Research ArticleResearch on the Measurement Evolution and Driving Factors ofGreen Innovation Efficiency in Yangtze River Economic BeltA Super-SBM and Spatial Durbin Model

Renyan Long 1 Hangyuan Guo2 Danting Zheng23 Ronghua Chang24

and Sanggyun Na 2

1School of Economics and Management Xinyu University Xinyu 338004 China2School of Business Administration Wonkwang University 460 Iksandae-ro Iksan 54538 Republic of Korea3School of Economics and Management Shangrao Normal University Shangrao 334001 China4School of Economics and Management Shanxi Datong University Datong 037009 China

Correspondence should be addressed to Sanggyun Na nsghywkuackr

Received 28 July 2020 Revised 6 September 2020 Accepted 19 September 2020 Published 27 October 2020

Academic Editor Zhihan Lv

Copyright copy 2020 Renyan Long 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

With the shortage of resources and the increasingly serious environmental pollution in China green innovation has become asustainable competition for a region e Yangtze River Economic Belt (YREB) strategy is one of the most important strategies forthe sustainable development of Chinarsquos economy under the new normal Green innovation plays a linking role in the resourcesexchange and trade flow in YREB and it is also the foundation and guarantee to implement the YREB strategy e global en-vironmental pollution and the weak recovery of world economy make the traditional extensive economic growth model unsus-tainable Sustainable economic growth should focus on the quality of development and its external costs to the environment In orderto implement the concept of sustainable development the improvement of logistics ecological efficiency is related to the quality ofecological civilization constructionerefore it is of theoretical and practical significance to study the measurement evolution anddriving factors of coordinated development level of regional green innovation system is paper proposes a super-slack-basedmeasure (super-SBM) data envelopment analysis (DEA) model to measure the green innovation efficiency of 11 provinces and citiesin YREB from 2008 to 2017 mastering its spatial and evolutionary characteristics and conduct empirical analysis on the influencingfactors e empirical results indicate that economic development government support and industrial structure upgrading are theleading forces to directly enhance the green technology innovation ability of cities in the Yangtze River Economic belt and play thecore driving role of green innovation To further enhance the capacity of urban green innovation in the Yangtze River Economic beltwe will increase the governmentrsquos support for green innovation optimize the environmental governance model promote the greenupgrading of industrial structure and enhance the enthusiasm of enterprises for green innovation

1 Introduction

With the adoption of Chinarsquos reform and open up policy themiracle of rapid economic growth is attracting the worldrsquosattention In the meantime the emerging issues of envi-ronment and resource depletion also pose a huge challengeto economic development Coordinating the relationshipbetween economy and environment is the key to theimplementation of sustainable development strategy [1]made in China 2025 and raised the ldquogreen innovation-

driven developmentrdquo strategy to promote the healthy de-velopment of national economy

As an important support belt for Chinarsquos economicdevelopment in the new era the YREB spans three regions inChina connects the Yangtze River Delta Basin with the mostdeveloped economy in China and together with the coastaleconomic belt forms the T-shaped model of economy de-velopment in China and it is important in promoting anddemonstrating industrial transformation and upgrading andgreen development With the diversification of innovation

HindawiComplexityVolume 2020 Article ID 8094247 14 pageshttpsdoiorg10115520208094247

sources ecological environment has become an importantfactor in innovation transformation and upgrading How-ever there aremany industries with high pollution and high-energy consumption in the YREB Environmental pollutionand resource shortage have become important factorsrestricting the YREB strategic development e contra-diction of ecological environment and economic society hasbecome extremely acute Under the rigid constraints ofenergy and environment it is urgent to incorporate greendevelopment concept into technological innovation [2]erefore as the integration point of innovation-driven andgreen development green innovation has become an ef-fective means to break through the constraints of resourcesand environment and promote sustainable development Itis of great practical significance to improve the green in-novation efficiency of the YREB and realize the win-win ofinnovation efficiency ecological efficiency and economicefficiency so as to enhance the ability of regional sustainabledevelopment and promote high-quality economic devel-opment [3]

In early 2016 Present Xi Jinping proposed that theecological environment of the YREB should be placed in anoverwhelming position In March 2016 the meeting of thePolitical Bureau of the CPC Central Committee presidedover by General Secretary Xi approved the outline of thedevelopment plan of the Yangtze River Economic Beltstressing once again that ldquodevelopment should be promotedon the premise of protecting the ecology enhancing theoverall planning integrity coordination and sustainabilityof development and improving the efficiency of essentialallocationrdquo In March 2018 it was clearly pointed out in thework report of the 19th National Congress of the CommunistParty of China that the development orientation of theYangtze River Economic Belt should be guided by ecologicalpriority and green development promote the overall layoutof ldquofive in onerdquo innovation coordination green openingand sharing accelerate the construction of ecological civi-lization and achieve regional coordination and sustainabledevelopment e fundamental way to adjust the regionaleconomic structure change the mode of economic devel-opment and promote high-quality economic developmentlies in the continuous green innovation ldquoGreen innovationrdquois a complex system including resource input innovationoutput and environmental benefits It coordinates the re-lationship between economic development and ecologicalprotection realizes the optimal benefit output with the leastresource input and creates the highest green innovationefficiency

is paper modifies the super-SBM-DEA model tomeasure the green innovation efficiency of 11 provinces andcities in YREB from 2008 to 2017 mastering its spatial andevolutionary characteristics and conduct empirical analysison the influencing factors e main contribution is usingthe undesirable super-SBM-DEA method to avoid anyunderestimation or overestimation of the green innovationefficiency caused by radial and nonradial DEA e re-mainder of this paper is organized as follows e literaturereview is presented in Section 2 Section 3 briefly describesthe measurement method for green innovation efficiency

Section 4 presents the data and variables Empirical resultsand analysis are reported in Section 5 Section 6 drawsconclusions and policy implications e article structure isshown in Figure 1

2 Literature Review

According to the needs of balanced development in ecologyand economy we need to find a relationship among therapid development of economy excessive use of resourcesand deterioration of natural environment By measuringgreen innovation efficiency we can find key influencingfactors and promote the sustainable development of greenecological economy [4ndash6] According to existing literaturesresearch on green innovation efficiency can be summarizedin the following three aspects (1) research on green inno-vation efficiency (2) research on the measurement of greeninnovation efficiency (3) research on the influencing factorsof green innovation efficiency

21 Research on Green Innovation Efficiency Green inno-vation has become a popular concept and it is often knownas ecological innovation sustainable innovation and envi-ronmental innovation [7] Fussier and James first introducedthe term green innovation in the book driving green in-novation defining as new products or processes whichprovide customer and business value but significantly de-crease environmental impacts [8] Kemp et al define greeninnovation as a new process technology system and productto avoid or reduce environmental damage [9] Comparedwith traditional innovation green innovation takes botheconomic and environmental benefits into account andadapts to the improvement of supply side structural reformquality and efficiency of industrial parks From the per-spective of systems theory green innovation is a combi-nation of industrial innovation system theory and greeneconomy theory in reference to both green products andgreen processes [10 11] including the introduction of anynew or significantly improved product process organiza-tional change or marketing solution to reduce the con-sumption of natural resources and the emission of harmfulsubstances in the product life cycle [12]

In 1951 Kaufman first put forward the concept of ldquoef-ficiencyrdquo He pointed out that if technology cannot realizethe increase or decrease in output or input at the given levelof output or input the input-output vector in this state wasdefined as technology efficiency en Schumpeter com-bines the concept of innovation and efficiency and points outthat the fundamental purpose of innovation is to maximizeregional economic and social benefits Feng Zhijun definedgreen innovation efficiency as an input-output efficiency thatcan promote the unity of ldquoeconomic benefits environmentalbenefits and social benefitsrdquo [13] e authors in [14]pointed out that green innovation efficiency should not onlyreflect ldquogreenrdquo and ldquoinnovationrdquo but also reflect its economiccharacteristics that is economic efficiency In addition theauthors in [15] believe that green innovation efficiency is a

2 Complexity

comprehensive innovation efficiency considering the cost ofresource consumption and environmental pollution

22 Research on the Measurement of Green InnovationEfficiency ere are two main methods to measure greeninnovation efficiency the parametric analysis method usingSFA and nonparametric analysis method using DEA [16 17]Parametric analysis assumes that the departure from thefrontier of the DMU is the result of a combination of stochasticdisturbances and technical inefficiencies e application ofSFA focused mainly on enterprisesrsquo efficiency and its influ-encing factors and on research in the economic field [18 19]e authors in [20] measured the green innovation efficiencyin Chinarsquos provinces based on the improved stochastic frontiermodel and demonstrated the spatial agglomeration charac-teristics and path dependence of interprovincial green inno-vation efficiency from a spatial perspective

e nonparametric analysis method constructs a mini-mum output possibility set that can accommodate all in-dividual production modes according to the input andoutput of all decision-making units in the sample andmeasures the input-output efficiency based on the pro-duction possibility set e authors in [21] compared theinnovation efficiency of 185 regions in 23 European coun-tries with the multiobjective DEA model and pointed outthat there were differences in the innovation efficiency be-tween different regions and different innovation stages eauthors in [22] used DEA to measure the innovation effi-ciency values of hospitals in 29 OECD countries between2000 and 2010 and then applied the panel Tobit model todetermine the environmental factors affecting hospital ef-ficiency scores By decomposing the Malmquist ProductivityIndex Decomposition the change in the efficiency decom-position value was analyzed e authors in [23] used theDEA method to calculate the overall efficiency patent

production efficiency and scientific paper production effi-ciency of 32 Mexican states e authors in [24] used theSBM model to measure the green innovation efficiency ofChinese industrial enterprises without considering thenonexpected output and analyzed the regional differences ofthe green innovation efficiency of industrial enterprises inthe regions Luo et al [3] applied the Malmquist Index anddata envelopment analysis to evaluate the efficiency of greentechnology innovation in strategic emerging industries Duet al [25] used a two-stage network DEA with shared inputto measure the efficiency of regional enterprisesrsquo greentechnology innovation and explored the regional differencesin industrial enterprisesrsquo green technology RampD and theefficiency of green technology achievement transformation

23 Research on the Influencing Factors of Green InnovationEfficiency e influencing factors of green innovation ef-ficiency can be classified into direct factors and indirectfactors Direct factors included labor quality industrialstructure resource consumption and technological inno-vation e authors in [26 27] conducted a dynamic eval-uation on the efficiency of technological innovations inOECD countries and 20 member states of the EuropeanUnion based on the Malmquist Index Guan and Zuo [28]applied dual network DEA model to compare technologicalinnovation efficiency of 35 countries Yu et al [29] con-sidered the direct factors such as human capital enterprisenature and industrial structure when measuring the effi-ciency of technological innovation in China and found thatthey all have a significant impact on the efficiency oftechnological innovation Wang et al [30] found that RampDinvestment intensity has double threshold effect on greeninnovation efficiency of high-tech industry based on pro-vincial panel data from 2006 to 2012

Background introduction of green innovation and Yangtze River Economic Belt

Literature review on green innovation concept evaluation method and influencing Factors

Build green innovation efficiency evaluation indicators

Influencing factors of green innovation efficiency

Research summary and suggestion

Input

Labor Capital Energy

Output

Economic Social Technology (desirable) Industrial wastes

Economic development industrial structure

foreign direct investment government support

environmental regulation

Spatial econometric model

Super-SBM model

Research on green innovation efficiency and Influencing factors of Yangtze River Economic Belt

Figure 1 Article structure

Complexity 3

e indirect factors included economic developmentgovernment funding regional infrastructure foreign directinvestment opening up and environmental regulation Yuet al [31] reveal that environmental regulation can signif-icantly improve the green innovation efficiency of the YREBbut different environmental regulation models have differ-ent effects on the green innovation efficiency of the YREBYang et al [32] further analyzed the driving mechanism ofgreen innovation efficiency in the YREB and found that thecost of enterprise pollution the maturity of technologymarket and the openness of market are conducive topromoting the efficiency of green innovation in the YREBwhile the industrial structure has no significant impact onthe efficiency of green innovation Luo et al [3] revealed theimpact of international RampD capital technology spillover onthe efficiency of green technology innovation by building aspatial model According to the agglomeration effect of FDIGong et al [33] demonstrated the effect and transmissionmechanism of industrial green innovation efficiency

According to the existing literatures on green innova-tion there are still some shortcomings Renyan Long con-centrated on the level of enterprises industrial or provincese research on the efficiency of regional green innovationor specific economic zones and economic basins is of greatpractical significance for promoting the coordinated de-velopment of regional economy At the same time factorsflow technology spillover pollution discharge and inno-vation all show spatial interdependence and mutual influ-ence Existing literatures ignore the spatial correlation ofgreen innovation as data with spatial correlation Using the11 provincescitiesrsquo panel data in the YREB from 2008 to2017 this paper uses the super-slack-based measure (super-SBM) model to evaluate the green innovation efficiencyusing Global Moran Index to analyze the spatial correlationand spatial agglomeration characteristics and finally use thespatial measurement model to analyze the influencing fac-tors of green innovation efficiency

3 Materials and Methods

31 Super-SBMModel Traditional DEA models such as theCCR and BCC models are radial projection constructs byCook and Seiford [34] which assumes that all the outputs of aproduction system are valuable and should be maximized forgiven inputs Nevertheless the undesirable output will havesignificant effects on the efficiency in the whole process[35 36]Tone [37] developed a nonradial measurement tosolve the problems of input and output slacks by proposingthe slack-based measure (SBM) Compared with the tradi-tional DEA the efficiency value of this method is distributedin the (0 1) interval and the efficiency value of the effectiveDMU is 1 erefore when there are multiple effectiveDMUs further comparison cannot be made en Tone [38]developed a superefficiency SBM-DEA model which solvesthe problem of effective sorting and allows the efficiency scoreto be greater than 1 and can be easily rank-efficient DMUsSuper-SBM model can not only deal with the unexpectedoutput more appropriately but also make further comparisonin effective decision-making units so it is more accurate and

rigorous erefore the super-SBM with undesirable outputsis introduced into measuring green innovation efficiency inthis study and the model is as follows

min ρlowast (1m) 1113936

miminus1 xixi0( 1113857

1 s1 + s2( 1113857( 1113857 1113936s1r1 y

gr y

gr0( 1113857 + 1113936

s2r1 y

bry

br01113872 11138731113872 1113873

(1)

st

xge 1113944n

j1ne0λjxj

yg le 1113944

n

j1ne0λjy

gj

yb ge 1113944

n

j1ne0λjy

bj

xgex0 yg ley

g0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(2)

where ρlowast is the green innovation efficiency and λ is theconstant vector e super-SBM model is simultaneouslyable to measure DMU efficiency and can also calculate DMUinput and undesirable output redundancy rates and it fullyconsiders and effectively solves the problem with undesir-able output and is more accurate to evaluate and analyzeregional sustainable development

32 Spatial Econometric Model

321 Spatial Autocorrelation Analysis Spatial autocorre-lation analysis is a kind of spatial data analysis method that isused for the estimation and analysis of spatial dependencyand heterogeneity among objects which is commonly in-dicated by Moran Index (Moranrsquos I) [39ndash42] Before usingspatial econometric methods it is needed to be constructedto examine whether the green innovation efficiency in YREBhas spatial dependence

Global spatial autocorrelation is used to measure thedistribution characteristics of the entire research unit amongspatial elements and it can effectively test the autocorre-lation of adjacent units e global Moranrsquos I value rangesfrom [minus1 1] If Ilt 0 there is a negative spatial correlationwhich indicates that the efficiency in the study area is in adiscrete state If Igt 0 there is a positive correlation indi-cating an agglomeration state If I 0 demonstration ismade that the treatment efficiency is random and theformula is as follows

Moranprimes I 1113936

ni1 1113936

njne 1 Wij xi minus x( 1113857 xj minus x1113872 1113873

S2

1113936ni1 1113936

njne 1 Wij

(3)

where S2 (1n) 1113936ni1 (xi minus x)2 x (1n) 1113936

ni1 xi S2 is the

variance value of green innovation efficiency n representsthe total 11 provincescities in YREB xi and xj showprovince i and province jprime s green innovation efficiency x

represents the average green innovation efficiency and wij isthe spatial weighting matrix

4 Complexity

322 Spatial Weighting Matrix Setting spatial weightingmatrix is the basis of the spatial autocorrelation test andspatial econometric model It reflects the spatial distancebetween two regions usually including geographical dis-tance and socioeconomic distance At present geographicdistance is more common in researche spherical distance(d) between provincial capitals can be used to construct thespatial weighting matrix of geographical distance [43] Ituses the reciprocal of the square of the central distancebetween regions e specific formula is as follows

Wij

1d2ij

(ine j)

0 (ine j)

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(4)

323 Spatial Econometric Model Following Elhorst andGeogr [44] there are mainly three kinds of spatial econo-metric models spatial lag panel model (SLM) spatial errorpanel model (SEM) and spatial Durbin panel model (SDM)e SLMmodel hypothesizes that the value of the dependentvariable observed at a particular location is partially de-termined by a spatially weighted average of neighboring-dependent variables

If the level of green innovation efficiency in the region isnot only affected by some variables in the region and by thelevel of green innovation efficiency in neighboring regionsthe spatial lag model (SLM) can be set up which can beexpressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit (5)

where α is the constant term and W is the spatialweighting matrix X is the variable matrix of the corre-sponding influencing factors after the logarithmic treat-ment and β is the influencing coefficient of the localinfluencing factors on the local green innovation effi-ciency i represents the corresponding region t representsthe corresponding year and μ is the random error term ρis the spatial lag variable influence coefficient of greeninnovation efficiency development which reflects thespillover effect of green innovation efficiency developmenton green innovation development in the surroundingareas of the target area

If the spatial dependence of green innovation behavior isaffected by some error disturbance terms which are difficultto observe and have certain spatial structure and to effec-tively measure the impact of this error impact on the effi-ciency of green innovation in this region the spatial errormodel (SEM) can be expressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit

