2002

[24] Z L Wen L Zhang K T Hou and H Y Liu ldquoInterme-diation effect testing procedure and its applicationrdquo Psy-chological Journal no 5 p 614 2004

[25] M Caner and B E Hansen ldquoInstrumental variable estimationof a threshold modelrdquo Econometric 2eory vol 20 no 5pp 813ndash843 2004

[26] S Kremer A Bick and D Nautz ldquoInflation and growth newevidence from a dynamic panel threshold analysisrdquo EmpiricalEconomics vol 44 no 2 pp 1ndash18 2012

[27] L Yang and X Z Hu ldquoRegional difference and convergenceanalysis of China green economy efficiency based on DEArdquoEconomist no 2 pp 46ndash54 2010

[28] Q L Mao and B Sheng ldquoEconomic opening regional marketintegration and total factor productivityrdquo Economics (Quar-terly) vol 11 no 1 pp 181ndash210 2012

[29] J Zhang G Y Wu and J P Zhang ldquoEstimation of Chinainterprovincial material capital stock 1952ndash2000rdquo EconomicStudies no 10 pp 35ndash44 2004

[30] S D Guo M Tong J Guo and Y Han ldquoInterprovincial realenvironmental efficiency measurement and influactors anal-ysis based on three-stage DEA modelrdquo China PopulationResources and Environment no 3 pp 106ndash116 2018

[31] X Peng ldquoEconomic growth effect of foreign direct investmentin Chinese high-tech industriesrdquo Journal of Shanxi Universityof Finance and Economics vol 31 no 3 pp 58ndash62 2009

[32] Z G Li Q Y Wang J S Ba and J Li ldquo1e ldquothreshold effectrdquoin the relationship between investment and credit a newperspective of investment growthrdquo Financial Research no 5pp 84ndash101 2010

[33] Y-Q Wu H-X Lu X-L Liao and J-M Zhu ldquoResearch onthe digitization of manufacturing will enhance the compet-itiveness of the value chain based on advantage comparisonrdquoComplexity vol 2021 Article ID 9917772 15 pages 2021

[34] F Xu L-Y Mo H Chen and J-M Zhu ldquoGenetic algorithmto optimize the design of high temperature protective clothingbased on BP neural networkrdquo Frontiers in Physics vol 9Article ID 600564 6 pages 2021

[35] J-M Zhu L Wang and J-B Liu ldquoEradication of ebola basedon dynamic programmingrdquo Computational and Mathemat-ical Methods in Medicine vol 2016 Article ID 15809179 pages 2016


What is the impact of FDI on green economic efficiencyheterogeneous A series of problems can provide new en-lightenment for Chinarsquos more reasonable and efficient allo-cation of FDI under the new development pattern

2 Literature Review

1e influence of FDI on the host country has been a key areaof scholars Scholars have discussed the impact of FDI on theeconomic growth resources and environment of the hostcountry from different perspectives mainly with the fol-lowing views (1) FDI can significantly promote the eco-nomic growth of the host country Some scholars believe thatthe most direct effect of FDI on the host country is theaccumulation effect of material capital and the lack of fi-nancial support in the early stage of economic developmentleads to the obstruction of regional development 1e in-troduction of FDI can provide capital for the infrastructureconstruction of the host country improve the capital uti-lization rate and facilitate the industrial structure upgrading[3] Second FDI is the main channel for the cross-borderflow of high and new technology It can promote the im-provement of personnel factors through technology spillovereffect and industry correlation effect make up for the lack ofpersonnel factors in host countries [4] and significantlyimprove the total factor productivity of host countries [5](2) 1e FDI is not conducive to the economic growth of thehost country Relevant studies believe that the large influx ofFDI is one of the reasons for the negative economic growthof the host country which is due to the competition andsubstitution effects between FDI and the domestic capital ofthe host country Initially the introduction of FDI in thehost country will increase its total capital amount when thecontinuous increase of FDI will cause its substitution effectand crowding effect gradually significant making the totaldomestic capital tends to decline eventually resulting in thehost country capital contraction [6]1ere is also a thresholdvalue for the FDI own scale 1e FDI scale is less than thethreshold value and the positive effect on the innovationefficiency when it exceeds the threshold value is not sig-nificant or even has a negative impact [7] For developingcountries excessive reliance on FDI in the early stage ofeconomic development leads to urbanization attached tothe industrialization process resulting in lag or excessiveurbanization [8] Second the technical spillover effect of FDIis not idealized for the input host country 1e main reasonis that it is difficult for the employees of domestic enterprisesin the host country to accept the advanced technology andmanagement experience of foreign enterprises In recentyears foreign-owned enterprises in sole proprietorship orholding form have increased restrictions on technologyspillover reducing the marginal contribution of FDI toeconomic growth [9] 1e spillover effect of FDI is alsolimited by the host countryrsquos own policy environmenteconomic development level human capital level and otherfactors When the policy environment is conducive to theintroduction of foreign investment and the high level ofeconomic development the positive spillover effect of for-eign investment will be strong 1ird as the host country

