researchondynamiccomprehensiveevaluationofresource...

Research ArticleResearch on Dynamic Comprehensive Evaluation of ResourceAllocation Efficiency of Technology Innovation in theAerospace Industry

Han Dongri Tuochen Li Ziyi Shi and Shaosong Feng

School of Economics and Management Harbin Engineering University Harbin 150001 China

Correspondence should be addressed to Tuochen Li m18845105191163com

Received 6 December 2019 Accepted 10 February 2020 Published 12 March 2020

Academic Editor Georgios I Giannopoulos

Copyright copy 2020 Han Dongri 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

From the perspective of input and output this paper constructs an evaluation index system for the status quo of technologyinnovation resource allocation in Chinarsquos aerospace industry Taking the industrial panel data of 20 provincial regions from 2007to 2016 in China as samples this paper uses the stochastic frontier method which is improved by the projection pursuit modelbased on accelerated genetic algorithm to analyze the factors influencing the allocation efficiency of technology innovationresource in the aerospace industry and thenmake a static evaluation for the current situation In addition based on the perspectiveof velocity characteristics this study uses the dynamic comprehensive evaluation model to evaluate the resource allocation oftechnology innovation in the aerospace industry -e empirical research shows that the resource allocation efficiency oftechnology innovation in the aerospace industry is generally at the lower middle level indicating an unbalanced trend of ldquoreverserdquoallocation with the level of regional economic development It is also found that the efficiency improvement effect in recent years isnot obvious At last based on the studyrsquos findings some countermeasures and suggestions are put forward to improve thecurrent situation

1 Introduction

-e 19th National Congress of the Communist Party ofChina was proposed to strengthen strategic and scientificforces and accelerate the construction of innovation-ori-ented country -e aerospace industry is a typical high-techindustry and as the leading force in the creation of spacepower it plays a vital role in promoting the upgrading ofindustrial quality and efficiency Its technical level and in-dustrial strength are not only the necessary conditions forthe improvement of comprehensive national strength butalso the comprehensive reflection of the modernization levelof Chinarsquos defense-related science and technology industrywhich is an essential part of national defense securityconstruction Unlike other technological powers such asBritain and the United States China as an emergingeconomy started its aerospace industry late with a smallscale and a relative lack of talent and infrastructure con-struction In the past 70 years the achievements of Chinarsquos

aerospace industry have mainly benefited from the supportof national policies but government policies are by nomeans omnipotent especially under the impact of the waveof internationalization andmarketization Chinarsquos aerospaceindustry has shown specific deep-rooted economic problemssuch as ldquounbalanced structurerdquo and ldquoinsufficient innova-tionrdquo which are particularly prominent in the new normal ofChinarsquos economy -ere are no more than two solutions tothese problems one is to promote industrial development byincreasing factor input the other is to improve the efficiencyof factor resource allocation through policy guidance As weall know the extensive economic growth model that reliessolely on factor input has become unsustainable In contrastthe latter may be a more reasonable way which is also themain issue discussed in this study

-e key to the development of the aerospace industry liesin technology innovationMoreover effective utilization andrational allocation of innovation resource are of practicalsignificance for improving technology innovation ability [1]

HindawiMathematical Problems in EngineeringVolume 2020 Article ID 8421495 15 pageshttpsdoiorg10115520208421495

As efficiency is the core index for evaluating industrialtechnology innovation capability the study on the efficiencyof resource allocation of technology innovation is conduciveto comprehensively investigating the process of resourceinput and innovation output in technology innovation ac-tivities and then finding out the existing institutional con-straints and institutional barriers [2 3] However there arefew papers to evaluate the resource allocation efficiency oftechnology innovation with the aerospace industry as theresearch object -erefore it is necessary to explore thetechnology innovation resource allocation situation of Chinarsquosaerospace industry and to reveal the key factors influencingthe allocation efficiency and mechanism which are of the-oretical significance to enrich the evaluation of innovationefficiency and of practical relevance to both coordinate theoptimal allocation of the aerospace industry resources andrealize the development pattern of aerospace industry power