μit λWμit + εit(6)

where the parameter λ reflects the regional spillover effectscaused by the error term and ε is the residual term

If the level of green innovation efficiency in the region isnot only affected by the spatial spillover effect of greeninnovation efficiency in neighboring regions but also byother variables in neighboring regions the spatial Durbinmodel (SDM) can be considered which can be expressed asfollows

lnGIEit αit + Xitβit + WXitθit + μit (7)where θ reflects the weighted influence of other regionalfactors on the efficiency of green innovation in this regionwhich is defined as other spillover effect in this paper

324 Decomposition of Direct and Indirect Effects Due tothe spatial correlation in the spatial regression models theauthors in [45] point out that the coefficients of the ex-planatory variables in the regression model cannot accu-rately reflect the marginal effect Spatial spillover effect is animportant analysis tool in the spatial econometric modelBecause spillover effect has a certain direction of source andsource there will be other spillover effects of other regionalinfluencing factors on innovation efficiency in the regionand there will be other spillover effects of regional relevantvariables on green innovation efficiency in the surroundingregions In the spatial econometric model the independentvariable and the dependent variable will interact At thistime the marginal effect of the independent variable on thedependent variable cannot be regressed by the linear modelFurther deconstruction is needed to simplify the abovespatial Durbin model into a vector expression at a specifictime point

lnGIEit (1 minus ρW)minus 1αyN +(1 minus ρW)

minus 1

middot β lnXi + θW lnXi( 1113857μlowast(8)

whereyN is the vector of N times 1-order dependent variable αis the constant term μlowast is the cross-section random andperiod error term and lnXi is the N times K dimension matrixcomposed of all independent variables At a specific timepoint the derivative matrix expression of the dependentvariable lnGIEit to the independent variable K is

z ln GIEz lnx

k1

middot middot middot z ln GIEz lnx

kN

⎡⎣ ⎤⎦ (1 minus ρW)minus 1

βk W12θk middot middot middot W1Nθk

W21θk βk middot middot middot W2Nθk

⋮ ⋮ ⋮

WN1θk WN2θk βk

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

e mean value of the elements on the main diagonal ofthe right matrix in the formula reflects the influence degreeof the independent variable on the dependent variable in theprovince that is the effect of a province on the efficiency of

Complexity 5

green technology innovation in the region through a certaininfluencing factor which is called direct spillover effect It isexpressed as Mdirect Nminus 1 tra[X(W)] where tra[X(W)] isthe trace of matrix X(W) and it is the sum of main di-agonals e mean value of other elements on the nonmaindiagonal of the right matrix in this formula reflects thespillover effect of a province on the green innovation effi-ciency of other provinces through its own relevant influ-encing factors which is called the indirect spillover effect[46] In this paper it is defined as the spillover effect that isNminus 1y X(W) y minus Nminus 1 tra[X(W)] Finally direct spillovereffect and indirect spillover effect are summed up as the totalspillover effect

33 Variables and Data Description

331 Variables for Green Innovation EfficiencyAccording to the principles of comprehensiveness scientificand availability of data the index system for evaluating theefficiency of industrial green technology innovation isconstructed by referring to the relevant research of greentechnology innovation

(1) Inputs including labor input (number of RampDemployees) capital input (total investment in RampD)and resource input (total energy consumption)which represent the consumption degree of inno-vation activities on resources

(2) Desirable outputs including new product salesrevenue and patent applications which respectivelyreflect the economic benefits living standards andoutput level of scientific research achievements ofeach region

(3) Undesirable outputs industrial pollution is the mainsource of environmental pollution so the undesiredoutput variable adopts the industrial wastewaterdischarge industrial smoke (dust) and industrialsulfur dioxide discharge of each city in the YREB anduses the entropy method to calculate an environ-mental pollution index which is used to explain thecomprehensive impact of innovation activities on theecological environment

e input-output index system of green innovation ef-ficiency in YREB is constructed in Table 1

332 Influential Factors on Green Innovation Efficiencyere are many driving factors for the coordinated devel-opment of green innovation system ese factors will affectthe development level of the subsystem invested in the re-gional green innovation subsystem and then affect the co-ordinated development level of the regional greeninnovation system However these factors cannot be used asthe direct investment of each subsystem and these factorsare often not directly measured [47] erefore in order tosystematically and comprehensively study the driving factorsof regional green innovation system the factors that affectthe efficiency of green innovation in the YREB are

summarized as direct and indirect factors including envi-ronmental regulation and industrial structure and the in-direct factors include the level of economic development thestrength of government support and the level of opening tothe outside world [48] Consider that the YREB as a strategicregion of our country has different responsibilities andrequirements in its upper middle and lower reaches Basedon the previous study the following five factors are used toexamine the impact on the green innovation efficiency

(1) Economic development (ED) green innovation has ahigher threshold than traditional innovation Ahigher level of economic development is conduciveto the improvement of environmental protectionneeds and environmental human capital of residentsand provides the necessary material basis and socialenvironment for promoting the green innovationachievements e exhibition has green incentiveeffect and cumulative effect of innovation ability andit can promote the promotion of green innovationability e YREB is a national key constructioninland river economic belt with global influence Itseconomic development speed is at the nationalleading level which should promote the ability ofgreen innovation and enhance the competitivenessof regional green innovation development

(2) Environmental regulation (ER) Porter believes thatenvironmental regulation can drive green innova-tion which is the famous ldquoPorter Hypothesisrdquo [49]Porter believes that appropriate environmentalregulations can stimulate enterprises to increaseinvestment in technology research and developmentpromote green innovation and achieve a win-winsituation of technological progress and environ-mental protection Since the ldquoPorter Hypothesisrdquo wasput forward a large number of empirical researchresults show that environmental regulation is one ofthe important driving factors of green innovation[50ndash52] Under the restriction of environmentalregulation the innovation subject in the region

Table 1 Evaluation indicator system of green innovation efficiencyin YREB

Type Indicator Description

Inputs

Labor Number of RampD employees(10000 people)

Capital Total investment in RampD(Billion yuan)

Energy Total energy consumption(tons of standard coal)

Desirable outputsEconomic New product sales revenue

(billion yuan)

Technology Number of patentapplications (billion)

Undesirableoutputs

Industrialwaste

Exhaust emissionswastewater

discharge and solid waste(tons)

6 Complexity

should increase the investment in technological in-novation promote the improvement of the devel-opment level of technological innovation system andthen reduce the expected output of the cost ofachievement transformation subsystem and pro-mote the coordinated development of regional greeninnovation system erefore environmental regu-lation is also an important driving factor for thecoordinated development of regional green inno-vation system

(3) Government support (GS) technology driving fac-tors are considered to be the fundamental cause ofgreen innovation and the improvement of tech-nology capability triggers green innovation ispaper chooses government support for technologyinnovation to represent technology driving factorse governmentrsquos financial expenditure on scienceand technology improves the national green inno-vation ability and promotes economic growth egovernmentrsquos support for scientific and technolog-ical innovation has created a good external envi-ronment for regional green innovation To a largeextent the governmentrsquos financial support also re-flects the strength of the governmentrsquos policy sup-port is paper chooses government funds fromRampD funds as the indicator of government supportfor technological innovation To a large extent thegovernmentrsquos financial support also reflects thestrength of the governmentrsquos policy support Choosegovernment funds from RampD funds as the indicatorof government support for green innovation

(4) Foreign direct investment (FDI) the degree ofmarket opening reflects the degree of exchange be-tween a region and other regions in the fields ofeconomy science and technology e impact ofmarket openness on green innovation is still con-troversial in academia One of the most famoushypotheses is the ldquopollution shelterrdquo hypothesis [53]According to the ldquopollution shelterrdquo hypothesiscompanies in developed countries will transfer theirpollution intensive industries to developing coun-tries with relatively low regulation so that developingcountries will become ldquopollution shelter paradiserdquoand bear more environmental pollution [54] Butanother hypothesis pollution halo hypothesis holdsthat market opening can reduce environmentalpollution [55] rough the spillover effect of foreigninvestment developing countries bring advancedforeign green technologies which can significantlyimprove the level of regional technological innova-tion and the level of regional unexpected output thuspromoting the coordinated development of regionalgreen innovation system [56 57] e YREB coversthe three major economic zones of the East themiddle and the West e introduction of foreigninvestment may promote the local technologicalprogress and at the same time there will be

competition for foreign investment which will makethe surrounding cities backward in productioncapacity

(5) Industrial structure (IS) optimizing the internalallocation of the industry is conducive to stimulatingthe vitality of industrial innovation and enhancingthe capacity of industrial green technology innova-tion With the gradual upgrading of industrialstructure the secondary industry with strong pol-lution production capacity has transformed into aclean and low-carbon service industry and thesecondary and tertiary industries have acceleratedthe pace of integrated development [58 59] eclose connection is promoted between green tech-nology RampD services and industrial green trans-formation and enhanced the technologicalinnovation ability with industrial characteristics eYREB actively promotes the optimization andupgrading of industrial structure promotes the in-tegrated development of urban productive serviceindustry and manufacturing industry and requiresenterprises to strengthen the research and devel-opment of green production technology to meet thetechnical requirements of industrial structureupgrading and low-end production capacity may beforced to move to surrounding areas

e influencing factors of green innovation efficiency inYREB is constructed in Table 2

4 Empirical Analysis

41 Green Innovation Efficiency of YREB Considering thatthere will be a certain time lag when green innovation inputis converted into output using other research results forreference the input-output time lag is set as 1 year [60] thatis the time interval of input index is set as 2008ndash2017 andthe output index is set as 2008ndash2017 All the data weredirectly derived from the China Statistical Yearbook(2008ndash2017) the China Energy Statistical Yearbook(2008ndash2017) and the China Statistical Yearbook(2008ndash2017) the carbon dioxide emissions were estimatedusing the method provided by the Intergovernmental Panelon Climate Change [61] Descriptive statistics of relatedvariables are shown in Table 3 It can be preliminarily judgedthat the green innovation efficiency of 11 provinces andcities may also be significantly different and further em-pirical analysis will be carried out in the future

is paper relies onMax DEA PRO 80 software by usingsuper-SBM model to measure the green innovation effi-ciency of 11 provinces in YREB from 2008 to 2017 eresults are summarized in Table 4

From 2008 to 2017 the overall green innovation effi-ciency of YREB was relatively stable From 2008 to 2010there was a slight downward trend It increased significantlyin 2013 and decreased slightly in 2013ndash2017 ere aresignificant regional differences in green innovation effi-ciency level and time evolution trend in the upper middleand lower reaches of the YREB During the research period

Complexity 7

the green innovation efficiency of the middle and lowerreaches of the province showed a trend of increasing firstand then decreasing slightly while that of the upper andlower reaches showed a trend of decreasing first and thenincreasing and then decreasing but the fluctuation rangewas small Since the promulgation of several opinions of theState Council on promoting the rise of the central region in2005 the industrial undertaking policies to promote the riseof the central and western regions have promoted the inflowof a large number of capital and labor factors which has ledto the economic growth of the central and western regionsHowever the industrial undertaking has brought economicbenefits as well as unexpected output making the greeninnovation efficiency of the middle and upper reaches ofprovinces at it is low and declining and only in recent yearsdoes it show an upward trend In 2014 the policy of buildingthe YREB into a leading demonstration zone of ecologicalcivilization was issued Since then the state and localgovernments of the YREB have successively issued relevant

policies and the construction of ecological civilization in theYREB has achieved initial results

Table 2 e influencing factors of green innovation efficiency in YREB

Variable DescriptionEconomic development (ED) GDP per capitaEnvironmental regulation (ER) e ratio of total investment in industrial pollutants to GDPGovernment support (GS) RampD fundsForeign direct investment (FDI) e proportion of foreign investment as a percentage of the regional GDPIndustrial structure (IS) Proportion of total output value of tertiary industry to total GDP in each region

Table 3 Descriptive statistics of green innovation efficiency in YREB

Index Minimum Maximum Mean Standard deviationNumber of RampD employees (10000 people) 12656 466735 111834 134245Total investment in RampD (Billion yuan) 324986 4365780 4326382 6023576Total energy consumption (tons of standard coal) 4658 30480 13762 6187New product sales revenue (billion yuan) 3795210 7456754 7134578 7238568Number of patent applications (billion) 1785 125784 32650 33468Exhaust emissions (10000 tons) 8730 64390 23561 13652Wastewater discharge (10000 tons) 14370 455321 138542 100654Solid waste (10000 tons) 1450 17890 8974 4376

Table 4 Green innovation efficiency of YREB in 2008ndash2017

RegionYear

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Jiangsu 1032 1053 1047 1092 1085 1066 1023 1026 1024 1031Shanghai 1042 1035 1037 1032 1013 1021 1032 1027 1034 1089Zhejiang 0765 0763 0768 0827 0845 0976 0853 0812 0743 0751Anhui 0652 0654 0667 0706 071 0733 0724 0697 0681 0678Jiangxi 0622 0623 0631 0673 0668 0677 0682 0653 0646 0643Hubei 0649 0661 0667 0711 0733 0742 0744 0725 0711 0698Hunan 068 0671 068 0731 0745 0947 1012 098 1092 1001Chongqing 0625 063 0639 0688 0705 0698 0707 0715 0689 0691Sichuan 0625 0633 0643 0707 0722 0703 0702 0686 0664 0653Guizhou 0596 0591 059 0635 0632 0636 0634 0638 0645 063Yunnan 0619 0601 0602 0628 0631 0632 0636 063 0615 0613Average 0719 0720 0725 0766 0772 0803 0795 0781 0777 0771

Table 5 Global Moranrsquos I Index of green innovation efficiency

Moranrsquos I z2008 0540lowastlowastlowast 35442009 0510lowastlowastlowast 34152010 0514lowastlowastlowast 34322011 0533lowastlowastlowast 34762012 0487lowastlowastlowast 32582013 0313lowastlowast 20222014 0252lowastlowast 17562015 0233lowastlowast 16222016 0226lowast 16342017 0209lowast 1567Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

8 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

comprehensive innovation efficiency considering the cost ofresource consumption and environmental pollution

22 Research on the Measurement of Green InnovationEfficiency ere are two main methods to measure greeninnovation efficiency the parametric analysis method usingSFA and nonparametric analysis method using DEA [16 17]Parametric analysis assumes that the departure from thefrontier of the DMU is the result of a combination of stochasticdisturbances and technical inefficiencies e application ofSFA focused mainly on enterprisesrsquo efficiency and its influ-encing factors and on research in the economic field [18 19]e authors in [20] measured the green innovation efficiencyin Chinarsquos provinces based on the improved stochastic frontiermodel and demonstrated the spatial agglomeration charac-teristics and path dependence of interprovincial green inno-vation efficiency from a spatial perspective

e nonparametric analysis method constructs a mini-mum output possibility set that can accommodate all in-dividual production modes according to the input andoutput of all decision-making units in the sample andmeasures the input-output efficiency based on the pro-duction possibility set e authors in [21] compared theinnovation efficiency of 185 regions in 23 European coun-tries with the multiobjective DEA model and pointed outthat there were differences in the innovation efficiency be-tween different regions and different innovation stages eauthors in [22] used DEA to measure the innovation effi-ciency values of hospitals in 29 OECD countries between2000 and 2010 and then applied the panel Tobit model todetermine the environmental factors affecting hospital ef-ficiency scores By decomposing the Malmquist ProductivityIndex Decomposition the change in the efficiency decom-position value was analyzed e authors in [23] used theDEA method to calculate the overall efficiency patent

production efficiency and scientific paper production effi-ciency of 32 Mexican states e authors in [24] used theSBM model to measure the green innovation efficiency ofChinese industrial enterprises without considering thenonexpected output and analyzed the regional differences ofthe green innovation efficiency of industrial enterprises inthe regions Luo et al [3] applied the Malmquist Index anddata envelopment analysis to evaluate the efficiency of greentechnology innovation in strategic emerging industries Duet al [25] used a two-stage network DEA with shared inputto measure the efficiency of regional enterprisesrsquo greentechnology innovation and explored the regional differencesin industrial enterprisesrsquo green technology RampD and theefficiency of green technology achievement transformation

23 Research on the Influencing Factors of Green InnovationEfficiency e influencing factors of green innovation ef-ficiency can be classified into direct factors and indirectfactors Direct factors included labor quality industrialstructure resource consumption and technological inno-vation e authors in [26 27] conducted a dynamic eval-uation on the efficiency of technological innovations inOECD countries and 20 member states of the EuropeanUnion based on the Malmquist Index Guan and Zuo [28]applied dual network DEA model to compare technologicalinnovation efficiency of 35 countries Yu et al [29] con-sidered the direct factors such as human capital enterprisenature and industrial structure when measuring the effi-ciency of technological innovation in China and found thatthey all have a significant impact on the efficiency oftechnological innovation Wang et al [30] found that RampDinvestment intensity has double threshold effect on greeninnovation efficiency of high-tech industry based on pro-vincial panel data from 2006 to 2012

Background introduction of green innovation and Yangtze River Economic Belt

Literature review on green innovation concept evaluation method and influencing Factors

Build green innovation efficiency evaluation indicators

Influencing factors of green innovation efficiency

Research summary and suggestion

Input

Labor Capital Energy

Output

Economic Social Technology (desirable) Industrial wastes

Economic development industrial structure

foreign direct investment government support

environmental regulation

Spatial econometric model

Super-SBM model

Research on green innovation efficiency and Influencing factors of Yangtze River Economic Belt