attaches great importance to high-tech research and de-velopment it promotes the transformation of domesticproduction technology so that the technology spillovereffect of FDI will be further reduced Moreover the tech-nology transfer effect of foreign capital is relatively strongWhen the development of foreign capital in the host countryis hindered it will promptly change the investment envi-ronment At this time the negative effect of FDI will becomemore significant [10] Finally relevant research shows thatthe industrial correlation effect of FDI does not systemati-cally form a positive spillover of upstream and downstreamenterprises but will inhibit the enthusiasm of related en-terprises and leads to the vicious competition [11] (3)Regarding the ldquoPollution of Heavenrdquo hypothesis somescholars believe that FDI will aggravate environmentalpollution in the host country and exchange ldquogreen watersand green mountainsrdquo for economic growth Relevant re-search shows that in order for developing countries to solvethe problem of lack of capital and technology in the earlystage of economic development they will reduce environ-mental regulatory standards to attract foreign capital intotheir own market resulting in excessive exploitation andutilization of domestic natural resources [12] 1e contin-uous reduction of environmental standards will form theldquoenvironmental bottom line competitionrdquo phenomenon1eFDI enters the host country without idealized high-tech in-dustry mostly concentrated on high pollution and highenergy consumption industries and forming the occupationof host natural resources and is not conducive to guide thedevelopment of host green industry but intensifies the en-vironmental pollution problem [13] (4)1e ldquoPollution Aurardquohypothesis states that the effects of FDI on technologyspillover industrial correlation and market competition inthe host country will promote the technological progress ofthe host country improve the utilization of production factorsand reduce pollution emissions and facilitate the treatment ofresource utilization and environmental pollution problems inthe host country1e main reason is that developed countriesare more stringent standards for waste discharge from en-terprises and more perfect environmental regulation stan-dards So FDI has more advanced production technology andpollutant treatment technology which can improve the re-source utilization rate and reduce the damage to the envi-ronment In addition the imitation of domestic enterprises inthe host country forms the improvement of the productionlevel and harmless waste treatment technology in the hostcountry 1e specific study is as follows Wang et al researchfound that FDI significantly promotes the regional green totalfactor efficiency by improving the input efficiency of labor andenergy [14] Liu and Zhao started from the three industrialwastes and found that FDI will effectively encourage thetreatment of traditional pollutants especially for sulfur di-oxide and industrial wastewater [15] Huang and Zhou an-alyzed the Yangtze River Economic Belt and found that FDIcan effectively inhibit wastewater discharge and promote theoptimization of environmental resources [16]

In conclusion the existing literature has recognized theeconomic and environmental impact of FDI and explored itwhich has an important inspiration for this paper1is paper







βit (1)




minus ρit( 1113857

minus (mρ) (2)






minus ρ1113858 1113859

minus (mβρ)

(4)




minus ρ1113858 1113859 (5)





c2

c1 + c2Lnk minus

mβρ

c22

c1 + c2( 11138572(Lnk)

2minus

mβρ

c2

c1 + c2(Lnk)

2+ αLnINit

+mβρ

c1

c1 + c21 minus

2c2


minusmβρ

c1

c1 + c21 minus

c1

c1 + c21113888 1113889lowast (LnY)

2minus

mβρ

c2

c1 + c21 +

c2

c1 + c21113888 1113889 LnFDIit( 1113857

2

+mβρ

c2


2c2


mβρ

2c1c2


(6)















Q x yg y

b1113872 1113873|x 1113944

n

j1ne 0λjxj y

b ge 1113944n

j1ne 0λjyj

b y

g le 1113944n

j1ne 0λjyj

g yj


⎪⎩

⎫⎪⎬

⎪⎭ (7)



mi1 xiXi0( 1113857

(1(T1 + T2)) 1113936T1r1 y

gr y

gr0( 1113857 + 1113936

T2r1 y

bry

br01113872 11138731113872 1113873

st

xge 1113944n

j1ne 0λjxj

yg le 1113944

n

j1ne 0λjy

gj

yb le 1113944

n

j1ne 0λjy

bj

xge x0 yleyg0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(8)




Tni f Pi αn( 1113857 + cni + μni (9)



(10)







(11)





(12)



(13)


+ 1113944jminus 1














i + εlowastit(16)




















095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest


















F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References











































βit (1)




minus ρit( 1113857

minus (mρ) (2)






minus ρ1113858 1113859

minus (mβρ)

(4)




minus ρ1113858 1113859 (5)





c2

c1 + c2Lnk minus

mβρ

c22

c1 + c2( 11138572(Lnk)

2minus

mβρ

c2

c1 + c2(Lnk)

2+ αLnINit

+mβρ

c1

c1 + c21 minus

2c2


minusmβρ

c1

c1 + c21 minus

c1

c1 + c21113888 1113889lowast (LnY)