2 Literature Review

At present there is no authoritative definition of the con-notation of resource allocation efficiency in academic circles[1] Instead relevant research results mainly focus on theefficiency of technology innovation and the allocation ofscientific and technical resources

-e concept of technology innovation efficiency was firstproposed by Afriat referring to the input-output variable ofeffective technology in RampD innovation activities [4] -emeasurement of technology innovation efficiency includesmostly two methods the parametric method and thenonparametric method One of the nonparametric tech-niques which are commonly used is the data envelopmentanalysis (DEA) [5] Scholars have carried out a lot of studieson regional innovation efficiency [5ndash7] industrial tech-nology innovation efficiency [8 9] and other issues based ondifferent research perspectives building different indicatorsystems and selecting various sample data In addition oneof the parametric methods which are commonly used is thestochastic frontier analysis (SFA) [10] At present the SFAmethod has been widely applied to evaluate innovationefficiency at many levels such as regional level [11] industriallevel [12ndash14] and research structure [15 16] Besidesscholars have also carried out some research on the influ-encing factors of technology innovation efficiency mainlyinvolving some traditional industrial organization factorssuch as ownership structure [17ndash19] enterprise scale[20ndash23] and market competition degree [24 25]

Regarding the allocation of technological resources theresearch priorities by domestic and foreign scholars aredifferent International scholars paid more attention to theoptimization effect of the implementation of technologypolicies and plans on the allocation of national technologyresources from the national level Campolina et al [26]Chaminade and Plechero [27] and other scholars proposedthe key factors to optimize the allocation path of science andtechnology resources by comparing the allocation methodsand expenditure on science and technology of variouscountries Meantime because enterprises play an indis-pensable role in the market economy of developed countries

and basically represent the science and technology inno-vation ability of the whole society many scholars started tostudy the allocation of science and technology resourcesfrom the perspective of enterprises Pelinand [28] Endo[29] and other scholars verified the mechanism of theimpact of government RampD subsidies corporate strategyand cost advantages on RampD resource allocation Howeverdomestic scholars mostly paid more attention on the scienceand technology resource allocation system mechanism andthe ability from the regional level which is based on the factsof Chinarsquos unbalanced local economic development Fanet al [30] Li andWen [31] and Chen et al [32] evaluated theregional science and technology resource allocation effi-ciency in different periods as well as analyzed the mainfactors affecting the improvement of the local science andtechnology resource allocation efficiency In addition Chenet al [32] and Huang and Zhang [33] respectively analyzedthe allocation efficiency of science and technological re-sources in Chinarsquos strategic emerging industries agriculturalcolleges and universities A brief presentation of previousstudies and their findings is presented in Table 1

In summary scholars have conducted in-depth researchon technology innovation efficiency and technology re-source allocation from the enterprise regional and nationallevels However there are few relevant analyses on the ef-ficiency of resource allocation of technology innovation andtechnology innovation in the aerospace industry in theliterature on the factors influencing the effectiveness oftechnology innovation scholars hold different views on thesame influencing variable -e influencing mechanism ofresource allocation efficiency in the aerospace industry ismore like a ldquoblack boxrdquo Accordingly this study compre-hensively considers both the technology innovation and theefficiency of science and technology resource allocationBased on the sample data of 20 provinces and cities thispaper uses the RAGA-PP-SFA model and the dynamiccomprehensive evaluation model to evaluate the resourceallocation efficiency of technology innovation in the aero-space industry from the regional level as well as to assess theextensive dynamic development trend during the wholeevaluation period so as to enrich the relevant studies

3 Research Model

31 Static EvaluationModel DEA and SFA are two effectivemethods of efficiency evaluation which are constantly im-proved in the application process For example PDEA solvesthe problem of inaccurate and ambiguous data in the tra-ditional DEA model [34] However the DEA method sets adeterministic boundary in the efficiency measurement sothat measurement errors are not allowed that is all devi-ations from the production boundary or cost boundary areattributed to low efficiency which is not in line with theactual situation [23] Compared with the DEA methodlacking risk considerations and statistical characteristics theSFA has the following two advantages in empirical analysisOn the one hand the SFA divides the actual output intothree parts production function random perturbation andtechnical inefficiency and divides the error term into two