Figure 1 Article structure

Complexity 3

e indirect factors included economic developmentgovernment funding regional infrastructure foreign directinvestment opening up and environmental regulation Yuet al [31] reveal that environmental regulation can signif-icantly improve the green innovation efficiency of the YREBbut different environmental regulation models have differ-ent effects on the green innovation efficiency of the YREBYang et al [32] further analyzed the driving mechanism ofgreen innovation efficiency in the YREB and found that thecost of enterprise pollution the maturity of technologymarket and the openness of market are conducive topromoting the efficiency of green innovation in the YREBwhile the industrial structure has no significant impact onthe efficiency of green innovation Luo et al [3] revealed theimpact of international RampD capital technology spillover onthe efficiency of green technology innovation by building aspatial model According to the agglomeration effect of FDIGong et al [33] demonstrated the effect and transmissionmechanism of industrial green innovation efficiency

According to the existing literatures on green innova-tion there are still some shortcomings Renyan Long con-centrated on the level of enterprises industrial or provincese research on the efficiency of regional green innovationor specific economic zones and economic basins is of greatpractical significance for promoting the coordinated de-velopment of regional economy At the same time factorsflow technology spillover pollution discharge and inno-vation all show spatial interdependence and mutual influ-ence Existing literatures ignore the spatial correlation ofgreen innovation as data with spatial correlation Using the11 provincescitiesrsquo panel data in the YREB from 2008 to2017 this paper uses the super-slack-based measure (super-SBM) model to evaluate the green innovation efficiencyusing Global Moran Index to analyze the spatial correlationand spatial agglomeration characteristics and finally use thespatial measurement model to analyze the influencing fac-tors of green innovation efficiency

3 Materials and Methods

31 Super-SBMModel Traditional DEA models such as theCCR and BCC models are radial projection constructs byCook and Seiford [34] which assumes that all the outputs of aproduction system are valuable and should be maximized forgiven inputs Nevertheless the undesirable output will havesignificant effects on the efficiency in the whole process[35 36]Tone [37] developed a nonradial measurement tosolve the problems of input and output slacks by proposingthe slack-based measure (SBM) Compared with the tradi-tional DEA the efficiency value of this method is distributedin the (0 1) interval and the efficiency value of the effectiveDMU is 1 erefore when there are multiple effectiveDMUs further comparison cannot be made en Tone [38]developed a superefficiency SBM-DEA model which solvesthe problem of effective sorting and allows the efficiency scoreto be greater than 1 and can be easily rank-efficient DMUsSuper-SBM model can not only deal with the unexpectedoutput more appropriately but also make further comparisonin effective decision-making units so it is more accurate and

rigorous erefore the super-SBM with undesirable outputsis introduced into measuring green innovation efficiency inthis study and the model is as follows

min ρlowast (1m) 1113936

miminus1 xixi0( 1113857

1 s1 + s2( 1113857( 1113857 1113936s1r1 y

gr y

gr0( 1113857 + 1113936

s2r1 y

bry

br01113872 11138731113872 1113873

(1)

st

xge 1113944n

j1ne0λjxj

yg le 1113944

n

j1ne0λjy

gj

yb ge 1113944

n

j1ne0λjy

bj

xgex0 yg ley

g0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(2)

where ρlowast is the green innovation efficiency and λ is theconstant vector e super-SBM model is simultaneouslyable to measure DMU efficiency and can also calculate DMUinput and undesirable output redundancy rates and it fullyconsiders and effectively solves the problem with undesir-able output and is more accurate to evaluate and analyzeregional sustainable development

32 Spatial Econometric Model

321 Spatial Autocorrelation Analysis Spatial autocorre-lation analysis is a kind of spatial data analysis method that isused for the estimation and analysis of spatial dependencyand heterogeneity among objects which is commonly in-dicated by Moran Index (Moranrsquos I) [39ndash42] Before usingspatial econometric methods it is needed to be constructedto examine whether the green innovation efficiency in YREBhas spatial dependence

Global spatial autocorrelation is used to measure thedistribution characteristics of the entire research unit amongspatial elements and it can effectively test the autocorre-lation of adjacent units e global Moranrsquos I value rangesfrom [minus1 1] If Ilt 0 there is a negative spatial correlationwhich indicates that the efficiency in the study area is in adiscrete state If Igt 0 there is a positive correlation indi-cating an agglomeration state If I 0 demonstration ismade that the treatment efficiency is random and theformula is as follows

Moranprimes I 1113936

ni1 1113936

njne 1 Wij xi minus x( 1113857 xj minus x1113872 1113873

S2

1113936ni1 1113936

njne 1 Wij

(3)

where S2 (1n) 1113936ni1 (xi minus x)2 x (1n) 1113936

ni1 xi S2 is the

variance value of green innovation efficiency n representsthe total 11 provincescities in YREB xi and xj showprovince i and province jprime s green innovation efficiency x

represents the average green innovation efficiency and wij isthe spatial weighting matrix

4 Complexity

322 Spatial Weighting Matrix Setting spatial weightingmatrix is the basis of the spatial autocorrelation test andspatial econometric model It reflects the spatial distancebetween two regions usually including geographical dis-tance and socioeconomic distance At present geographicdistance is more common in researche spherical distance(d) between provincial capitals can be used to construct thespatial weighting matrix of geographical distance [43] Ituses the reciprocal of the square of the central distancebetween regions e specific formula is as follows

Wij

1d2ij

(ine j)

0 (ine j)

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(4)

323 Spatial Econometric Model Following Elhorst andGeogr [44] there are mainly three kinds of spatial econo-metric models spatial lag panel model (SLM) spatial errorpanel model (SEM) and spatial Durbin panel model (SDM)e SLMmodel hypothesizes that the value of the dependentvariable observed at a particular location is partially de-termined by a spatially weighted average of neighboring-dependent variables

If the level of green innovation efficiency in the region isnot only affected by some variables in the region and by thelevel of green innovation efficiency in neighboring regionsthe spatial lag model (SLM) can be set up which can beexpressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit (5)

where α is the constant term and W is the spatialweighting matrix X is the variable matrix of the corre-sponding influencing factors after the logarithmic treat-ment and β is the influencing coefficient of the localinfluencing factors on the local green innovation effi-ciency i represents the corresponding region t representsthe corresponding year and μ is the random error term ρis the spatial lag variable influence coefficient of greeninnovation efficiency development which reflects thespillover effect of green innovation efficiency developmenton green innovation development in the surroundingareas of the target area

If the spatial dependence of green innovation behavior isaffected by some error disturbance terms which are difficultto observe and have certain spatial structure and to effec-tively measure the impact of this error impact on the effi-ciency of green innovation in this region the spatial errormodel (SEM) can be expressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit

μit λWμit + εit(6)

where the parameter λ reflects the regional spillover effectscaused by the error term and ε is the residual term

If the level of green innovation efficiency in the region isnot only affected by the spatial spillover effect of greeninnovation efficiency in neighboring regions but also byother variables in neighboring regions the spatial Durbinmodel (SDM) can be considered which can be expressed asfollows

lnGIEit αit + Xitβit + WXitθit + μit (7)where θ reflects the weighted influence of other regionalfactors on the efficiency of green innovation in this regionwhich is defined as other spillover effect in this paper

324 Decomposition of Direct and Indirect Effects Due tothe spatial correlation in the spatial regression models theauthors in [45] point out that the coefficients of the ex-planatory variables in the regression model cannot accu-rately reflect the marginal effect Spatial spillover effect is animportant analysis tool in the spatial econometric modelBecause spillover effect has a certain direction of source andsource there will be other spillover effects of other regionalinfluencing factors on innovation efficiency in the regionand there will be other spillover effects of regional relevantvariables on green innovation efficiency in the surroundingregions In the spatial econometric model the independentvariable and the dependent variable will interact At thistime the marginal effect of the independent variable on thedependent variable cannot be regressed by the linear modelFurther deconstruction is needed to simplify the abovespatial Durbin model into a vector expression at a specifictime point

lnGIEit (1 minus ρW)minus 1αyN +(1 minus ρW)

minus 1

middot β lnXi + θW lnXi( 1113857μlowast(8)

whereyN is the vector of N times 1-order dependent variable αis the constant term μlowast is the cross-section random andperiod error term and lnXi is the N times K dimension matrixcomposed of all independent variables At a specific timepoint the derivative matrix expression of the dependentvariable lnGIEit to the independent variable K is

z ln GIEz lnx

k1

middot middot middot z ln GIEz lnx

kN

⎡⎣ ⎤⎦ (1 minus ρW)minus 1

βk W12θk middot middot middot W1Nθk

W21θk βk middot middot middot W2Nθk

⋮ ⋮ ⋮

WN1θk WN2θk βk

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

e mean value of the elements on the main diagonal ofthe right matrix in the formula reflects the influence degreeof the independent variable on the dependent variable in theprovince that is the effect of a province on the efficiency of

Complexity 5

green technology innovation in the region through a certaininfluencing factor which is called direct spillover effect It isexpressed as Mdirect Nminus 1 tra[X(W)] where tra[X(W)] isthe trace of matrix X(W) and it is the sum of main di-agonals e mean value of other elements on the nonmaindiagonal of the right matrix in this formula reflects thespillover effect of a province on the green innovation effi-ciency of other provinces through its own relevant influ-encing factors which is called the indirect spillover effect[46] In this paper it is defined as the spillover effect that isNminus 1y X(W) y minus Nminus 1 tra[X(W)] Finally direct spillovereffect and indirect spillover effect are summed up as the totalspillover effect

33 Variables and Data Description

331 Variables for Green Innovation EfficiencyAccording to the principles of comprehensiveness scientificand availability of data the index system for evaluating theefficiency of industrial green technology innovation isconstructed by referring to the relevant research of greentechnology innovation

(1) Inputs including labor input (number of RampDemployees) capital input (total investment in RampD)and resource input (total energy consumption)which represent the consumption degree of inno-vation activities on resources

(2) Desirable outputs including new product salesrevenue and patent applications which respectivelyreflect the economic benefits living standards andoutput level of scientific research achievements ofeach region

(3) Undesirable outputs industrial pollution is the mainsource of environmental pollution so the undesiredoutput variable adopts the industrial wastewaterdischarge industrial smoke (dust) and industrialsulfur dioxide discharge of each city in the YREB anduses the entropy method to calculate an environ-mental pollution index which is used to explain thecomprehensive impact of innovation activities on theecological environment

e input-output index system of green innovation ef-ficiency in YREB is constructed in Table 1

332 Influential Factors on Green Innovation Efficiencyere are many driving factors for the coordinated devel-opment of green innovation system ese factors will affectthe development level of the subsystem invested in the re-gional green innovation subsystem and then affect the co-ordinated development level of the regional greeninnovation system However these factors cannot be used asthe direct investment of each subsystem and these factorsare often not directly measured [47] erefore in order tosystematically and comprehensively study the driving factorsof regional green innovation system the factors that affectthe efficiency of green innovation in the YREB are

summarized as direct and indirect factors including envi-ronmental regulation and industrial structure and the in-direct factors include the level of economic development thestrength of government support and the level of opening tothe outside world [48] Consider that the YREB as a strategicregion of our country has different responsibilities andrequirements in its upper middle and lower reaches Basedon the previous study the following five factors are used toexamine the impact on the green innovation efficiency

(1) Economic development (ED) green innovation has ahigher threshold than traditional innovation Ahigher level of economic development is conduciveto the improvement of environmental protectionneeds and environmental human capital of residentsand provides the necessary material basis and socialenvironment for promoting the green innovationachievements e exhibition has green incentiveeffect and cumulative effect of innovation ability andit can promote the promotion of green innovationability e YREB is a national key constructioninland river economic belt with global influence Itseconomic development speed is at the nationalleading level which should promote the ability ofgreen innovation and enhance the competitivenessof regional green innovation development

(2) Environmental regulation (ER) Porter believes thatenvironmental regulation can drive green innova-tion which is the famous ldquoPorter Hypothesisrdquo [49]Porter believes that appropriate environmentalregulations can stimulate enterprises to increaseinvestment in technology research and developmentpromote green innovation and achieve a win-winsituation of technological progress and environ-mental protection Since the ldquoPorter Hypothesisrdquo wasput forward a large number of empirical researchresults show that environmental regulation is one ofthe important driving factors of green innovation[50ndash52] Under the restriction of environmentalregulation the innovation subject in the region

Table 1 Evaluation indicator system of green innovation efficiencyin YREB

Type Indicator Description

Inputs

Labor Number of RampD employees(10000 people)

Capital Total investment in RampD(Billion yuan)

Energy Total energy consumption(tons of standard coal)

Desirable outputsEconomic New product sales revenue

(billion yuan)

Technology Number of patentapplications (billion)

Undesirableoutputs

Industrialwaste

Exhaust emissionswastewater

discharge and solid waste(tons)

6 Complexity

should increase the investment in technological in-novation promote the improvement of the devel-opment level of technological innovation system andthen reduce the expected output of the cost ofachievement transformation subsystem and pro-mote the coordinated development of regional greeninnovation system erefore environmental regu-lation is also an important driving factor for thecoordinated development of regional green inno-vation system

(3) Government support (GS) technology driving fac-tors are considered to be the fundamental cause ofgreen innovation and the improvement of tech-nology capability triggers green innovation ispaper chooses government support for technologyinnovation to represent technology driving factorse governmentrsquos financial expenditure on scienceand technology improves the national green inno-vation ability and promotes economic growth egovernmentrsquos support for scientific and technolog-ical innovation has created a good external envi-ronment for regional green innovation To a largeextent the governmentrsquos financial support also re-flects the strength of the governmentrsquos policy sup-port is paper chooses government funds fromRampD funds as the indicator of government supportfor technological innovation To a large extent thegovernmentrsquos financial support also reflects thestrength of the governmentrsquos policy support Choosegovernment funds from RampD funds as the indicatorof government support for green innovation

(4) Foreign direct investment (FDI) the degree ofmarket opening reflects the degree of exchange be-tween a region and other regions in the fields ofeconomy science and technology e impact ofmarket openness on green innovation is still con-troversial in academia One of the most famoushypotheses is the ldquopollution shelterrdquo hypothesis [53]According to the ldquopollution shelterrdquo hypothesiscompanies in developed countries will transfer theirpollution intensive industries to developing coun-tries with relatively low regulation so that developingcountries will become ldquopollution shelter paradiserdquoand bear more environmental pollution [54] Butanother hypothesis pollution halo hypothesis holdsthat market opening can reduce environmentalpollution [55] rough the spillover effect of foreigninvestment developing countries bring advancedforeign green technologies which can significantlyimprove the level of regional technological innova-tion and the level of regional unexpected output thuspromoting the coordinated development of regionalgreen innovation system [56 57] e YREB coversthe three major economic zones of the East themiddle and the West e introduction of foreigninvestment may promote the local technologicalprogress and at the same time there will be

competition for foreign investment which will makethe surrounding cities backward in productioncapacity

(5) Industrial structure (IS) optimizing the internalallocation of the industry is conducive to stimulatingthe vitality of industrial innovation and enhancingthe capacity of industrial green technology innova-tion With the gradual upgrading of industrialstructure the secondary industry with strong pol-lution production capacity has transformed into aclean and low-carbon service industry and thesecondary and tertiary industries have acceleratedthe pace of integrated development [58 59] eclose connection is promoted between green tech-nology RampD services and industrial green trans-formation and enhanced the technologicalinnovation ability with industrial characteristics eYREB actively promotes the optimization andupgrading of industrial structure promotes the in-tegrated development of urban productive serviceindustry and manufacturing industry and requiresenterprises to strengthen the research and devel-opment of green production technology to meet thetechnical requirements of industrial structureupgrading and low-end production capacity may beforced to move to surrounding areas

e influencing factors of green innovation efficiency inYREB is constructed in Table 2

4 Empirical Analysis

41 Green Innovation Efficiency of YREB Considering thatthere will be a certain time lag when green innovation inputis converted into output using other research results forreference the input-output time lag is set as 1 year [60] thatis the time interval of input index is set as 2008ndash2017 andthe output index is set as 2008ndash2017 All the data weredirectly derived from the China Statistical Yearbook(2008ndash2017) the China Energy Statistical Yearbook(2008ndash2017) and the China Statistical Yearbook(2008ndash2017) the carbon dioxide emissions were estimatedusing the method provided by the Intergovernmental Panelon Climate Change [61] Descriptive statistics of relatedvariables are shown in Table 3 It can be preliminarily judgedthat the green innovation efficiency of 11 provinces andcities may also be significantly different and further em-pirical analysis will be carried out in the future

is paper relies onMax DEA PRO 80 software by usingsuper-SBM model to measure the green innovation effi-ciency of 11 provinces in YREB from 2008 to 2017 eresults are summarized in Table 4

From 2008 to 2017 the overall green innovation effi-ciency of YREB was relatively stable From 2008 to 2010there was a slight downward trend It increased significantlyin 2013 and decreased slightly in 2013ndash2017 ere aresignificant regional differences in green innovation effi-ciency level and time evolution trend in the upper middleand lower reaches of the YREB During the research period

Complexity 7

the green innovation efficiency of the middle and lowerreaches of the province showed a trend of increasing firstand then decreasing slightly while that of the upper andlower reaches showed a trend of decreasing first and thenincreasing and then decreasing but the fluctuation rangewas small Since the promulgation of several opinions of theState Council on promoting the rise of the central region in2005 the industrial undertaking policies to promote the riseof the central and western regions have promoted the inflowof a large number of capital and labor factors which has ledto the economic growth of the central and western regionsHowever the industrial undertaking has brought economicbenefits as well as unexpected output making the greeninnovation efficiency of the middle and upper reaches ofprovinces at it is low and declining and only in recent yearsdoes it show an upward trend In 2014 the policy of buildingthe YREB into a leading demonstration zone of ecologicalcivilization was issued Since then the state and localgovernments of the YREB have successively issued relevant

policies and the construction of ecological civilization in theYREB has achieved initial results

Table 2 e influencing factors of green innovation efficiency in YREB

Variable DescriptionEconomic development (ED) GDP per capitaEnvironmental regulation (ER) e ratio of total investment in industrial pollutants to GDPGovernment support (GS) RampD fundsForeign direct investment (FDI) e proportion of foreign investment as a percentage of the regional GDPIndustrial structure (IS) Proportion of total output value of tertiary industry to total GDP in each region