2minus

mβρ

c2

c1 + c21 +

c2

c1 + c21113888 1113889 LnFDIit( 1113857

2

+mβρ

c2


2c2


mβρ

2c1c2


(6)















Q x yg y

b1113872 1113873|x 1113944

n

j1ne 0λjxj y

b ge 1113944n

j1ne 0λjyj

b y

g le 1113944n

j1ne 0λjyj

g yj


⎪⎩

⎫⎪⎬

⎪⎭ (7)



mi1 xiXi0( 1113857

(1(T1 + T2)) 1113936T1r1 y

gr y

gr0( 1113857 + 1113936

T2r1 y

bry

br01113872 11138731113872 1113873

st

xge 1113944n

j1ne 0λjxj

yg le 1113944

n

j1ne 0λjy

gj

yb le 1113944

n

j1ne 0λjy

bj

xge x0 yleyg0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(8)




Tni f Pi αn( 1113857 + cni + μni (9)



(10)







(11)





(12)



(13)


+ 1113944jminus 1














i + εlowastit(16)




















095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest


















F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References







































c2

c1 + c2Lnk minus

mβρ

c22

c1 + c2( 11138572(Lnk)

2minus

mβρ

c2

c1 + c2(Lnk)

2+ αLnINit

+mβρ

c1

c1 + c21 minus

2c2


minusmβρ

c1

c1 + c21 minus

c1

c1 + c21113888 1113889lowast (LnY)

2minus

mβρ

c2

c1 + c21 +

c2

c1 + c21113888 1113889 LnFDIit( 1113857

2

+mβρ

c2


2c2


mβρ

2c1c2


(6)















Q x yg y

b1113872 1113873|x 1113944

n

j1ne 0λjxj y

b ge 1113944n

j1ne 0λjyj

b y

g le 1113944n

j1ne 0λjyj

g yj


⎪⎩

⎫⎪⎬

⎪⎭ (7)



mi1 xiXi0( 1113857

(1(T1 + T2)) 1113936T1r1 y

gr y

gr0( 1113857 + 1113936

T2r1 y

bry

br01113872 11138731113872 1113873

st

xge 1113944n

j1ne 0λjxj

yg le 1113944

n

j1ne 0λjy

gj

yb le 1113944

n

j1ne 0λjy

bj

xge x0 yleyg0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(8)




Tni f Pi αn( 1113857 + cni + μni (9)



(10)







(11)





(12)



(13)


+ 1113944jminus 1














i + εlowastit(16)




















095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest


















F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References













































Q x yg y

b1113872 1113873|x 1113944

n

j1ne 0λjxj y

b ge 1113944n

j1ne 0λjyj

b y

g le 1113944n

j1ne 0λjyj

g yj


⎪⎩

⎫⎪⎬

⎪⎭ (7)



mi1 xiXi0( 1113857

(1(T1 + T2)) 1113936T1r1 y

gr y

gr0( 1113857 + 1113936

T2r1 y

bry

br01113872 11138731113872 1113873

st

xge 1113944n

j1ne 0λjxj

yg le 1113944

n

j1ne 0λjy

gj

yb le 1113944

n

j1ne 0λjy

bj

xge x0 yleyg0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(8)




Tni f Pi αn( 1113857 + cni + μni (9)



(10)







(11)





(12)



(13)


+ 1113944jminus 1














i + εlowastit(16)




















095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest


















F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References








































Tni f Pi αn( 1113857 + cni + μni (9)



(10)







(11)





(12)



(13)


+ 1113944jminus 1














i + εlowastit(16)




















095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest


















F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References














































i + εlowastit(16)




















095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest


















F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References















































095

09

085

08

075

07

2010 2012 2014time (year)

Gre

en ec

onom

ic ef

ficie

ncy

2016 2017

NationwideCentral

EastWest


















F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References





















































F 2660000 84900 304900 532800 286400(le0001) (le0001) (le0001) (le0001) (le0001)











AR(2) minus 07100 minus 03800 07400(0480) (0703) (0460)

Hansen 66000 30400 42600(0999) (0386) (1000)


F 578200 122600 56600(0000) (0001) (0019)


















AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References






















































AR(2) 02100 07400 minus 00700(0834) (0461) (0945)

Hansen 257700 225500 237100(0174) (0258) (0208)


F 304900 920100 286400(0000) (0000) (0000)







25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References











































25

20

15

10

5

08 9 10

LnGDP

LR

11

50

40

30

20

10

08 9 10

LnGDPLR

11









AR(2) (0474) (1000) (0933)mdash 12718 00453

Wald mdash (0203) (0964)2016500 590700 20588500






(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References









































(17)


zL nGECMit

zL nFDIit β2FDI

2it + β3FDIit (18)


















Data Availability




Acknowledgments


References










































Data Availability




Acknowledgments


References






































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