2 Mathematical Problems in Engineering

Table 1 Existing literatures on measurement of technology innovation efficiency

Study Researchmethod Research context Period Findings

Zemtsov andKotsemir [5] DEA

Measurement of the efficiency ofRussiarsquos regional technology system

(RIS)2009ndash2012

RIS efficiency in Russia increased during theperiod especially in the least developedterritories RIS efficiency was higher in

technologically more developed regions with theoldest universities and larger patent stock

Chen and Fu[6] Dynamic DEA Evaluation on efficiency of RampD input-

output systems in Chinarsquos provinces 2006ndash2010

All regions had deficiencies in the overalloperation of the RampD and production system theoverall efficiency of the economically developedeastern coastal areas is significantly higher than

that of inland provinces

Liang et al [7] SBM modelEvaluation on the efficiency of regionalgreen technology innovation in 30

regions of China2006ndash2017

-e efficiency of regional green technologyinnovation is generally low and technology

inefficiency is more serious than scale inefficiency

Sueyoshi andGoto [8]

An improvedDEA model

Discussion on the green technologyinnovation efficiency of Japanese

industries2013ndash2015

In the current business environment productionlimits are likely to occur in most industries with

manufacturing outperformingnonmanufacturing in operating efficiency

Martinez andPina [9]

Two MalmquistDEA

Regional energy efficiency acrossColombian departments in the

manufacturing industries2005ndash2013

Various manufacturing industries acrossColombian departments have a high potential to

increase energy efficiency

Zhang andZhang [11] SFA

Measurement of the efficiency oftechnology innovation in the Beijing-

Tianjin-Hebei region2011ndash2015

-e overall innovation efficiency of Beijing-Tianjin-Hebei is relatively low but the

innovation efficiency of Hebei province isbasically higher than the average of Beijing-

Tianjin-Hebei

Wang et al[12] SFA

Measurement of the technologyinnovation efficiency of Xinjiangrsquosequipment manufacturing industry

2000ndash2014

-e technology innovation efficiency ofXinjiangrsquos equipment manufacturing industryand its subsectors is not high but the efficiencyhas improved significantly in recent years there isnot much difference between the subsectors and

there is still much room for improvement

Yi et al [13] SFACalculation and analysis of the

technology innovation efficiency ofChinarsquos high-tech industries

2000ndash2015

-e overall level of high-tech industry`sinnovation efficiency is not high the innovation

efficiency of the high-tech industry amongregions generally shows a fluctuating increaseand the regional distribution of its growth rate is

different

Yin and Chen[14] Two-phase SFA

Innovation efficiency of 103 Shanghai-Shenzhen pharmaceutical listed

companies in China2009ndash2013

-e efficiency of technology innovation inenterprises is characterized by stages resourceutilization in innovation generation phase isbetween 39 and 46 efficiency loss in

innovation transformation stage is less than 35innovation generation promotes thetransformation of innovative output

Yu [15] PP-SFA Measurement of the innovationefficiency of 27 regions in China 2008ndash2015

-e average innovation efficiency of Chineseuniversities is 0509 which is at a medium level as

a whole and the innovation efficiency ofuniversities in different regions is quite different

and their development is uneven

Li and Dong[16] PP-SFA

Measurement of value creationefficiency in 12 branches of Chinese

Academy of Sciences2009ndash2014

-e overall value creation efficiency of CAS is lowand the mean is 058 which could be improved

significantly

Fan et al [30] SBM model-e analysis of regional science and

technology resource allocationefficiency in China

2001ndash2014

-e spatial distribution pattern of regionalscience and technology resource allocation

efficiency is obvious-e efficiency of science andtechnological resource allocation without

considering the nonexpected output is more thanthat of the nonexpected output

Mathematical Problems in Engineering 3

parts the first part V sim N(0 σ2V) is used to reflect the sta-tistical noise (error) and the second part is Uge 0 which isused to reflect the part that is controllable but not optimal[21] By these ways the SFA has advantages in measurementerror and statistical interference processing On the otherhand based on robust economic theory the SFAmethod canquantitatively analyze the impact of related factors includinginput variables and environmental variables on innovationefficiency while measuring the efficiency of technology in-novation [34]