Table 3 Descriptive statistics of green innovation efficiency in YREB

Index Minimum Maximum Mean Standard deviationNumber of RampD employees (10000 people) 12656 466735 111834 134245Total investment in RampD (Billion yuan) 324986 4365780 4326382 6023576Total energy consumption (tons of standard coal) 4658 30480 13762 6187New product sales revenue (billion yuan) 3795210 7456754 7134578 7238568Number of patent applications (billion) 1785 125784 32650 33468Exhaust emissions (10000 tons) 8730 64390 23561 13652Wastewater discharge (10000 tons) 14370 455321 138542 100654Solid waste (10000 tons) 1450 17890 8974 4376

Table 4 Green innovation efficiency of YREB in 2008ndash2017

RegionYear

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Jiangsu 1032 1053 1047 1092 1085 1066 1023 1026 1024 1031Shanghai 1042 1035 1037 1032 1013 1021 1032 1027 1034 1089Zhejiang 0765 0763 0768 0827 0845 0976 0853 0812 0743 0751Anhui 0652 0654 0667 0706 071 0733 0724 0697 0681 0678Jiangxi 0622 0623 0631 0673 0668 0677 0682 0653 0646 0643Hubei 0649 0661 0667 0711 0733 0742 0744 0725 0711 0698Hunan 068 0671 068 0731 0745 0947 1012 098 1092 1001Chongqing 0625 063 0639 0688 0705 0698 0707 0715 0689 0691Sichuan 0625 0633 0643 0707 0722 0703 0702 0686 0664 0653Guizhou 0596 0591 059 0635 0632 0636 0634 0638 0645 063Yunnan 0619 0601 0602 0628 0631 0632 0636 063 0615 0613Average 0719 0720 0725 0766 0772 0803 0795 0781 0777 0771

Table 5 Global Moranrsquos I Index of green innovation efficiency

Moranrsquos I z2008 0540lowastlowastlowast 35442009 0510lowastlowastlowast 34152010 0514lowastlowastlowast 34322011 0533lowastlowastlowast 34762012 0487lowastlowastlowast 32582013 0313lowastlowast 20222014 0252lowastlowast 17562015 0233lowastlowast 16222016 0226lowast 16342017 0209lowast 1567Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

8 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

e indirect factors included economic developmentgovernment funding regional infrastructure foreign directinvestment opening up and environmental regulation Yuet al [31] reveal that environmental regulation can signif-icantly improve the green innovation efficiency of the YREBbut different environmental regulation models have differ-ent effects on the green innovation efficiency of the YREBYang et al [32] further analyzed the driving mechanism ofgreen innovation efficiency in the YREB and found that thecost of enterprise pollution the maturity of technologymarket and the openness of market are conducive topromoting the efficiency of green innovation in the YREBwhile the industrial structure has no significant impact onthe efficiency of green innovation Luo et al [3] revealed theimpact of international RampD capital technology spillover onthe efficiency of green technology innovation by building aspatial model According to the agglomeration effect of FDIGong et al [33] demonstrated the effect and transmissionmechanism of industrial green innovation efficiency

According to the existing literatures on green innova-tion there are still some shortcomings Renyan Long con-centrated on the level of enterprises industrial or provincese research on the efficiency of regional green innovationor specific economic zones and economic basins is of greatpractical significance for promoting the coordinated de-velopment of regional economy At the same time factorsflow technology spillover pollution discharge and inno-vation all show spatial interdependence and mutual influ-ence Existing literatures ignore the spatial correlation ofgreen innovation as data with spatial correlation Using the11 provincescitiesrsquo panel data in the YREB from 2008 to2017 this paper uses the super-slack-based measure (super-SBM) model to evaluate the green innovation efficiencyusing Global Moran Index to analyze the spatial correlationand spatial agglomeration characteristics and finally use thespatial measurement model to analyze the influencing fac-tors of green innovation efficiency

3 Materials and Methods

31 Super-SBMModel Traditional DEA models such as theCCR and BCC models are radial projection constructs byCook and Seiford [34] which assumes that all the outputs of aproduction system are valuable and should be maximized forgiven inputs Nevertheless the undesirable output will havesignificant effects on the efficiency in the whole process[35 36]Tone [37] developed a nonradial measurement tosolve the problems of input and output slacks by proposingthe slack-based measure (SBM) Compared with the tradi-tional DEA the efficiency value of this method is distributedin the (0 1) interval and the efficiency value of the effectiveDMU is 1 erefore when there are multiple effectiveDMUs further comparison cannot be made en Tone [38]developed a superefficiency SBM-DEA model which solvesthe problem of effective sorting and allows the efficiency scoreto be greater than 1 and can be easily rank-efficient DMUsSuper-SBM model can not only deal with the unexpectedoutput more appropriately but also make further comparisonin effective decision-making units so it is more accurate and

rigorous erefore the super-SBM with undesirable outputsis introduced into measuring green innovation efficiency inthis study and the model is as follows

min ρlowast (1m) 1113936

miminus1 xixi0( 1113857

1 s1 + s2( 1113857( 1113857 1113936s1r1 y

gr y

gr0( 1113857 + 1113936

s2r1 y

bry

br01113872 11138731113872 1113873

(1)

st

xge 1113944n

j1ne0λjxj

yg le 1113944

n

j1ne0λjy

gj

yb ge 1113944

n

j1ne0λjy

bj

xgex0 yg ley

g0 y

b geyb0 λge 0

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

(2)

where ρlowast is the green innovation efficiency and λ is theconstant vector e super-SBM model is simultaneouslyable to measure DMU efficiency and can also calculate DMUinput and undesirable output redundancy rates and it fullyconsiders and effectively solves the problem with undesir-able output and is more accurate to evaluate and analyzeregional sustainable development

32 Spatial Econometric Model

321 Spatial Autocorrelation Analysis Spatial autocorre-lation analysis is a kind of spatial data analysis method that isused for the estimation and analysis of spatial dependencyand heterogeneity among objects which is commonly in-dicated by Moran Index (Moranrsquos I) [39ndash42] Before usingspatial econometric methods it is needed to be constructedto examine whether the green innovation efficiency in YREBhas spatial dependence

Global spatial autocorrelation is used to measure thedistribution characteristics of the entire research unit amongspatial elements and it can effectively test the autocorre-lation of adjacent units e global Moranrsquos I value rangesfrom [minus1 1] If Ilt 0 there is a negative spatial correlationwhich indicates that the efficiency in the study area is in adiscrete state If Igt 0 there is a positive correlation indi-cating an agglomeration state If I 0 demonstration ismade that the treatment efficiency is random and theformula is as follows

Moranprimes I 1113936

ni1 1113936

njne 1 Wij xi minus x( 1113857 xj minus x1113872 1113873

S2

1113936ni1 1113936

njne 1 Wij

(3)

where S2 (1n) 1113936ni1 (xi minus x)2 x (1n) 1113936

ni1 xi S2 is the

variance value of green innovation efficiency n representsthe total 11 provincescities in YREB xi and xj showprovince i and province jprime s green innovation efficiency x

represents the average green innovation efficiency and wij isthe spatial weighting matrix

4 Complexity

322 Spatial Weighting Matrix Setting spatial weightingmatrix is the basis of the spatial autocorrelation test andspatial econometric model It reflects the spatial distancebetween two regions usually including geographical dis-tance and socioeconomic distance At present geographicdistance is more common in researche spherical distance(d) between provincial capitals can be used to construct thespatial weighting matrix of geographical distance [43] Ituses the reciprocal of the square of the central distancebetween regions e specific formula is as follows

Wij

1d2ij

(ine j)

0 (ine j)

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(4)

323 Spatial Econometric Model Following Elhorst andGeogr [44] there are mainly three kinds of spatial econo-metric models spatial lag panel model (SLM) spatial errorpanel model (SEM) and spatial Durbin panel model (SDM)e SLMmodel hypothesizes that the value of the dependentvariable observed at a particular location is partially de-termined by a spatially weighted average of neighboring-dependent variables

If the level of green innovation efficiency in the region isnot only affected by some variables in the region and by thelevel of green innovation efficiency in neighboring regionsthe spatial lag model (SLM) can be set up which can beexpressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit (5)

where α is the constant term and W is the spatialweighting matrix X is the variable matrix of the corre-sponding influencing factors after the logarithmic treat-ment and β is the influencing coefficient of the localinfluencing factors on the local green innovation effi-ciency i represents the corresponding region t representsthe corresponding year and μ is the random error term ρis the spatial lag variable influence coefficient of greeninnovation efficiency development which reflects thespillover effect of green innovation efficiency developmenton green innovation development in the surroundingareas of the target area

If the spatial dependence of green innovation behavior isaffected by some error disturbance terms which are difficultto observe and have certain spatial structure and to effec-tively measure the impact of this error impact on the effi-ciency of green innovation in this region the spatial errormodel (SEM) can be expressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit

μit λWμit + εit(6)

where the parameter λ reflects the regional spillover effectscaused by the error term and ε is the residual term

If the level of green innovation efficiency in the region isnot only affected by the spatial spillover effect of greeninnovation efficiency in neighboring regions but also byother variables in neighboring regions the spatial Durbinmodel (SDM) can be considered which can be expressed asfollows

lnGIEit αit + Xitβit + WXitθit + μit (7)where θ reflects the weighted influence of other regionalfactors on the efficiency of green innovation in this regionwhich is defined as other spillover effect in this paper

324 Decomposition of Direct and Indirect Effects Due tothe spatial correlation in the spatial regression models theauthors in [45] point out that the coefficients of the ex-planatory variables in the regression model cannot accu-rately reflect the marginal effect Spatial spillover effect is animportant analysis tool in the spatial econometric modelBecause spillover effect has a certain direction of source andsource there will be other spillover effects of other regionalinfluencing factors on innovation efficiency in the regionand there will be other spillover effects of regional relevantvariables on green innovation efficiency in the surroundingregions In the spatial econometric model the independentvariable and the dependent variable will interact At thistime the marginal effect of the independent variable on thedependent variable cannot be regressed by the linear modelFurther deconstruction is needed to simplify the abovespatial Durbin model into a vector expression at a specifictime point

lnGIEit (1 minus ρW)minus 1αyN +(1 minus ρW)

minus 1

middot β lnXi + θW lnXi( 1113857μlowast(8)

whereyN is the vector of N times 1-order dependent variable αis the constant term μlowast is the cross-section random andperiod error term and lnXi is the N times K dimension matrixcomposed of all independent variables At a specific timepoint the derivative matrix expression of the dependentvariable lnGIEit to the independent variable K is

z ln GIEz lnx

k1

middot middot middot z ln GIEz lnx

kN

⎡⎣ ⎤⎦ (1 minus ρW)minus 1

βk W12θk middot middot middot W1Nθk

W21θk βk middot middot middot W2Nθk

⋮ ⋮ ⋮

WN1θk WN2θk βk

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

e mean value of the elements on the main diagonal ofthe right matrix in the formula reflects the influence degreeof the independent variable on the dependent variable in theprovince that is the effect of a province on the efficiency of

Complexity 5

green technology innovation in the region through a certaininfluencing factor which is called direct spillover effect It isexpressed as Mdirect Nminus 1 tra[X(W)] where tra[X(W)] isthe trace of matrix X(W) and it is the sum of main di-agonals e mean value of other elements on the nonmaindiagonal of the right matrix in this formula reflects thespillover effect of a province on the green innovation effi-ciency of other provinces through its own relevant influ-encing factors which is called the indirect spillover effect[46] In this paper it is defined as the spillover effect that isNminus 1y X(W) y minus Nminus 1 tra[X(W)] Finally direct spillovereffect and indirect spillover effect are summed up as the totalspillover effect

33 Variables and Data Description

331 Variables for Green Innovation EfficiencyAccording to the principles of comprehensiveness scientificand availability of data the index system for evaluating theefficiency of industrial green technology innovation isconstructed by referring to the relevant research of greentechnology innovation

(1) Inputs including labor input (number of RampDemployees) capital input (total investment in RampD)and resource input (total energy consumption)which represent the consumption degree of inno-vation activities on resources

(2) Desirable outputs including new product salesrevenue and patent applications which respectivelyreflect the economic benefits living standards andoutput level of scientific research achievements ofeach region

(3) Undesirable outputs industrial pollution is the mainsource of environmental pollution so the undesiredoutput variable adopts the industrial wastewaterdischarge industrial smoke (dust) and industrialsulfur dioxide discharge of each city in the YREB anduses the entropy method to calculate an environ-mental pollution index which is used to explain thecomprehensive impact of innovation activities on theecological environment

e input-output index system of green innovation ef-ficiency in YREB is constructed in Table 1

332 Influential Factors on Green Innovation Efficiencyere are many driving factors for the coordinated devel-opment of green innovation system ese factors will affectthe development level of the subsystem invested in the re-gional green innovation subsystem and then affect the co-ordinated development level of the regional greeninnovation system However these factors cannot be used asthe direct investment of each subsystem and these factorsare often not directly measured [47] erefore in order tosystematically and comprehensively study the driving factorsof regional green innovation system the factors that affectthe efficiency of green innovation in the YREB are

summarized as direct and indirect factors including envi-ronmental regulation and industrial structure and the in-direct factors include the level of economic development thestrength of government support and the level of opening tothe outside world [48] Consider that the YREB as a strategicregion of our country has different responsibilities andrequirements in its upper middle and lower reaches Basedon the previous study the following five factors are used toexamine the impact on the green innovation efficiency

(1) Economic development (ED) green innovation has ahigher threshold than traditional innovation Ahigher level of economic development is conduciveto the improvement of environmental protectionneeds and environmental human capital of residentsand provides the necessary material basis and socialenvironment for promoting the green innovationachievements e exhibition has green incentiveeffect and cumulative effect of innovation ability andit can promote the promotion of green innovationability e YREB is a national key constructioninland river economic belt with global influence Itseconomic development speed is at the nationalleading level which should promote the ability ofgreen innovation and enhance the competitivenessof regional green innovation development

(2) Environmental regulation (ER) Porter believes thatenvironmental regulation can drive green innova-tion which is the famous ldquoPorter Hypothesisrdquo [49]Porter believes that appropriate environmentalregulations can stimulate enterprises to increaseinvestment in technology research and developmentpromote green innovation and achieve a win-winsituation of technological progress and environ-mental protection Since the ldquoPorter Hypothesisrdquo wasput forward a large number of empirical researchresults show that environmental regulation is one ofthe important driving factors of green innovation[50ndash52] Under the restriction of environmentalregulation the innovation subject in the region

Table 1 Evaluation indicator system of green innovation efficiencyin YREB

Type Indicator Description

Inputs

Labor Number of RampD employees(10000 people)

Capital Total investment in RampD(Billion yuan)

Energy Total energy consumption(tons of standard coal)

Desirable outputsEconomic New product sales revenue

(billion yuan)

Technology Number of patentapplications (billion)

Undesirableoutputs

Industrialwaste

Exhaust emissionswastewater

discharge and solid waste(tons)

6 Complexity

should increase the investment in technological in-novation promote the improvement of the devel-opment level of technological innovation system andthen reduce the expected output of the cost ofachievement transformation subsystem and pro-mote the coordinated development of regional greeninnovation system erefore environmental regu-lation is also an important driving factor for thecoordinated development of regional green inno-vation system

(3) Government support (GS) technology driving fac-tors are considered to be the fundamental cause ofgreen innovation and the improvement of tech-nology capability triggers green innovation ispaper chooses government support for technologyinnovation to represent technology driving factorse governmentrsquos financial expenditure on scienceand technology improves the national green inno-vation ability and promotes economic growth egovernmentrsquos support for scientific and technolog-ical innovation has created a good external envi-ronment for regional green innovation To a largeextent the governmentrsquos financial support also re-flects the strength of the governmentrsquos policy sup-port is paper chooses government funds fromRampD funds as the indicator of government supportfor technological innovation To a large extent thegovernmentrsquos financial support also reflects thestrength of the governmentrsquos policy support Choosegovernment funds from RampD funds as the indicatorof government support for green innovation

(4) Foreign direct investment (FDI) the degree ofmarket opening reflects the degree of exchange be-tween a region and other regions in the fields ofeconomy science and technology e impact ofmarket openness on green innovation is still con-troversial in academia One of the most famoushypotheses is the ldquopollution shelterrdquo hypothesis [53]According to the ldquopollution shelterrdquo hypothesiscompanies in developed countries will transfer theirpollution intensive industries to developing coun-tries with relatively low regulation so that developingcountries will become ldquopollution shelter paradiserdquoand bear more environmental pollution [54] Butanother hypothesis pollution halo hypothesis holdsthat market opening can reduce environmentalpollution [55] rough the spillover effect of foreigninvestment developing countries bring advancedforeign green technologies which can significantlyimprove the level of regional technological innova-tion and the level of regional unexpected output thuspromoting the coordinated development of regionalgreen innovation system [56 57] e YREB coversthe three major economic zones of the East themiddle and the West e introduction of foreigninvestment may promote the local technologicalprogress and at the same time there will be

competition for foreign investment which will makethe surrounding cities backward in productioncapacity

(5) Industrial structure (IS) optimizing the internalallocation of the industry is conducive to stimulatingthe vitality of industrial innovation and enhancingthe capacity of industrial green technology innova-tion With the gradual upgrading of industrialstructure the secondary industry with strong pol-lution production capacity has transformed into aclean and low-carbon service industry and thesecondary and tertiary industries have acceleratedthe pace of integrated development [58 59] eclose connection is promoted between green tech-nology RampD services and industrial green trans-formation and enhanced the technologicalinnovation ability with industrial characteristics eYREB actively promotes the optimization andupgrading of industrial structure promotes the in-tegrated development of urban productive serviceindustry and manufacturing industry and requiresenterprises to strengthen the research and devel-opment of green production technology to meet thetechnical requirements of industrial structureupgrading and low-end production capacity may beforced to move to surrounding areas

e influencing factors of green innovation efficiency inYREB is constructed in Table 2