However the SFA method also has an obvious short-coming that it can only explain the efficiency of a singleoutput rather than dealing with the multioutput efficiencyConsequently this study uses the RAGA-PP (real-codedaccelerating genetic algorithm-projection pursuit) model toimprove the SFA method that is to reduce the multidi-mensional output data by optimizing the projection direc-tion with the purpose of reflecting the original high-dimensional data structure and characteristics to the greatestextent Finally by means of optimizing the projection di-rection of the original high-dimensional data globally theone-dimensional optimal output projection value is ob-tained [15 16] In the case of random errors this papercalculates the resource allocation efficiency of technologyinnovation in the aerospace industry and further analyzesthe influence of five factors including government supportand enterprise scale in order to provide decision support forthe policy formulation of technology innovation in theaerospace industry In summary the detailed steps of thestatic evaluation model of the technical innovation resourceallocation efficiency in the aerospace industry are as follows

Step 1 determine the projection value of innovationoutput

z(i)t 1113944

p

j1a(j)ty(i j)t (1)

where a(j)t represents the projection direction of thevariable j (j 1 2 3) of 20 provinces and municipal-ities in the year of t and j is the projection value ofinnovation outputStep 2 construct the projection index function

Q(a) SzDz (2)

where Sz is the standard deviation of z(i)t and DZ

represents the local density of z(i)tStep 3 optimize the projection index functionAccording to the dispersion characteristics of projec-tion values the local projection points are required tobe as dense as possible and it is preferable to formseveral point clusters while the overall projection pointclusters are as dispersed as possible therefore thisarticle constructs an optimization function so as tomake sure the product of the variance of projectionvalues and the local density is the largest Because thefunction is a complex nonlinear function this paperadopts RAGA to conduct global optimization of theoptimal projection direction and maximum functionvalue of the projection index function -e function isconstructed as follows

max Q at( 1113857 SzDz

st 11139363

j1a2(j)t 1

⎧⎪⎪⎨

⎪⎪⎩(3)

Step 4 calculate the technology innovation outputindexCombined with Step (3) the projection value of thetechnology innovation output z(i)t for 10 years iscalculated which is regarded as the technology inno-vation output indexStep 5 calculate the resource allocation efficiency oftechnology innovation in the aerospace industry

-e stochastic frontier model is a stochastic boundarymodel based on parameters with compound perturbationterms Different from the data enveloping analysis methodthis model can not only measure the technical efficiency butalso analyze the nonefficiency factors of innovation

yit f xit t( 1113857exp vit minus uit( 1113857 (4)

Taking the logarithm of both sides of formula (4) weobtain the following equation

Table 1 Continued


Li and Wen[31] SBM-Tobit

Estimation of the allocation efficiency ofscience and technology financial

resources in 27 provinces of China2009ndash2016

-e overall status of resource allocation has notbeen reached there are differences amongregions and there is still much room for

improvement

Chen et al[32]

Catastropheseries method

-e regional differences in theallocation capacity of agriculturaltechnology resource in China

1999ndash2011

-ere is a significant difference in the allocationof agricultural science and technology resourceby region and the fairness of allocation has

generally declined

Huang andZhang [33] DEA model

Analysis on the resource allocationefficiency of the strategic emerging

industries2009ndash2011 Analysis on the resource allocation efficiency of

the strategic emerging industries


lnyit lnf xit t( 1113857 + vit minus uit (5)

where yit represents the innovation output of region i in yeart xit represents the RampD input of region i in the year of tvit minus uit serves the error term and vit is a random variableassuming that vitsimN(0 σ2) is independent of uit which is anonnegative random variable It is expected that the positivehalf of the distribution of uitsimN(mit σ2) reflects the inef-ficiency of production technology namely the loss oftechnology innovation efficiency in the t year in the region ofi In order to systematically reflect the statistical charac-teristics of the variation of resource allocation efficiency wecan set the variance parameter c the expression is

c σ2uσ2u

+ σ2v (6)

where c isin (0 1) reflects the different characteristics of in-novation efficiency in statistics c⟶ 0 the input-outputpoints in all regions are on the production frontier and thenthe least square method can be used When c⟶ 1 itindicates that u accounts for the main component in thedeviation between the actual production unit and theproduction front in each region and the SFAmethod shouldbe adopted -is study uses the translog production func-tion which is more flexible than the CobbndashDouglas pro-duction function and can effectively avoid the deviation ofefficiency estimation caused by improper model setting [21]-e establishment of the translog-random stochastic frontiermodel is as follows

lnyit β0 + β1 ln lit + β2 ln kit + β3 ln lit( 11138572

+ β4 ln kit( 11138572

+ β5 ln lit ln kit + vit minus uit

(7)

where yit represents the overall output of technology in-novation in region i of the t year lit represents the full-timeequivalent of RampD personnel kit represents the RampD capitalstock and β is the parameter to be estimated

On the basis of the stochastic frontier production modelthe nonefficiency function of technology is introduced tofurther analyze the impact of five factors including gov-ernment support enterprise scale market concentrationregional development level and labor quality on the allo-cation efficiency of technology innovation resource in theaerospace industry the model is set to

mit δ0 + δ1GS + δ2ES + δ3MC + δ4RDL + δ5EDS (8)

wheremit represents themean value of the distribution functionof technical inefficiency in technology innovation output GSES MC RDL and EDS represent government support en-terprise size market concentration regional development leveland labor quality respectively δ is a parameter to be estimatedreflecting the degree of influence factors on technical ineffi-ciencyWhen δ lt 0 it means that it has a positive impact on theallocation efficiency of technology innovation resource andδ gt 0 means that it has a negative effect on the allocation ef-ficiency of technology innovation resource

32 Dynamic Comprehensive Evaluation Model -e dy-namic comprehensive evaluation of the resource allocationefficiency of technology innovation in the aerospace industryis based on the static evaluation index In this paper thedynamic evaluation model constructed by Liu et al [35] isused for reference to measure the integration significance-e purpose of the dynamic evaluation is to analyze thedevelopment speed acceleration and overall developmenttrend of the allocation efficiency of technology innovationresource in the aerospace industry so as to make up for thetime-point stagnation of static evaluation and to be morecomprehensive -e specific steps of thorough dynamicassessment are as follows

Step 1 calculate the dynamic change speed vij-e static evaluation timing sequence matrix R isconstituted by the static evaluation value (in which theevaluated object i isin [1 m] and the time pointj [1 n + 1]) and the dynamic change degree vij iscalculated to form the dynamic change speed matrix V-e model is set as follows

R rij1113960 1113961m(n+1)

r11 middot middot middot r1(n+1)

⋮ ⋱ ⋮

rm1 middot middot middot rm(n+1)

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

Vij ri(j+1) minus rij

tj+1 minus tj

gt 0 increasing trend

0 relatively stable

lt 0 decreasing trend

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

V vij1113960 1113961mn

v11 middot middot middot v1n

⋮ ⋱ ⋮

vm1 middot middot middot vmn

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)

Step 2 calculate the dynamic change rate state Siv

According to the definition of definite integral theintegral calculation is carried out at different momentsof dynamic change velocity and the comprehensive areais the state of active change rate obtaining the stateinformation matrix S of dynamic change rate and theevaluation objectrsquos linear change of pace aij in a certainperiod of time the formula is as follows

Siv tj tj+11113872 1113873 1113946

tj+1

tj

vij + t minus tj1113872 1113873 middotvi(j+1)minusvij

tj+1minustj

⎡⎢⎣ ⎤⎥⎦dt

aij

0 tj+1 1

vi(j+1)minusvij

tj+1minustj

tj+1 gt 1

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)