4 Empirical Analysis

41 Green Innovation Efficiency of YREB Considering thatthere will be a certain time lag when green innovation inputis converted into output using other research results forreference the input-output time lag is set as 1 year [60] thatis the time interval of input index is set as 2008ndash2017 andthe output index is set as 2008ndash2017 All the data weredirectly derived from the China Statistical Yearbook(2008ndash2017) the China Energy Statistical Yearbook(2008ndash2017) and the China Statistical Yearbook(2008ndash2017) the carbon dioxide emissions were estimatedusing the method provided by the Intergovernmental Panelon Climate Change [61] Descriptive statistics of relatedvariables are shown in Table 3 It can be preliminarily judgedthat the green innovation efficiency of 11 provinces andcities may also be significantly different and further em-pirical analysis will be carried out in the future

is paper relies onMax DEA PRO 80 software by usingsuper-SBM model to measure the green innovation effi-ciency of 11 provinces in YREB from 2008 to 2017 eresults are summarized in Table 4

From 2008 to 2017 the overall green innovation effi-ciency of YREB was relatively stable From 2008 to 2010there was a slight downward trend It increased significantlyin 2013 and decreased slightly in 2013ndash2017 ere aresignificant regional differences in green innovation effi-ciency level and time evolution trend in the upper middleand lower reaches of the YREB During the research period

Complexity 7

the green innovation efficiency of the middle and lowerreaches of the province showed a trend of increasing firstand then decreasing slightly while that of the upper andlower reaches showed a trend of decreasing first and thenincreasing and then decreasing but the fluctuation rangewas small Since the promulgation of several opinions of theState Council on promoting the rise of the central region in2005 the industrial undertaking policies to promote the riseof the central and western regions have promoted the inflowof a large number of capital and labor factors which has ledto the economic growth of the central and western regionsHowever the industrial undertaking has brought economicbenefits as well as unexpected output making the greeninnovation efficiency of the middle and upper reaches ofprovinces at it is low and declining and only in recent yearsdoes it show an upward trend In 2014 the policy of buildingthe YREB into a leading demonstration zone of ecologicalcivilization was issued Since then the state and localgovernments of the YREB have successively issued relevant

policies and the construction of ecological civilization in theYREB has achieved initial results

Table 2 e influencing factors of green innovation efficiency in YREB

Variable DescriptionEconomic development (ED) GDP per capitaEnvironmental regulation (ER) e ratio of total investment in industrial pollutants to GDPGovernment support (GS) RampD fundsForeign direct investment (FDI) e proportion of foreign investment as a percentage of the regional GDPIndustrial structure (IS) Proportion of total output value of tertiary industry to total GDP in each region

Table 3 Descriptive statistics of green innovation efficiency in YREB

Index Minimum Maximum Mean Standard deviationNumber of RampD employees (10000 people) 12656 466735 111834 134245Total investment in RampD (Billion yuan) 324986 4365780 4326382 6023576Total energy consumption (tons of standard coal) 4658 30480 13762 6187New product sales revenue (billion yuan) 3795210 7456754 7134578 7238568Number of patent applications (billion) 1785 125784 32650 33468Exhaust emissions (10000 tons) 8730 64390 23561 13652Wastewater discharge (10000 tons) 14370 455321 138542 100654Solid waste (10000 tons) 1450 17890 8974 4376

Table 4 Green innovation efficiency of YREB in 2008ndash2017

RegionYear

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Jiangsu 1032 1053 1047 1092 1085 1066 1023 1026 1024 1031Shanghai 1042 1035 1037 1032 1013 1021 1032 1027 1034 1089Zhejiang 0765 0763 0768 0827 0845 0976 0853 0812 0743 0751Anhui 0652 0654 0667 0706 071 0733 0724 0697 0681 0678Jiangxi 0622 0623 0631 0673 0668 0677 0682 0653 0646 0643Hubei 0649 0661 0667 0711 0733 0742 0744 0725 0711 0698Hunan 068 0671 068 0731 0745 0947 1012 098 1092 1001Chongqing 0625 063 0639 0688 0705 0698 0707 0715 0689 0691Sichuan 0625 0633 0643 0707 0722 0703 0702 0686 0664 0653Guizhou 0596 0591 059 0635 0632 0636 0634 0638 0645 063Yunnan 0619 0601 0602 0628 0631 0632 0636 063 0615 0613Average 0719 0720 0725 0766 0772 0803 0795 0781 0777 0771

Table 5 Global Moranrsquos I Index of green innovation efficiency

Moranrsquos I z2008 0540lowastlowastlowast 35442009 0510lowastlowastlowast 34152010 0514lowastlowastlowast 34322011 0533lowastlowastlowast 34762012 0487lowastlowastlowast 32582013 0313lowastlowast 20222014 0252lowastlowast 17562015 0233lowastlowast 16222016 0226lowast 16342017 0209lowast 1567Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

8 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

322 Spatial Weighting Matrix Setting spatial weightingmatrix is the basis of the spatial autocorrelation test andspatial econometric model It reflects the spatial distancebetween two regions usually including geographical dis-tance and socioeconomic distance At present geographicdistance is more common in researche spherical distance(d) between provincial capitals can be used to construct thespatial weighting matrix of geographical distance [43] Ituses the reciprocal of the square of the central distancebetween regions e specific formula is as follows

Wij

1d2ij

(ine j)

0 (ine j)

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(4)

323 Spatial Econometric Model Following Elhorst andGeogr [44] there are mainly three kinds of spatial econo-metric models spatial lag panel model (SLM) spatial errorpanel model (SEM) and spatial Durbin panel model (SDM)e SLMmodel hypothesizes that the value of the dependentvariable observed at a particular location is partially de-termined by a spatially weighted average of neighboring-dependent variables

If the level of green innovation efficiency in the region isnot only affected by some variables in the region and by thelevel of green innovation efficiency in neighboring regionsthe spatial lag model (SLM) can be set up which can beexpressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit (5)

where α is the constant term and W is the spatialweighting matrix X is the variable matrix of the corre-sponding influencing factors after the logarithmic treat-ment and β is the influencing coefficient of the localinfluencing factors on the local green innovation effi-ciency i represents the corresponding region t representsthe corresponding year and μ is the random error term ρis the spatial lag variable influence coefficient of greeninnovation efficiency development which reflects thespillover effect of green innovation efficiency developmenton green innovation development in the surroundingareas of the target area

If the spatial dependence of green innovation behavior isaffected by some error disturbance terms which are difficultto observe and have certain spatial structure and to effec-tively measure the impact of this error impact on the effi-ciency of green innovation in this region the spatial errormodel (SEM) can be expressed as follows

lnGIEit αit + ρW ln GIEit + Xitβit + μit

μit λWμit + εit(6)

where the parameter λ reflects the regional spillover effectscaused by the error term and ε is the residual term

If the level of green innovation efficiency in the region isnot only affected by the spatial spillover effect of greeninnovation efficiency in neighboring regions but also byother variables in neighboring regions the spatial Durbinmodel (SDM) can be considered which can be expressed asfollows

lnGIEit αit + Xitβit + WXitθit + μit (7)where θ reflects the weighted influence of other regionalfactors on the efficiency of green innovation in this regionwhich is defined as other spillover effect in this paper

324 Decomposition of Direct and Indirect Effects Due tothe spatial correlation in the spatial regression models theauthors in [45] point out that the coefficients of the ex-planatory variables in the regression model cannot accu-rately reflect the marginal effect Spatial spillover effect is animportant analysis tool in the spatial econometric modelBecause spillover effect has a certain direction of source andsource there will be other spillover effects of other regionalinfluencing factors on innovation efficiency in the regionand there will be other spillover effects of regional relevantvariables on green innovation efficiency in the surroundingregions In the spatial econometric model the independentvariable and the dependent variable will interact At thistime the marginal effect of the independent variable on thedependent variable cannot be regressed by the linear modelFurther deconstruction is needed to simplify the abovespatial Durbin model into a vector expression at a specifictime point

lnGIEit (1 minus ρW)minus 1αyN +(1 minus ρW)

minus 1

middot β lnXi + θW lnXi( 1113857μlowast(8)

whereyN is the vector of N times 1-order dependent variable αis the constant term μlowast is the cross-section random andperiod error term and lnXi is the N times K dimension matrixcomposed of all independent variables At a specific timepoint the derivative matrix expression of the dependentvariable lnGIEit to the independent variable K is

z ln GIEz lnx

k1

middot middot middot z ln GIEz lnx

kN

⎡⎣ ⎤⎦ (1 minus ρW)minus 1

βk W12θk middot middot middot W1Nθk

W21θk βk middot middot middot W2Nθk

⋮ ⋮ ⋮

WN1θk WN2θk βk

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

e mean value of the elements on the main diagonal ofthe right matrix in the formula reflects the influence degreeof the independent variable on the dependent variable in theprovince that is the effect of a province on the efficiency of

Complexity 5

green technology innovation in the region through a certaininfluencing factor which is called direct spillover effect It isexpressed as Mdirect Nminus 1 tra[X(W)] where tra[X(W)] isthe trace of matrix X(W) and it is the sum of main di-agonals e mean value of other elements on the nonmaindiagonal of the right matrix in this formula reflects thespillover effect of a province on the green innovation effi-ciency of other provinces through its own relevant influ-encing factors which is called the indirect spillover effect[46] In this paper it is defined as the spillover effect that isNminus 1y X(W) y minus Nminus 1 tra[X(W)] Finally direct spillovereffect and indirect spillover effect are summed up as the totalspillover effect

33 Variables and Data Description

331 Variables for Green Innovation EfficiencyAccording to the principles of comprehensiveness scientificand availability of data the index system for evaluating theefficiency of industrial green technology innovation isconstructed by referring to the relevant research of greentechnology innovation

(1) Inputs including labor input (number of RampDemployees) capital input (total investment in RampD)and resource input (total energy consumption)which represent the consumption degree of inno-vation activities on resources

(2) Desirable outputs including new product salesrevenue and patent applications which respectivelyreflect the economic benefits living standards andoutput level of scientific research achievements ofeach region

(3) Undesirable outputs industrial pollution is the mainsource of environmental pollution so the undesiredoutput variable adopts the industrial wastewaterdischarge industrial smoke (dust) and industrialsulfur dioxide discharge of each city in the YREB anduses the entropy method to calculate an environ-mental pollution index which is used to explain thecomprehensive impact of innovation activities on theecological environment

e input-output index system of green innovation ef-ficiency in YREB is constructed in Table 1

332 Influential Factors on Green Innovation Efficiencyere are many driving factors for the coordinated devel-opment of green innovation system ese factors will affectthe development level of the subsystem invested in the re-gional green innovation subsystem and then affect the co-ordinated development level of the regional greeninnovation system However these factors cannot be used asthe direct investment of each subsystem and these factorsare often not directly measured [47] erefore in order tosystematically and comprehensively study the driving factorsof regional green innovation system the factors that affectthe efficiency of green innovation in the YREB are

summarized as direct and indirect factors including envi-ronmental regulation and industrial structure and the in-direct factors include the level of economic development thestrength of government support and the level of opening tothe outside world [48] Consider that the YREB as a strategicregion of our country has different responsibilities andrequirements in its upper middle and lower reaches Basedon the previous study the following five factors are used toexamine the impact on the green innovation efficiency

(1) Economic development (ED) green innovation has ahigher threshold than traditional innovation Ahigher level of economic development is conduciveto the improvement of environmental protectionneeds and environmental human capital of residentsand provides the necessary material basis and socialenvironment for promoting the green innovationachievements e exhibition has green incentiveeffect and cumulative effect of innovation ability andit can promote the promotion of green innovationability e YREB is a national key constructioninland river economic belt with global influence Itseconomic development speed is at the nationalleading level which should promote the ability ofgreen innovation and enhance the competitivenessof regional green innovation development

(2) Environmental regulation (ER) Porter believes thatenvironmental regulation can drive green innova-tion which is the famous ldquoPorter Hypothesisrdquo [49]Porter believes that appropriate environmentalregulations can stimulate enterprises to increaseinvestment in technology research and developmentpromote green innovation and achieve a win-winsituation of technological progress and environ-mental protection Since the ldquoPorter Hypothesisrdquo wasput forward a large number of empirical researchresults show that environmental regulation is one ofthe important driving factors of green innovation[50ndash52] Under the restriction of environmentalregulation the innovation subject in the region

Table 1 Evaluation indicator system of green innovation efficiencyin YREB

Type Indicator Description

Inputs

Labor Number of RampD employees(10000 people)

Capital Total investment in RampD(Billion yuan)

Energy Total energy consumption(tons of standard coal)

Desirable outputsEconomic New product sales revenue

(billion yuan)

Technology Number of patentapplications (billion)

Undesirableoutputs

Industrialwaste

Exhaust emissionswastewater

discharge and solid waste(tons)

6 Complexity

should increase the investment in technological in-novation promote the improvement of the devel-opment level of technological innovation system andthen reduce the expected output of the cost ofachievement transformation subsystem and pro-mote the coordinated development of regional greeninnovation system erefore environmental regu-lation is also an important driving factor for thecoordinated development of regional green inno-vation system

(3) Government support (GS) technology driving fac-tors are considered to be the fundamental cause ofgreen innovation and the improvement of tech-nology capability triggers green innovation ispaper chooses government support for technologyinnovation to represent technology driving factorse governmentrsquos financial expenditure on scienceand technology improves the national green inno-vation ability and promotes economic growth egovernmentrsquos support for scientific and technolog-ical innovation has created a good external envi-ronment for regional green innovation To a largeextent the governmentrsquos financial support also re-flects the strength of the governmentrsquos policy sup-port is paper chooses government funds fromRampD funds as the indicator of government supportfor technological innovation To a large extent thegovernmentrsquos financial support also reflects thestrength of the governmentrsquos policy support Choosegovernment funds from RampD funds as the indicatorof government support for green innovation

(4) Foreign direct investment (FDI) the degree ofmarket opening reflects the degree of exchange be-tween a region and other regions in the fields ofeconomy science and technology e impact ofmarket openness on green innovation is still con-troversial in academia One of the most famoushypotheses is the ldquopollution shelterrdquo hypothesis [53]According to the ldquopollution shelterrdquo hypothesiscompanies in developed countries will transfer theirpollution intensive industries to developing coun-tries with relatively low regulation so that developingcountries will become ldquopollution shelter paradiserdquoand bear more environmental pollution [54] Butanother hypothesis pollution halo hypothesis holdsthat market opening can reduce environmentalpollution [55] rough the spillover effect of foreigninvestment developing countries bring advancedforeign green technologies which can significantlyimprove the level of regional technological innova-tion and the level of regional unexpected output thuspromoting the coordinated development of regionalgreen innovation system [56 57] e YREB coversthe three major economic zones of the East themiddle and the West e introduction of foreigninvestment may promote the local technologicalprogress and at the same time there will be

competition for foreign investment which will makethe surrounding cities backward in productioncapacity

(5) Industrial structure (IS) optimizing the internalallocation of the industry is conducive to stimulatingthe vitality of industrial innovation and enhancingthe capacity of industrial green technology innova-tion With the gradual upgrading of industrialstructure the secondary industry with strong pol-lution production capacity has transformed into aclean and low-carbon service industry and thesecondary and tertiary industries have acceleratedthe pace of integrated development [58 59] eclose connection is promoted between green tech-nology RampD services and industrial green trans-formation and enhanced the technologicalinnovation ability with industrial characteristics eYREB actively promotes the optimization andupgrading of industrial structure promotes the in-tegrated development of urban productive serviceindustry and manufacturing industry and requiresenterprises to strengthen the research and devel-opment of green production technology to meet thetechnical requirements of industrial structureupgrading and low-end production capacity may beforced to move to surrounding areas

e influencing factors of green innovation efficiency inYREB is constructed in Table 2

4 Empirical Analysis

41 Green Innovation Efficiency of YREB Considering thatthere will be a certain time lag when green innovation inputis converted into output using other research results forreference the input-output time lag is set as 1 year [60] thatis the time interval of input index is set as 2008ndash2017 andthe output index is set as 2008ndash2017 All the data weredirectly derived from the China Statistical Yearbook(2008ndash2017) the China Energy Statistical Yearbook(2008ndash2017) and the China Statistical Yearbook(2008ndash2017) the carbon dioxide emissions were estimatedusing the method provided by the Intergovernmental Panelon Climate Change [61] Descriptive statistics of relatedvariables are shown in Table 3 It can be preliminarily judgedthat the green innovation efficiency of 11 provinces andcities may also be significantly different and further em-pirical analysis will be carried out in the future

is paper relies onMax DEA PRO 80 software by usingsuper-SBM model to measure the green innovation effi-ciency of 11 provinces in YREB from 2008 to 2017 eresults are summarized in Table 4

From 2008 to 2017 the overall green innovation effi-ciency of YREB was relatively stable From 2008 to 2010there was a slight downward trend It increased significantlyin 2013 and decreased slightly in 2013ndash2017 ere aresignificant regional differences in green innovation effi-ciency level and time evolution trend in the upper middleand lower reaches of the YREB During the research period

Complexity 7

the green innovation efficiency of the middle and lowerreaches of the province showed a trend of increasing firstand then decreasing slightly while that of the upper andlower reaches showed a trend of decreasing first and thenincreasing and then decreasing but the fluctuation rangewas small Since the promulgation of several opinions of theState Council on promoting the rise of the central region in2005 the industrial undertaking policies to promote the riseof the central and western regions have promoted the inflowof a large number of capital and labor factors which has ledto the economic growth of the central and western regionsHowever the industrial undertaking has brought economicbenefits as well as unexpected output making the greeninnovation efficiency of the middle and upper reaches ofprovinces at it is low and declining and only in recent yearsdoes it show an upward trend In 2014 the policy of buildingthe YREB into a leading demonstration zone of ecologicalcivilization was issued Since then the state and localgovernments of the YREB have successively issued relevant

policies and the construction of ecological civilization in theYREB has achieved initial results