Step 3 calculate the dynamic change speed trendω(aij)-e formula of the function ω(aij) is constructed asfollows -e dynamic trend value increases with thegrowth of aij When aij approaches positive infinity thevalue of the function is θ as aij approaches minus in-finity the value of the function is 0 if θ is assigned to 2then when aij 0 the function value is 1 and the range is(0 2) Before the inflection points of aij 0 andω(aij) 1 the growth speed of the trend function ofdynamic change rate is in increasing state after theinflection point the situation is reversed and the changerate of both sides is getting larger and larger whichrepresents the incentive effect of the change rate trend ofthe evaluated object on the comprehensive dynamicchange evaluation -e evaluation result is set as followswhen ω(aij) isin (1 2) it can positively stimulate thechanging speed state when ω(aij) 1 the change speedis relatively stable and no incentive or punishmentmeasures are taken When ω(aij) isin (0 1) negativecorrectionmeasures aremade for the changing rate state

ω aij1113872 1113873 θ

1 + eminus aij (11)

Step 4 calculate the dynamic comprehensive evaluationof value Fi

Referring to Newtonrsquos second law 1113936 F kma where thecoefficient k is a set term it does not affect the analysis of theevaluation results so it is set to 1 m is represented by thedynamic change rate state Si

v and the acceleration a isrepresented by the dynamic change rate trend ω(aij)Newtonrsquos second law formula is used to obtain the dynamiccomprehensive evaluation value Fi of the object i at the timepoint of j Finally the dynamic complete evaluation value Fi

of the object to be evaluated is obtained by summing up thecomprehensive evaluation values at each time point -eformula is as follows

fij Siv tj tj+11113872 1113873 middot ω aij1113872 1113873

Fi 1113944

jnminus1

j1fij

(12)

-e numerical value of the dynamic comprehensiveevaluation and its positive and negative performance indi-cate the development trend of the evaluated object intui-tively When Fi gt 0 the progressive development ofaerospace technology innovation resource allocation effi-ciency in the evaluated area is on the rise and the larger thevalue is the better the development situation isWhen Fi lt 0the development trend of the evaluated objects showed adownward trend When Fi 0 the subject was relativelystable throughout the empirical period

4 Indicator System and Data Selection

41 Indicator System -e input of technology innovationresource allocation in the aerospace industry includes RampD

personnel input and RampD expenditure input of which RampDpersonnel input is expressed by RampD personnel full-timeequivalent and RampD funding input is selected by RampD in-ternal expenditure Because the expenditure of RampD ex-penditure is a flow index which reflects the input of currentRampD expenditure it is unable to reflect the cumulative effectof RampD activities and also has a time-lag effect the RampDcapital stock with a lag of one period is carefully chosen asthe index of RampD expenditure [36] -e initial capital stockis estimated by dividing the internal support of RampD fundsin 2007 by 10 [35] and the capital stock of other years isKimiddott Kimiddottminus1(1 minus δ) + Cimiddott where Kimiddott and Kimiddot(tminus1) are actualRampD expenditures for the t and tminus 1 years in the region of iδ is the depreciation rate (δ 9600) and C is the net valueof the new capital stock Based on these indicators we cal-culate the actual value of RampD expenditure on the allocationof technology innovation resource in the aerospace industry

-e technology innovation output of the aerospace in-dustry includes knowledge benefit output and economicbenefit output -e patented invention is a direct output ofthe aerospace industry RampD activities which can objectivelyreflect the technology innovation capability and compre-hensive science and technological strength of the aerospaceindustry In this paper the number of patent applicationsand effective invention patents of the aerospace industry invarious regions [8] is selected to represent the knowledgebenefit output of its technology innovation efficiency -ispaper regards the sales revenue of new products of theaerospace industry in various regions as economic benefitoutput in that the ultimate value of scientific and technologyinnovation is its commercial value -is study processes theRAGA-PP model to reduce the number of patent applica-tions effective inventions and sales revenue of new productsof the aerospace industry in various regions and years

-e efficiency of resource allocation of technology in-novation in the aerospace industry is influenced by manyfactors such as its own industrial characteristics (enterprisescale and so on) regional environment (market concen-tration regional development level labor quality and so on)and relevant policies and guidelines of government de-partments -e control variables selected in this paper are asfollows the government funds raised by the RampD activitiesrepresent government support (GS) the ratio of the primarybusiness income to the number of enterprises is taken as themeasurement index of the average size of enterprises whichis named as enterprise scale (ES) market concentrationdegree (MC) is represented by the proportion of the numberof enterprises in each region in the total number of enter-prises in the area the logarithm of current GDP of eachregion is selected as the representative variable of regionaldevelopment level (RDL) EDS is the labor quality repre-sented by the logarithm of the number of college studentsper 100000 population

42 Data Selection Aerospace manufacturing is a typicalrepresentative of Chinarsquos high-tech industry which is alandmark industry in Chinarsquos construction and high-techlevel [35] Fortunately the aerospace industry sector has


authoritative public data Consequently based on the avi-ation and aerospace manufacturing industry from 2007 to2016 in China Science and Technology Statistical YearbookStatistical Yearbook of High-tech Industry and China Sta-tistical Yearbook this article selects 20 provincial regionsincluding Beijing Shaanxi and Liaoning as samples (data ofXizang Hainan and other rural local are seriously missingand do not include examples)

5 Empirical Analysis

51 Calculation of the Aerospace Industry Innovation OutputIndex -e science and technology innovation output dataof provincial regions from 2007 to 2016 in China aresubstituted into the projection pursuit model applying theaccelerated genetic algorithm RAGA based on real coding tooptimize the optimal projection direction and to obtain theoptimal projection value -e RAGA program is compiledby the software MATLAB2014a and the relevant parametersare set as follows population size n 400 crossoverprobability Pc 08 the mutation probability Pm 01 andthe acceleration times are set to 7 -e calculation results areshown in Table 2

52 Analysis of the Factors Affecting Resource Allocation Ef-ficiency of Technology Innovation in the Aerospace IndustryBased on the provincial-level panel data of China in2007ndash2016 the transcendental logarithmic stochastic frontiermodel is used to calculate both the allocation efficiency oftechnology innovation resource and the influence of variousfactors on the efficiency in Chinarsquos aerospace industry

As shown in Table 3 c 099 is significant at the 1 levelindicating that the SFA method can adequately estimate theoutput function -e log-likelihood function value is 70446and the maximum likelihood estimation works well theunilateral LR test value is 509555 which indicates that thetechnical inefficiency term has a significant impact on theallocation efficiency of science and technology resources invarious regions According to the above numerical analysisthe establishment of the model has a higher rationalityCombined with the evaluation results the output elasticityof resource input factors in the aerospace industry can beobtained We find that the output elasticity of RampD per-sonnel resource elements is rising while the output elasticityof RampD expenditure resource elements is declining whichindicates that the effectiveness of Chinarsquos aerospace industrytechnology innovation resource allocation is more depen-dent on the input of RampD personnel resource elements -isis exactly consistent with the current situation that humanresource input is relatively insufficient while financial re-source input is comparatively excessive and the per capitaRampD expenditure is relatively high while the high-levelprofessional labor resources are inadequate in the resourceallocation of technology innovation in Chinarsquos aerospaceindustry

It can be seen from the estimation of the efficiencyfunction that the regression coefficient supported by thegovernment is significantly positive showing that the

governmentrsquos financial support has not met the expectationsin improving the efficiency of resource allocation fortechnology innovation Because of severe informationasymmetry between the government and the production andmanagement of the aerospace industry there are subjectiveone-sidedness and time lag in the judgment and capitalinvestment of technology innovation and development Atthe same time merely intervening in innovation activities ofthe aerospace industry in the form of financial support willenable some enterprises profit from rent-seeking behaviorsand undermine fair competition in the market environmentcausing the crowding-out effect -is is consistent with thedeclining elasticity of the cost factor output shown in theproduction function

-e regression coefficient of the enterprise scale is sig-nificantly negative indicating that the expansion of theenterprise scale has a significant positive impact on theimprovement of the resource allocation efficiency of tech-nology innovation in the aerospace industry Because of therisk and difficulty of technology innovation activities in theaerospace industry small enterprises with weak financialstrength are in a disadvantaged position in terms of cost-sharing and financing channels while large enterprises withhigh capital intensity have remarkable economies of scalewith the strong antirisk ability and easy access to effectiveventure capital support