Table 2 e influencing factors of green innovation efficiency in YREB

Variable DescriptionEconomic development (ED) GDP per capitaEnvironmental regulation (ER) e ratio of total investment in industrial pollutants to GDPGovernment support (GS) RampD fundsForeign direct investment (FDI) e proportion of foreign investment as a percentage of the regional GDPIndustrial structure (IS) Proportion of total output value of tertiary industry to total GDP in each region

Table 3 Descriptive statistics of green innovation efficiency in YREB

Index Minimum Maximum Mean Standard deviationNumber of RampD employees (10000 people) 12656 466735 111834 134245Total investment in RampD (Billion yuan) 324986 4365780 4326382 6023576Total energy consumption (tons of standard coal) 4658 30480 13762 6187New product sales revenue (billion yuan) 3795210 7456754 7134578 7238568Number of patent applications (billion) 1785 125784 32650 33468Exhaust emissions (10000 tons) 8730 64390 23561 13652Wastewater discharge (10000 tons) 14370 455321 138542 100654Solid waste (10000 tons) 1450 17890 8974 4376

Table 4 Green innovation efficiency of YREB in 2008ndash2017

RegionYear

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Jiangsu 1032 1053 1047 1092 1085 1066 1023 1026 1024 1031Shanghai 1042 1035 1037 1032 1013 1021 1032 1027 1034 1089Zhejiang 0765 0763 0768 0827 0845 0976 0853 0812 0743 0751Anhui 0652 0654 0667 0706 071 0733 0724 0697 0681 0678Jiangxi 0622 0623 0631 0673 0668 0677 0682 0653 0646 0643Hubei 0649 0661 0667 0711 0733 0742 0744 0725 0711 0698Hunan 068 0671 068 0731 0745 0947 1012 098 1092 1001Chongqing 0625 063 0639 0688 0705 0698 0707 0715 0689 0691Sichuan 0625 0633 0643 0707 0722 0703 0702 0686 0664 0653Guizhou 0596 0591 059 0635 0632 0636 0634 0638 0645 063Yunnan 0619 0601 0602 0628 0631 0632 0636 063 0615 0613Average 0719 0720 0725 0766 0772 0803 0795 0781 0777 0771

Table 5 Global Moranrsquos I Index of green innovation efficiency

Moranrsquos I z2008 0540lowastlowastlowast 35442009 0510lowastlowastlowast 34152010 0514lowastlowastlowast 34322011 0533lowastlowastlowast 34762012 0487lowastlowastlowast 32582013 0313lowastlowast 20222014 0252lowastlowast 17562015 0233lowastlowast 16222016 0226lowast 16342017 0209lowast 1567Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

8 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

green technology innovation in the region through a certaininfluencing factor which is called direct spillover effect It isexpressed as Mdirect Nminus 1 tra[X(W)] where tra[X(W)] isthe trace of matrix X(W) and it is the sum of main di-agonals e mean value of other elements on the nonmaindiagonal of the right matrix in this formula reflects thespillover effect of a province on the green innovation effi-ciency of other provinces through its own relevant influ-encing factors which is called the indirect spillover effect[46] In this paper it is defined as the spillover effect that isNminus 1y X(W) y minus Nminus 1 tra[X(W)] Finally direct spillovereffect and indirect spillover effect are summed up as the totalspillover effect

33 Variables and Data Description

331 Variables for Green Innovation EfficiencyAccording to the principles of comprehensiveness scientificand availability of data the index system for evaluating theefficiency of industrial green technology innovation isconstructed by referring to the relevant research of greentechnology innovation

(1) Inputs including labor input (number of RampDemployees) capital input (total investment in RampD)and resource input (total energy consumption)which represent the consumption degree of inno-vation activities on resources

(2) Desirable outputs including new product salesrevenue and patent applications which respectivelyreflect the economic benefits living standards andoutput level of scientific research achievements ofeach region

(3) Undesirable outputs industrial pollution is the mainsource of environmental pollution so the undesiredoutput variable adopts the industrial wastewaterdischarge industrial smoke (dust) and industrialsulfur dioxide discharge of each city in the YREB anduses the entropy method to calculate an environ-mental pollution index which is used to explain thecomprehensive impact of innovation activities on theecological environment

e input-output index system of green innovation ef-ficiency in YREB is constructed in Table 1

332 Influential Factors on Green Innovation Efficiencyere are many driving factors for the coordinated devel-opment of green innovation system ese factors will affectthe development level of the subsystem invested in the re-gional green innovation subsystem and then affect the co-ordinated development level of the regional greeninnovation system However these factors cannot be used asthe direct investment of each subsystem and these factorsare often not directly measured [47] erefore in order tosystematically and comprehensively study the driving factorsof regional green innovation system the factors that affectthe efficiency of green innovation in the YREB are

summarized as direct and indirect factors including envi-ronmental regulation and industrial structure and the in-direct factors include the level of economic development thestrength of government support and the level of opening tothe outside world [48] Consider that the YREB as a strategicregion of our country has different responsibilities andrequirements in its upper middle and lower reaches Basedon the previous study the following five factors are used toexamine the impact on the green innovation efficiency

(1) Economic development (ED) green innovation has ahigher threshold than traditional innovation Ahigher level of economic development is conduciveto the improvement of environmental protectionneeds and environmental human capital of residentsand provides the necessary material basis and socialenvironment for promoting the green innovationachievements e exhibition has green incentiveeffect and cumulative effect of innovation ability andit can promote the promotion of green innovationability e YREB is a national key constructioninland river economic belt with global influence Itseconomic development speed is at the nationalleading level which should promote the ability ofgreen innovation and enhance the competitivenessof regional green innovation development

(2) Environmental regulation (ER) Porter believes thatenvironmental regulation can drive green innova-tion which is the famous ldquoPorter Hypothesisrdquo [49]Porter believes that appropriate environmentalregulations can stimulate enterprises to increaseinvestment in technology research and developmentpromote green innovation and achieve a win-winsituation of technological progress and environ-mental protection Since the ldquoPorter Hypothesisrdquo wasput forward a large number of empirical researchresults show that environmental regulation is one ofthe important driving factors of green innovation[50ndash52] Under the restriction of environmentalregulation the innovation subject in the region

Table 1 Evaluation indicator system of green innovation efficiencyin YREB

Type Indicator Description

Inputs

Labor Number of RampD employees(10000 people)

Capital Total investment in RampD(Billion yuan)

Energy Total energy consumption(tons of standard coal)

Desirable outputsEconomic New product sales revenue

(billion yuan)

Technology Number of patentapplications (billion)

Undesirableoutputs

Industrialwaste

Exhaust emissionswastewater

discharge and solid waste(tons)

6 Complexity

should increase the investment in technological in-novation promote the improvement of the devel-opment level of technological innovation system andthen reduce the expected output of the cost ofachievement transformation subsystem and pro-mote the coordinated development of regional greeninnovation system erefore environmental regu-lation is also an important driving factor for thecoordinated development of regional green inno-vation system

(3) Government support (GS) technology driving fac-tors are considered to be the fundamental cause ofgreen innovation and the improvement of tech-nology capability triggers green innovation ispaper chooses government support for technologyinnovation to represent technology driving factorse governmentrsquos financial expenditure on scienceand technology improves the national green inno-vation ability and promotes economic growth egovernmentrsquos support for scientific and technolog-ical innovation has created a good external envi-ronment for regional green innovation To a largeextent the governmentrsquos financial support also re-flects the strength of the governmentrsquos policy sup-port is paper chooses government funds fromRampD funds as the indicator of government supportfor technological innovation To a large extent thegovernmentrsquos financial support also reflects thestrength of the governmentrsquos policy support Choosegovernment funds from RampD funds as the indicatorof government support for green innovation

(4) Foreign direct investment (FDI) the degree ofmarket opening reflects the degree of exchange be-tween a region and other regions in the fields ofeconomy science and technology e impact ofmarket openness on green innovation is still con-troversial in academia One of the most famoushypotheses is the ldquopollution shelterrdquo hypothesis [53]According to the ldquopollution shelterrdquo hypothesiscompanies in developed countries will transfer theirpollution intensive industries to developing coun-tries with relatively low regulation so that developingcountries will become ldquopollution shelter paradiserdquoand bear more environmental pollution [54] Butanother hypothesis pollution halo hypothesis holdsthat market opening can reduce environmentalpollution [55] rough the spillover effect of foreigninvestment developing countries bring advancedforeign green technologies which can significantlyimprove the level of regional technological innova-tion and the level of regional unexpected output thuspromoting the coordinated development of regionalgreen innovation system [56 57] e YREB coversthe three major economic zones of the East themiddle and the West e introduction of foreigninvestment may promote the local technologicalprogress and at the same time there will be

competition for foreign investment which will makethe surrounding cities backward in productioncapacity

(5) Industrial structure (IS) optimizing the internalallocation of the industry is conducive to stimulatingthe vitality of industrial innovation and enhancingthe capacity of industrial green technology innova-tion With the gradual upgrading of industrialstructure the secondary industry with strong pol-lution production capacity has transformed into aclean and low-carbon service industry and thesecondary and tertiary industries have acceleratedthe pace of integrated development [58 59] eclose connection is promoted between green tech-nology RampD services and industrial green trans-formation and enhanced the technologicalinnovation ability with industrial characteristics eYREB actively promotes the optimization andupgrading of industrial structure promotes the in-tegrated development of urban productive serviceindustry and manufacturing industry and requiresenterprises to strengthen the research and devel-opment of green production technology to meet thetechnical requirements of industrial structureupgrading and low-end production capacity may beforced to move to surrounding areas

e influencing factors of green innovation efficiency inYREB is constructed in Table 2

4 Empirical Analysis

41 Green Innovation Efficiency of YREB Considering thatthere will be a certain time lag when green innovation inputis converted into output using other research results forreference the input-output time lag is set as 1 year [60] thatis the time interval of input index is set as 2008ndash2017 andthe output index is set as 2008ndash2017 All the data weredirectly derived from the China Statistical Yearbook(2008ndash2017) the China Energy Statistical Yearbook(2008ndash2017) and the China Statistical Yearbook(2008ndash2017) the carbon dioxide emissions were estimatedusing the method provided by the Intergovernmental Panelon Climate Change [61] Descriptive statistics of relatedvariables are shown in Table 3 It can be preliminarily judgedthat the green innovation efficiency of 11 provinces andcities may also be significantly different and further em-pirical analysis will be carried out in the future

is paper relies onMax DEA PRO 80 software by usingsuper-SBM model to measure the green innovation effi-ciency of 11 provinces in YREB from 2008 to 2017 eresults are summarized in Table 4

From 2008 to 2017 the overall green innovation effi-ciency of YREB was relatively stable From 2008 to 2010there was a slight downward trend It increased significantlyin 2013 and decreased slightly in 2013ndash2017 ere aresignificant regional differences in green innovation effi-ciency level and time evolution trend in the upper middleand lower reaches of the YREB During the research period

Complexity 7

the green innovation efficiency of the middle and lowerreaches of the province showed a trend of increasing firstand then decreasing slightly while that of the upper andlower reaches showed a trend of decreasing first and thenincreasing and then decreasing but the fluctuation rangewas small Since the promulgation of several opinions of theState Council on promoting the rise of the central region in2005 the industrial undertaking policies to promote the riseof the central and western regions have promoted the inflowof a large number of capital and labor factors which has ledto the economic growth of the central and western regionsHowever the industrial undertaking has brought economicbenefits as well as unexpected output making the greeninnovation efficiency of the middle and upper reaches ofprovinces at it is low and declining and only in recent yearsdoes it show an upward trend In 2014 the policy of buildingthe YREB into a leading demonstration zone of ecologicalcivilization was issued Since then the state and localgovernments of the YREB have successively issued relevant

policies and the construction of ecological civilization in theYREB has achieved initial results

Table 2 e influencing factors of green innovation efficiency in YREB

Variable DescriptionEconomic development (ED) GDP per capitaEnvironmental regulation (ER) e ratio of total investment in industrial pollutants to GDPGovernment support (GS) RampD fundsForeign direct investment (FDI) e proportion of foreign investment as a percentage of the regional GDPIndustrial structure (IS) Proportion of total output value of tertiary industry to total GDP in each region

Table 3 Descriptive statistics of green innovation efficiency in YREB

Index Minimum Maximum Mean Standard deviationNumber of RampD employees (10000 people) 12656 466735 111834 134245Total investment in RampD (Billion yuan) 324986 4365780 4326382 6023576Total energy consumption (tons of standard coal) 4658 30480 13762 6187New product sales revenue (billion yuan) 3795210 7456754 7134578 7238568Number of patent applications (billion) 1785 125784 32650 33468Exhaust emissions (10000 tons) 8730 64390 23561 13652Wastewater discharge (10000 tons) 14370 455321 138542 100654Solid waste (10000 tons) 1450 17890 8974 4376

Table 4 Green innovation efficiency of YREB in 2008ndash2017

RegionYear

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Jiangsu 1032 1053 1047 1092 1085 1066 1023 1026 1024 1031Shanghai 1042 1035 1037 1032 1013 1021 1032 1027 1034 1089Zhejiang 0765 0763 0768 0827 0845 0976 0853 0812 0743 0751Anhui 0652 0654 0667 0706 071 0733 0724 0697 0681 0678Jiangxi 0622 0623 0631 0673 0668 0677 0682 0653 0646 0643Hubei 0649 0661 0667 0711 0733 0742 0744 0725 0711 0698Hunan 068 0671 068 0731 0745 0947 1012 098 1092 1001Chongqing 0625 063 0639 0688 0705 0698 0707 0715 0689 0691Sichuan 0625 0633 0643 0707 0722 0703 0702 0686 0664 0653Guizhou 0596 0591 059 0635 0632 0636 0634 0638 0645 063Yunnan 0619 0601 0602 0628 0631 0632 0636 063 0615 0613Average 0719 0720 0725 0766 0772 0803 0795 0781 0777 0771

Table 5 Global Moranrsquos I Index of green innovation efficiency

Moranrsquos I z2008 0540lowastlowastlowast 35442009 0510lowastlowastlowast 34152010 0514lowastlowastlowast 34322011 0533lowastlowastlowast 34762012 0487lowastlowastlowast 32582013 0313lowastlowast 20222014 0252lowastlowast 17562015 0233lowastlowast 16222016 0226lowast 16342017 0209lowast 1567Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

8 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

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[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

should increase the investment in technological in-novation promote the improvement of the devel-opment level of technological innovation system andthen reduce the expected output of the cost ofachievement transformation subsystem and pro-mote the coordinated development of regional greeninnovation system erefore environmental regu-lation is also an important driving factor for thecoordinated development of regional green inno-vation system

(3) Government support (GS) technology driving fac-tors are considered to be the fundamental cause ofgreen innovation and the improvement of tech-nology capability triggers green innovation ispaper chooses government support for technologyinnovation to represent technology driving factorse governmentrsquos financial expenditure on scienceand technology improves the national green inno-vation ability and promotes economic growth egovernmentrsquos support for scientific and technolog-ical innovation has created a good external envi-ronment for regional green innovation To a largeextent the governmentrsquos financial support also re-flects the strength of the governmentrsquos policy sup-port is paper chooses government funds fromRampD funds as the indicator of government supportfor technological innovation To a large extent thegovernmentrsquos financial support also reflects thestrength of the governmentrsquos policy support Choosegovernment funds from RampD funds as the indicatorof government support for green innovation

(4) Foreign direct investment (FDI) the degree ofmarket opening reflects the degree of exchange be-tween a region and other regions in the fields ofeconomy science and technology e impact ofmarket openness on green innovation is still con-troversial in academia One of the most famoushypotheses is the ldquopollution shelterrdquo hypothesis [53]According to the ldquopollution shelterrdquo hypothesiscompanies in developed countries will transfer theirpollution intensive industries to developing coun-tries with relatively low regulation so that developingcountries will become ldquopollution shelter paradiserdquoand bear more environmental pollution [54] Butanother hypothesis pollution halo hypothesis holdsthat market opening can reduce environmentalpollution [55] rough the spillover effect of foreigninvestment developing countries bring advancedforeign green technologies which can significantlyimprove the level of regional technological innova-tion and the level of regional unexpected output thuspromoting the coordinated development of regionalgreen innovation system [56 57] e YREB coversthe three major economic zones of the East themiddle and the West e introduction of foreigninvestment may promote the local technologicalprogress and at the same time there will be

competition for foreign investment which will makethe surrounding cities backward in productioncapacity

(5) Industrial structure (IS) optimizing the internalallocation of the industry is conducive to stimulatingthe vitality of industrial innovation and enhancingthe capacity of industrial green technology innova-tion With the gradual upgrading of industrialstructure the secondary industry with strong pol-lution production capacity has transformed into aclean and low-carbon service industry and thesecondary and tertiary industries have acceleratedthe pace of integrated development [58 59] eclose connection is promoted between green tech-nology RampD services and industrial green trans-formation and enhanced the technologicalinnovation ability with industrial characteristics eYREB actively promotes the optimization andupgrading of industrial structure promotes the in-tegrated development of urban productive serviceindustry and manufacturing industry and requiresenterprises to strengthen the research and devel-opment of green production technology to meet thetechnical requirements of industrial structureupgrading and low-end production capacity may beforced to move to surrounding areas

e influencing factors of green innovation efficiency inYREB is constructed in Table 2

4 Empirical Analysis

41 Green Innovation Efficiency of YREB Considering thatthere will be a certain time lag when green innovation inputis converted into output using other research results forreference the input-output time lag is set as 1 year [60] thatis the time interval of input index is set as 2008ndash2017 andthe output index is set as 2008ndash2017 All the data weredirectly derived from the China Statistical Yearbook(2008ndash2017) the China Energy Statistical Yearbook(2008ndash2017) and the China Statistical Yearbook(2008ndash2017) the carbon dioxide emissions were estimatedusing the method provided by the Intergovernmental Panelon Climate Change [61] Descriptive statistics of relatedvariables are shown in Table 3 It can be preliminarily judgedthat the green innovation efficiency of 11 provinces andcities may also be significantly different and further em-pirical analysis will be carried out in the future

is paper relies onMax DEA PRO 80 software by usingsuper-SBM model to measure the green innovation effi-ciency of 11 provinces in YREB from 2008 to 2017 eresults are summarized in Table 4

From 2008 to 2017 the overall green innovation effi-ciency of YREB was relatively stable From 2008 to 2010there was a slight downward trend It increased significantlyin 2013 and decreased slightly in 2013ndash2017 ere aresignificant regional differences in green innovation effi-ciency level and time evolution trend in the upper middleand lower reaches of the YREB During the research period

Complexity 7

the green innovation efficiency of the middle and lowerreaches of the province showed a trend of increasing firstand then decreasing slightly while that of the upper andlower reaches showed a trend of decreasing first and thenincreasing and then decreasing but the fluctuation rangewas small Since the promulgation of several opinions of theState Council on promoting the rise of the central region in2005 the industrial undertaking policies to promote the riseof the central and western regions have promoted the inflowof a large number of capital and labor factors which has ledto the economic growth of the central and western regionsHowever the industrial undertaking has brought economicbenefits as well as unexpected output making the greeninnovation efficiency of the middle and upper reaches ofprovinces at it is low and declining and only in recent yearsdoes it show an upward trend In 2014 the policy of buildingthe YREB into a leading demonstration zone of ecologicalcivilization was issued Since then the state and localgovernments of the YREB have successively issued relevant

policies and the construction of ecological civilization in theYREB has achieved initial results

Table 2 e influencing factors of green innovation efficiency in YREB

Variable DescriptionEconomic development (ED) GDP per capitaEnvironmental regulation (ER) e ratio of total investment in industrial pollutants to GDPGovernment support (GS) RampD fundsForeign direct investment (FDI) e proportion of foreign investment as a percentage of the regional GDPIndustrial structure (IS) Proportion of total output value of tertiary industry to total GDP in each region

Table 3 Descriptive statistics of green innovation efficiency in YREB

Index Minimum Maximum Mean Standard deviationNumber of RampD employees (10000 people) 12656 466735 111834 134245Total investment in RampD (Billion yuan) 324986 4365780 4326382 6023576Total energy consumption (tons of standard coal) 4658 30480 13762 6187New product sales revenue (billion yuan) 3795210 7456754 7134578 7238568Number of patent applications (billion) 1785 125784 32650 33468Exhaust emissions (10000 tons) 8730 64390 23561 13652Wastewater discharge (10000 tons) 14370 455321 138542 100654Solid waste (10000 tons) 1450 17890 8974 4376

Table 4 Green innovation efficiency of YREB in 2008ndash2017

RegionYear

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Jiangsu 1032 1053 1047 1092 1085 1066 1023 1026 1024 1031Shanghai 1042 1035 1037 1032 1013 1021 1032 1027 1034 1089Zhejiang 0765 0763 0768 0827 0845 0976 0853 0812 0743 0751Anhui 0652 0654 0667 0706 071 0733 0724 0697 0681 0678Jiangxi 0622 0623 0631 0673 0668 0677 0682 0653 0646 0643Hubei 0649 0661 0667 0711 0733 0742 0744 0725 0711 0698Hunan 068 0671 068 0731 0745 0947 1012 098 1092 1001Chongqing 0625 063 0639 0688 0705 0698 0707 0715 0689 0691Sichuan 0625 0633 0643 0707 0722 0703 0702 0686 0664 0653Guizhou 0596 0591 059 0635 0632 0636 0634 0638 0645 063Yunnan 0619 0601 0602 0628 0631 0632 0636 063 0615 0613Average 0719 0720 0725 0766 0772 0803 0795 0781 0777 0771

Table 5 Global Moranrsquos I Index of green innovation efficiency

Moranrsquos I z2008 0540lowastlowastlowast 35442009 0510lowastlowastlowast 34152010 0514lowastlowastlowast 34322011 0533lowastlowastlowast 34762012 0487lowastlowastlowast 32582013 0313lowastlowast 20222014 0252lowastlowast 17562015 0233lowastlowast 16222016 0226lowast 16342017 0209lowast 1567Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

8 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

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[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

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[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

the green innovation efficiency of the middle and lowerreaches of the province showed a trend of increasing firstand then decreasing slightly while that of the upper andlower reaches showed a trend of decreasing first and thenincreasing and then decreasing but the fluctuation rangewas small Since the promulgation of several opinions of theState Council on promoting the rise of the central region in2005 the industrial undertaking policies to promote the riseof the central and western regions have promoted the inflowof a large number of capital and labor factors which has ledto the economic growth of the central and western regionsHowever the industrial undertaking has brought economicbenefits as well as unexpected output making the greeninnovation efficiency of the middle and upper reaches ofprovinces at it is low and declining and only in recent yearsdoes it show an upward trend In 2014 the policy of buildingthe YREB into a leading demonstration zone of ecologicalcivilization was issued Since then the state and localgovernments of the YREB have successively issued relevant

policies and the construction of ecological civilization in theYREB has achieved initial results

Table 2 e influencing factors of green innovation efficiency in YREB

Variable DescriptionEconomic development (ED) GDP per capitaEnvironmental regulation (ER) e ratio of total investment in industrial pollutants to GDPGovernment support (GS) RampD fundsForeign direct investment (FDI) e proportion of foreign investment as a percentage of the regional GDPIndustrial structure (IS) Proportion of total output value of tertiary industry to total GDP in each region

Table 3 Descriptive statistics of green innovation efficiency in YREB

Index Minimum Maximum Mean Standard deviationNumber of RampD employees (10000 people) 12656 466735 111834 134245Total investment in RampD (Billion yuan) 324986 4365780 4326382 6023576Total energy consumption (tons of standard coal) 4658 30480 13762 6187New product sales revenue (billion yuan) 3795210 7456754 7134578 7238568Number of patent applications (billion) 1785 125784 32650 33468Exhaust emissions (10000 tons) 8730 64390 23561 13652Wastewater discharge (10000 tons) 14370 455321 138542 100654Solid waste (10000 tons) 1450 17890 8974 4376

Table 4 Green innovation efficiency of YREB in 2008ndash2017

RegionYear

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017Jiangsu 1032 1053 1047 1092 1085 1066 1023 1026 1024 1031Shanghai 1042 1035 1037 1032 1013 1021 1032 1027 1034 1089Zhejiang 0765 0763 0768 0827 0845 0976 0853 0812 0743 0751Anhui 0652 0654 0667 0706 071 0733 0724 0697 0681 0678Jiangxi 0622 0623 0631 0673 0668 0677 0682 0653 0646 0643Hubei 0649 0661 0667 0711 0733 0742 0744 0725 0711 0698Hunan 068 0671 068 0731 0745 0947 1012 098 1092 1001Chongqing 0625 063 0639 0688 0705 0698 0707 0715 0689 0691Sichuan 0625 0633 0643 0707 0722 0703 0702 0686 0664 0653Guizhou 0596 0591 059 0635 0632 0636 0634 0638 0645 063Yunnan 0619 0601 0602 0628 0631 0632 0636 063 0615 0613Average 0719 0720 0725 0766 0772 0803 0795 0781 0777 0771

Table 5 Global Moranrsquos I Index of green innovation efficiency

Moranrsquos I z2008 0540lowastlowastlowast 35442009 0510lowastlowastlowast 34152010 0514lowastlowastlowast 34322011 0533lowastlowastlowast 34762012 0487lowastlowastlowast 32582013 0313lowastlowast 20222014 0252lowastlowast 17562015 0233lowastlowast 16222016 0226lowast 16342017 0209lowast 1567Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

8 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

42 Spatial Autocorrelation Analysis

421 Global Spatial Autocorrelation is paper usesMATLAB to calculate the Global Moranrsquos I Index of logisticsgreen innovation efficiency in YREB Table 5 presents the results

e results show that the Global Moranrsquos I Index of thegreen innovation efficiency passed the test at 10 signifi-cance level in 2008ndash2017 indicating that the green inno-vation efficiency displays a positive spatial correlation it isnot distributed randomly the index is basically between 03and 06 and it indicates that the green innovation efficiencyshows a weak agglomeration state In the main the GlobalMoranrsquos I Index moves upward along a wave-like curve thismay be related to the macroeconomic environment in whichthe Chinese economy enters the ldquonew normalrdquo and theindustrial structure is transformed and upgraded

e high and low efficiency neighboring provinces showa spatial cluster e higher green innovation efficiencyregions were adjacent and the regions with lower greeninnovation efficiency were close to each other

422 Spatial Effect of Green Innovation EfficiencyBecause the data used in this paper are panel data it isnecessary to determine whether the fixed effect model or therandom effect model should be used before regressionanalysis of the model e Hausman test was carried out forSLM and SEM byMATLAB and the test results are shown inTable 6

According to Table 6 both SLM and SEM passed theHausman test at 5 significance level so the panel modelwith fixed effect was selected for regression analysis

According to the test of spatial correlation Table 7 showsthat the test of LM_lag is greater than LM_error so theestimation method of spatial lag model is studied andanalyzed

e general OLS regression coefficient is smaller than thespatial Durbin model (SDM) which shows that OLS re-gression ignores the spatial interaction between independentvariables and dependent variables and overestimates theinfluence of related variables From the regression results ofthe SDMmodel the log-L and R2 of the spatiotemporal fixedmodel are obvious and it is larger than the fixed time modeland the fixed space model so the double fixed model has thebest estimation results Table 8 is an analysis of the influ-encing factors of green innovation efficiency based on theempirical results of the time-space fixed model e resultsare shown in Table 8

(1) Economic development (ED) has a significant pos-itive role in promoting the green innovation effi-ciency of the YREB For every 1 increase in GDPper capita the efficiency of green innovation willincrease by an average of 03487 It shows thateconomic growth will improve the green innovationefficiency Economically developed regions in theYREB on the one hand will pay more attention tothe development of environmental quality on theother hand the RampD investment in the field of greeninnovation will increase and the investment

subsidies and production subsidies for products andservices will be greater e coefficient of the spatiallag term of economic growth is minus01593 and throughthe 1 significance test it shows that the economicgrowth of the neighboring areas in the YREB hasnegative spatial spillover effects to the green inno-vation efficiency of the region is is because theeconomic development of the neighboring areas willhave a certain siphon effect on the relevant inno-vation elements of the region which is not conduciveto the improvement of the green innovation effi-ciency of the region

Table 6 Hausman test results

Test summary Hausman test-statistic VarianceSLM 29631lowastlowastlowast 15SEM 231586lowastlowastlowast 29Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 7 Spatial correlation test results

Spatialdependencetest

LM_lag RobustLM_lag LM_error Robust

LM_error

68237lowastlowastlowast 19632lowastlowastlowast 50792lowastlowastlowast 10011lowastlowastlowast

Note lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Table 8 Estimation and test results based on spatial Durbin model(SDM) for the driving factor

OLS TF SF STF

LnED 01813lowastlowastlowast 01255lowastlowastlowast 02536lowastlowastlowast 03487lowastlowastlowast717 423 536 691

LnIS 00521lowastlowastlowast 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast082 132 143 038

LnFDI 0023lowastlowastlowast 0017 1551 00270639 235 375 411

LnGS 0003lowastlowastlowast 0003lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast038 013 245 299

LnER 0002lowastlowastlowast 0003lowastlowastlowast 0019lowastlowastlowast 0005lowastlowastlowast117 204 291 067

WlowastLnED minus01675lowastlowastlowast minus02036lowastlowastlowast minus01593lowastlowastlowastminus234 minus312 minus402

WlowastLnIS 0074lowastlowastlowast 00867lowastlowastlowast 01356lowastlowastlowast132 122 046

WlowastLnFDI 0017lowast 1551 0027235 400 206

WlowastLnGS 0001lowastlowastlowast 0030lowastlowast 0029lowastlowastlowast013 240 299

WlowastLnER 0004lowastlowastlowast 0021lowastlowastlowast 0003lowastlowastlowastminus044 minus022 minus044

ρ 0434lowastlowastlowast minus0464lowastlowastlowast minus0003lowastlowastlowast minus0117lowastlowastlowast757 753 476 096

R2 0642 0643 0709log-L 568895 683685 709185lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 9

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

(2) Industrial structure (IS) has a significant positive roleat the level of 5 In the TF model the coefficient isalso significantly positive is is mainly because inthe adjustment and upgrading of industrial structurein the YREB those high energy consumptions andhigh pollution situation have improved However inthe process of promoting the industrial structure toachieve a high degree and rationalization in a certainregion in the YREB it may cause the imitation ofneighboring regions and promote the coordinateddevelopment of regional green systems in differentregions

(3) Foreign direct investment (FDI) has no significanteffect on the green innovation efficiency of the YREBAt the same time the corresponding spatial lag hasnot passed the significance test which means thatwhen the YREB regions introduce FDI they neitherpromote the efficiency of local green innovation norbring spillover effects to the efficiency of green in-novation in the surrounding areas e reason maybe that FDI does not really consider regional envi-ronmental technology innovation in most provincesor even occupies the provincial RampD innovation ofthe YREB and inhibits the technological innovationability and the green innovation efficiency of in-troducing foreign investment is not ideal is alsomeans that the purpose of most FDI entry is topursue low cost and tax advantages It does not reallyconsider environmental technology innovation andthe quality of investment still needs to be furtherimproved

(4) Government support (GS) has a significant positiverole in promoting the green innovation efficiency ofthe YREB e governmentrsquos support for green in-novation activities can improve the developmentlevel of scientific and technological research anddevelopment subsystem so as to reduce the unex-pected output in the process of achievement trans-formation increase the expected output andpromote the coordinated development of green in-novation system e government should continueto increase its support for green innovation in theYREB especially in the less developed areas such asthe central and western regions By promoting thedevelopment of technological innovation we candevelop more technologies that are beneficial to theecological environment and promote the coordi-nated development of green innovation system

(5) Environmental regulation (ER) has significant pos-itive effect on the green innovation efficiency of theYREB For every 1 increase in the level of envi-ronmental regulation the green innovation effi-ciency will increase by an average of 0005 whichmeans that the more stringent the environmentalregulation is the stronger the environmental pol-lution cost constraints enterprises bear so that theyhave the motivation to pay attention to the

production of clean ecological and recycling andthe enterprises that take the lead in technologicalinnovation have the first mover advantage in pol-lution control It is helpful for enterprises to seizemarket share and gain competitive advantage and itis also helpful for enterprises to improve their greeninnovation performance e coefficient of thespatial lag term of environmental regulation isminus0003 and through the 1 significance test itshows that the environmental regulation of theneighboring areas has negative spatial spilloverbenefits to the green innovation efficiency of theregion is may be due to the deterrence effect ofenvironmental regulations on enterprises whichforces enterprises to increase investment in envi-ronmental governance so that the correspondinglow-tech pollution links are transferred to otherareas with relatively low environmental standardsleading to the ldquopollution shelterrdquo effect and inhib-iting green innovation

423 Spatial Spillover Effects of Green Innovation EfficiencyBased on the SDM model this paper analyzes the directindirect and total effects of various influencing factorsamong which the total effect represents the average impact ofinfluencing factors on green innovation while the directeffect and indirect effect represent the decomposition of thetotal effect which respectively represents the impact ofinfluencing factors on the region and adjacent regionsTable 9 are the results

e indirect effect of the level of economic developmentis negative which shows that in general the level of eco-nomic development is not conducive to the spatial spilloverof green innovation efficiency in the period under investi-gation especially in the provinces with the higher level ofeconomic development the lower the spatial spillover effectsof green innovation efficiency which highlights that themore developed provinces pay more attention to ldquoprotectrdquothe efficiency of green innovation in their own provinces inthe YREB Under the requirements of national green de-velopment the economically developed regions in the YREBmake use of their own advantages in capital and the eco-nomically underdeveloped regions are eager to improve thelocal economic level and transfer some high energy con-sumption high pollution and high emission industries tothe economically underdeveloped provinces which to someextent causes the green innovation efficiency to show neg-ative spatial spillover effects

e total effect direct effect and indirect effect of in-dustrial structure are all positive e optimization of in-dustrial structure is conducive to the transformation ofdevelopment mode reduction of energy consumption andenvironmental pollution so as to improve the efficiency ofgreen innovation

FDI in this region has no significant impact on thegreen innovation efficiency of surrounding areas in theYREB It shows that there are corresponding regionaltechnical barriers in the process of promoting the

10 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

efficiency of green innovation in all regions of YREB Onthe one hand they are unwilling to cooperate and sharethe technical innovation on the other hand they are easyto transfer the cost of environmental pollution to thesurrounding areas

e direct effect regression coefficient is positive underthe significance of 5 and the indirect effect regressioncoefficient fails to pass the significance test Governmentsupport is a strong backing to enhance the capacity of greeninnovation especially along with the transformation andupgrading driven by green innovation in the YREB and thegovernment has given strong support in building basicinnovation platform and increasing investment in innova-tion and RampD e guiding effect of green innovation policyis significant laying a solid foundation for the high-qualitydevelopment of the YREB

e direct effect of environmental regulation on thespatial spillover of green innovation efficiency is positive andpasses the significance test the indirect effect and the totaleffect on the productivity of green innovation pass thesignificance test of 5 but has a negative impact on thechange in green innovation efficiency is shows that onthe one hand environmental regulation has a positive role inpromoting the efficiency of green innovation in our prov-ince but at present it has not fundamentally changed thelevel of green development in China so it cannot signifi-cantly improve the efficiency of green innovation in ChinaOn the other hand when Chinarsquos green innovation capacityis insufficient the imbalance of the intensity of interpro-vincial environmental regulation is likely to lead the envi-ronmental pollution industry in the provinces with highintensity of regulation to enter the provinces with low in-tensity of regulation in the YREB

5 Conclusions and Discussion

51 Conclusions is study used super-SBM model toconsider undesirable outputs measuring the green inno-vation efficiency in YREB from 2008 to 2017 Since greeninnovation efficiency has spatial spillover effects therefore aspatial econometric model SDM model is applied to analyzethe influencing factors of green innovation efficiency egreen innovation efficiency empirical results indicate thegreen innovation efficiency is developing slowly and the

green innovation of the eastern part of YREB is significantlybetter than that of the lower reaches in the west From thespatial autocorrelation result it shows that there is a sig-nificant spatial autocorrelation of green innovation effi-ciency in YREB regions From the spatial econometric ofSDM analysis indicating that the level of economic devel-opment foreign direct investment to the outside world andenvironmental pollution control has positive effects on thegreen economic efficiency of the YREB while the proportionof the secondary industry has negative effects e greeneconomic efficiency of the YREB has a significant spatialcorrelation e provinces with high level of economicdevelopment and environmental pollution control have asignificant positive role in promoting the green economicefficiency of the neighboring provinces e provinces withhigh proportion of the secondary industry and high gov-ernment support have a negative inhibitory effect on thegreen economic efficiency of the neighboring provinces

52 Discussion According to the empirical results thisstudy put forward proposals to enhance green innovationefficiency

First the optimization and upgrading of industrialstructure is promoted Industrial structure has a significantnegative inhibitory effect on the green innovation efficiencyof the YREB so it is necessary to speed up the pace ofindustrial structure adjustment and new industrializationWe will bring superiority into full play of industry andintelligence intensity in the YREB vigorously implementinnovation-driven development strategy add to newmomentum of reform innovation and developmentsubtract from the elimination of backward productioncapacity and accelerate industrial transformation andupgrading We will build a manufacturing innovationsystem improve the ability to develop key systems andequipment and foster and expand high technology in-dustries emerging sectors of strategic importanceequipment manufacturing and other industries We willoptimize the layout of strategic emerging industries ac-celerate the construction of regional characteristic in-dustrial bases give free rein to radiation driving andleading demonstration and form a national strategicemerging industry development highland

Second the quality of opening up is improved etechnology spillover effects of FDI in the YREB are morethan the environmental pollution effect e introduction ofFDI can improve green economic development level in theYREB but improvement effect is not significant so weshould further improve the quality of opening up Weshould further promote the improvement of the negative listof market access in the YREB improve the project accessmechanism promote the formation of an institutionalizedstandardized green transparent and procedural system forforeign capital introduction system vigorously introducenew green technologies and industries attract environ-mentally friendly enterprises to settle down give full play tothe technology spillover effects of green foreign capital in-dustries and improve the green production of local

Table 9 Spatial spillover effects of green innovation efficiency

Direct effect Indirect effect Total effect

LnED 0244lowastlowastlowast minus0232lowastlowastlowast 0012lowastlowastlowast520 448 882

LnIS 0038lowastlowastlowast 0047lowastlowastlowast 0085lowastlowastlowast129 089 127

LnFDI 0162lowast 0414 0576145 368 287

LnGS 0025lowastlowastlowast 0034 0059lowastlowast256 328 262

LnER 0381lowastlowastlowast minus0463lowastlowast minus0082lowast141 186 112

lowastlowastlowast lowastlowast and lowast represent the significance level at 1 5 and 10

Complexity 11

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

enterprises to build a green ecological industrial chain andimprove the level of green development and ecologicalquality

Finally we will intensify efforts to prevent and controlenvironmental pollution Environmental pollution controlin the YREB has a positive role in improving green inno-vation efficiency but it has not produced significant resultsso the efforts of environmental pollution control need to befurther strengthened We should strengthen the joint pre-vention and control of environmental pollution establishand improve the emergency response mechanism for crossdepartment cross region and cross basin environmentalemergencies strictly control industrial pollution dispose ofurban sewage and garbage control agricultural nonpointsource pollution prevent ship and air pollution strengthenthe collaborative protection of ecological environment es-tablish a negative list management system strengthen dailymonitoring and supervision and strictly implement theecological environment e system of responsibility in-vestigation for environmental damage should be improvedthe proportion of resource utilization rate environmentalpollution prevention and control and quality evaluationsystem of economic and ecological development should beincreased and the performance evaluation system reflectingthe requirements of ecological civilization should beimproved

Data Availability

e data used to support the findings of this study areavailable from the corresponding author upon request

Disclosure

Hangyuan Guo is the co-first author

Conflicts of Interest

e authors declare that there are no conflicts of interest

Acknowledgments

is paper was supported by Wonkwang University in 2020

References

[1] Y Yuan R Luo and Y Li ldquoAnalysis of the development leveland spatial evolution characteristics of ecological civilizationin the Yangtze river economic beltrdquo Statistics and Decisionvol 1 no 20 pp 98ndash101 2016

[2] L W Lu D Y Song and X F Li ldquoResearch on green ef-ficiency of urban development in the Yangtze river economicbeltrdquo China Population Resources and Environment vol 26no 6 pp 35ndash42 2016

[3] Q Luo C Miao L Sun X Meng and M Duan ldquoEfficiencyevaluation of green technology innovation of Chinarsquos strategicemerging industries an empirical analysis based on Malm-quist-data envelopment analysis indexrdquo Journal of CleanerProduction vol 238 Article ID 117782 2019

[4] C Ghisetti S Mancinelli M Mazzanti and M Zoli ldquoFi-nancial barriers and environmental innovations evidence

from EU manufacturing firmsrdquo Climate Policy vol 17no sup1 pp S131ndashS147 2017

[5] D Li M Zheng C Cao X Chen S Ren and M Huang ldquoeimpact of legitimacy pressure and corporate profitability ongreen innovation evidence from China top 100rdquo Journal ofCleaner Production vol 141 pp 41ndash49 2017

[6] M Cao J Ukko and T Rantala ldquoSustainability as a driver ofgreen innovation investment and exploitationrdquo Journal ofCleaner Production vol 179 pp 631ndash641 2018

[7] T Bernauer S Engel and D Kammerer ldquoExplaining greeninnovation ten years after porterrsquos win-win proposition howto study the effects of regulation on corporate environmentalinnovationrdquo Politische Vierteljahresschrift vol 39 pp 323ndash341 2007

[8] C Fussier and P James Book Review Driving Eco-InnovationA Breakthrough Discipline for Innovation and Sustainability[CrossRef] p 297 Pitman Pub Wetherby UKPitman Pub1996

[9] R Kemp and A Arundel Survey indicators for environmentalinnovation IDEA (indicators and data for European analysis)Sub-project 22 Indicators on the importance of environ-mental goals 26 1998

[10] L Ahlvik P Ekholm K Hyytiainen and H Pitkanen ldquoAneconomic-ecological model to evaluate impacts of nutrientabatement in the Baltic Seardquo Environmental Modelling ampSoftware vol 55 pp 164ndash175 2014

[11] T Heffels R McKenna and W Fichtner ldquoAn ecological andeconomic assessment of absorption-enhanced-reforming(AER) biomass gasificationrdquo Energy Conversion and Man-agement vol 77 pp 535ndash544 2014

[12] Y Chen C Jayaprakash and E Irwin ldquoreshold manage-ment in a coupled economic-ecological systemrdquo Journal ofEnvironmental Economics and Management vol 64 no 3pp 442ndash455 2012

[13] Z J Feng and W Chen ldquoSources of technology and the totalfactor productivity growth of R amp D innovation based onChina regional big medium-sized industrial enterprisesrdquoScience of Science and Management of S amp T vol 34 no 3pp 33ndash41 2013

[14] Y Ren C K Niu T Niu and X L Yao ldquoResearch on thegreen Innovation efficiency model and empirical analysisrdquoManagement World vol 7 pp 176-177 2014

[15] Q Yin and Y Chen ldquoStudy on the regional differences andcauses of green innovation efficiency in Chinardquo Jiangsu SocialSciences vol 18 no 2 pp 64ndash69 2016 [CrossRef]

[16] T Charoenrat and C Harvie ldquoe efficiency of SMEs in aimanufacturing a stochastic frontier analysisrdquo EconomicModelling vol 43 pp 372ndash393 2014

[17] H Li J Zhang C Wang Y Wang and V Coffey ldquoAnevaluation of the impact of environmental regulation on theefficiency of technology innovation using the combined DEAmodel a case study of Xirsquoan Chinardquo Sustainable Cities andSociety vol 42 pp 355ndash369 2018

[18] H W Lampe and D Hilgers ldquoTrajectories of efficiencymeasurement a bibliometric analysis of DEA and SFArdquoEuropean Journal of Operational Research vol 240 no 1pp 1ndash21 2015

[19] C Miao D Fang L Sun Q Luo and Q Yu ldquoDriving effect oftechnology innovation on energy utilization efficiency instrategic emerging industriesrdquo Journal of Cleaner Productionvol 170 pp 1177ndash1184 2018

[20] L Xiao J Gao and S Liu ldquoe change trend of greentechnology innovation efficiency in China based on spatial

12 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

gradientmdashempirical analysis of provincial panel datardquo SoftScience vol 31 pp 63ndash68 2017

[21] E G Carayannis E Grigoroudis and Y Goletsis ldquoA mul-tilevel and multistage efficiency evaluation of innovationsystems a multiobjective DEA approachrdquo Expert Systems withApplications vol 62 pp 63ndash80 2016

[22] P Samut and R Cafri ldquoAnalysis of the efficiency determinantsof health systems in OECD countries by DEA and panel tobitrdquoSocial Indicators Research vol 129 no 1 pp 113ndash132 2016

[23] C Lafarga and J Balderrama ldquoEfficiency of Mexicorsquos regionalinnovation systems an evaluation applying data envelopmentanalysis (DEA)rdquo African Journal of Science Technology In-novation amp Development vol 7 pp 36ndash44 2015

[24] Y Ren and CWang ldquoResearch on the regional difference andspatial effect of green innovation efficiency of industrial en-terprises in Chinardquo Revista Iberica de Sistemase Tecnologias deInformaccedilatildeo vol 10 pp 373ndash384 2016

[25] J-L Du Y Liu and W-X Diao ldquoAssessing regional dif-ferences in green innovation efficiency of industrial enter-prises in Chinardquo International Journal of EnvironmentalResearch and Public Health vol 16 no 6 p 940 2019

[26] M Buesa J Heijs M Martınez Pellitero and T BaumertldquoRegional systems of innovation and the knowledge pro-duction function the Spanish caserdquo Technovation vol 26no 4 pp 463ndash472 2006

[27] R Fare S Grosskopf and GWhittaker ldquoNetwork DEA IIrdquo inData Envelopment Analysis International Series in OperationsResearch amp Management Science W Cook and J Zhu Edsvol 208 pp 307ndash327 Springer Berlin Germany 2014

[28] J Guan and K Zuo ldquoA cross-country comparison of inno-vation efficiencyrdquo Scientometrics vol 100 no 2 pp 541ndash5752014

[29] X Yu Z Li R Chi and M Shi ldquoTechnological innovationefficiency of different regions in China status quo and causesrdquoStudies In Science of Science vol 23 pp 258ndash264 2005

[30] H Wang S Wang Z Miao and X Li ldquoHeterogeneitythreshold effect of R amp D investment on green innovationefficiency based on Chinese high-tech industriesrdquo ScienceResearch Management vol 37 pp 63ndash71 2016

[31] S Yu X Li and Z Peng ldquoEnvironmental regulation modeland green innovation efficiency of the Yangtze river economicbeltrdquo Jianghai Academic Journal vol 3 pp 209ndash214 2017

[32] S Yang T Wu and Z Li ldquoStudy on the spatial-temporaldifferences and influencing factors of green innovation effi-ciency in the Yangtze river economic beltrdquo MACROECO-NOMICS vol 6 pp 107ndash132 2018

[33] X Gong M Li and H Zhang ldquoHas OFDI promoted theindustrial enterprisesrsquo green innovation efficiency in Chi-namdashmdashevidence based on agglomeration economic effectrdquoJournal of International Trade vol 11 pp 127ndash137 2017

[34] W D Cook and L M Seiford ldquoData envelopment analysis(DEA)-irty years onrdquo European Journal of OperationalResearch vol 192 no 1 pp 1ndash17 2009

[35] H Li K Fang W Yang D Wang and X Hong ldquoRegionalenvironmental efficiency evaluation in China analysis basedon the super-SBM model with undesirable outputsrdquo Math-ematical and Computer Modelling vol 58 no 5-6pp 1018ndash1031 2013

[36] J Zhang W Zeng J Wang F Yang and H Jiang ldquoRegionallow-carbon economy efficiency in China analysis based onthe super-SBM model with CO2 emissionsrdquo Journal ofCleaner Production vol 163 pp 202ndash211 2017

[37] K Tone ldquoA slacks-based measure of efficiency in data en-velopment analysisrdquo European Journal of Operational Re-search vol 130 no 3 pp 498ndash509 2001

[38] 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

[39] F Jin and L Lee ldquoOn the bootstrap for Moranrsquos I test forspatial dependencerdquo Journal of Economics vol 184pp 295ndash314 2015

[40] S J Rey ldquoSpatial empirics for economic growth and con-vergencerdquo Geographical Analysis vol 33 no 3 pp 195ndash2142001

[41] Y Xiong D Bingham W J Braun and X J Hu ldquoMoranrsquos Istatistic-based nonparametric test with spatio-temporal ob-servationsrdquo Journal of Nonparametric Statistics vol 31 no 1pp 244ndash267 2019

[42] T Zhang and G Lin ldquoOn Moranrsquos I coefficient under het-erogeneityrdquo Computational Statistics amp Data Analysis vol 95pp 83ndash94 2016

[43] H D Liu ldquoe inside outside and space spillover effects ofregional innovationrdquo Science Research Management vol 34no 1 pp 28ndash36 2013

[44] J P Elhorst ldquoDynamic spatial panels models methods andinferencesrdquo Journal of Geographical Systems vol 14 no 1pp 5ndash28 2012

[45] J P LeSage and R K Pace Introduction to Spatial Econo-metrics CRC Press Boca Raton FL USA 2009

[46] K R Zuo and J C Gong ldquoExploring the change and influencefactors of R amp D efficiency at province-level of Chinardquo Scienceof Science and Management of S amp T vol 37 no 4 pp 79ndash882016

[47] H Chen H Lin and W Zou ldquoResearch on the regionaldifferences and influencing factors of the innovation efficiencyof Chinarsquos high-tech industries based on a shared inputs two-stage network DEArdquo Sustainability vol 12 no 8 p 32842020

[48] K Rennings ldquoRedefining innovation-eco-innovation researchand the contribution from ecological economicsrdquo EcologicalEconomics vol 32 no 2 pp 319ndash332 2000

[49] M E Porter and C Van Der Linde ldquoGreen and competitiveending the stalematerdquoHarvard Business Review vol 73 no 5pp 120ndash134 1995

[50] J Horbach C Rammer and K Rennings ldquoDeterminants ofeco-innovations by type of environmental impact-the role ofregulatory pushpull technology push and market pullrdquoEcological Economics vol 78 pp 112ndash122 2012

[51] E Kesidou and P Demirel ldquoOn the drivers of eco-innova-tions empirical evidence from the UKrdquo Research Policyvol 41 no 5 pp 862ndash870 2012

[52] K Rennings and C Rammer ldquoe impact of regulation-driven environmental innovation on innovation success andfirm performancerdquo Industry amp Innovation vol 18 no 3pp 255ndash283 2011

[53] B R Copeland and M S Taylor ldquoNorth-South trade and theenvironmentrdquo Ne Quarterly Journal of Economics vol 109no 3 pp 755ndash787 1994

[54] J X Zhang N Cai J S Mao and C Yang ldquoIndependentinnovation technology introduction and green growth ofindustry in China an empirical research based on industryheterogeneityrdquo Studies in Science of Science vol 33 no 2pp 185ndash194 2015

[55] M H Kim and N Adilov ldquoe lesser of two evils an em-pirical investigation of foreign direct investment-pollution

Complexity 13

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity

tradeoffrdquo Applied Economics vol 44 no 20 pp 2597ndash26062012

[56] K X Bi C J Yang and P Huang ldquoResearch on the impact ofFDI on the green process innovation of Chinesemanufacturing industries an empirical analysis based on thepanel datardquo China Soft Science vol 20 no 9 pp 172ndash1802011

[57] J Horbach ldquoDeterminants of environmental innovation-newevidence from German panel data sourcesrdquo Research Policyvol 37 no 1 pp 163ndash173 2008

[58] M C Cuerva A Triguero-Cano and D Corcoles ldquoDrivers ofgreen and non-green innovation empirical evidence in low-tech SMEsrdquo Journal of Cleaner Production vol 68 pp 104ndash113 2014

[59] L Luo and S Liang ldquoStudy on the efficiency and regionaldisparity of green technology innovation in Chinarsquos industrialcompaniesrdquo Chinese Journal of Population Resources andEnvironment vol 14 no 4 pp 262ndash270 2017

[60] P C Zhu D H Liu and X H Huang ldquoAn evaluation ofscience and technology innovation efficiency of cities from thedynamic perspective by taking 9 prefecture-level cities inFujian Province as an examplerdquo Science Research Manage-ment vol 38 no 6 pp 43ndash50 2017

[61] R Y Long H Z Ouyang and H Y Guo ldquoSuper-slack-basedmeasuring data envelopment analysis on the spatial-temporalpatterns of logistics ecological efficiency using globalMalmquist index modelrdquo Environmental Technology amp In-novation vol 18 Article ID 100770 2020

14 Complexity