-e regression coefficient of market concentration issignificantly negative indicating that market concentrationhas a significant positive impact on the improvement of theallocation efficiency of technology innovation resource inthe aerospace industry As a reverse index the greater themarket concentration degree the lower the monopolisticdegree and the higher the efficiency -e aerospace industryinevitably faces the international competitive environmentand it is absolutely necessary for enterprises to joint researchand innovation in both vertical and horizontal directionsunder the strict innovation condition However the lack oftechnology innovation impetus in the monopolized marketand the emergence of innovation inertia are not conduciveto the improvement of the overall efficiency of technologyinnovation resource allocation

-e regression coefficient of regional development levelis significantly negative indicating that the improvement oflocal development level has a significant positive impact onthe growth of allocation efficiency of technology innovationresource in the aerospace industry -e regionrsquos preciousmaterial resources and solid technology innovation foun-dation promote the two-way flow of technology innovationresource and contribute to the enlargement of the linkageeffect of industrial structure optimization and economicdevelopment

-e regression coefficient of labor force quality is sig-nificantly positive which indicates that the optimization oflabor quality has not achieved the expected improvement inthe efficiency of the technology innovation resource allo-cation of the aerospace industry Consistent with the pre-vious analysis in the context of the new economicdevelopment the demand for talents is reflected not only inquantity but also in high quality and professionalism On a


national scale high-tech talents are mainly concentrated inthe eastern region but the aerospace industry is concen-trated in the western and northeastern regions which oftendoes not match the career plans of high-quality talentsresulting in a serious mismatch of human resources Hencethe optimization of labor quality has no beneficial effect on

the allocation of technology innovation resource in theaerospace industry

53 Static Comprehensive Evaluation of Resource AllocationEfficiency of Technology Innovation in the Aerospace IndustryAccording to the output of themodel firstly we calculate theaverage efficiency of resource allocation efficiency of theaerospace industry in each province (see Table 4) and thenwe carry out the simple arithmetic average calculation of thedata Finally we obtain the resource innovation resourceallocation of the aerospace industry in each region (seeTable 5) Accordingly we draw a radar map (see Figure 1)On the whole there are significant differences in the allo-cation efficiency of technology innovation resource amongdifferent regions which reflects the imbalance of regionaldevelopment Also on the space there is a pattern of ldquoloweast and high westrdquo which is opposite to the local economicdevelopment

From the national perspective the mean value of theevaluation of efficiency in Chinarsquos aerospace industrytechnology innovation resource allocation is 04571 which isbetween the average cost of the central and western regionsgenerally at the lower middle level and in urgent need ofimprovement -ere are 9 regions higher than the nationalaverage and 11 areas lower than the national average in-dicating that more than half of the areas are still at a low levelof allocation of technology innovation resource in theaerospace industry and there is an excellent room for im-provement Among them the average evaluation values oftechnology innovation resource allocation in the northeast

Table 2 Science and technology innovation output index

RegionYear

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016Beijing 002104 033554 036382 023165 039360 050790 039257 071672 089806 078669Tianjin 001901 002071 001842 002772 003264 000487 002993 048416 125213 076408Hebei 001953 002762 005012 004791 006278 005322 003497 009318 006020 006772Liaoning 067467 161304 123579 124324 146332 131262 126758 141745 148870 093925Heilongjiang 070421 066442 075872 059320 067589 070146 040862 070079 051927 059848Shanghai 011031 010875 039683 023632 039369 036550 023890 033659 040025 035112Jiangsu 000793 051274 035772 042501 089435 070191 067421 047556 038504 036386Zhejiang 002009 016311 020596 003679 004718 001021 002709 003498 002025 001979Anhui 005746 015133 036925 033788 017136 023344 003097 002112 001066 000543Jiangxi 018693 041326 038096 043630 042030 071651 058609 071118 051186 014581Shandong 000106 002060 001462 003374 003847 004504 008408 007887 008787 005106Henan 000697 052921 089083 099860 081981 054966 027651 047007 047729 058225Hubei 002501 031245 022087 021765 033180 017968 017823 040303 038504 034647Hunan 080578 041331 048816 043629 042030 032825 041800 048428 024799 025501Guangdong 100116 051274 036343 002757 003696 006530 005220 075404 010467 076418Chongqing 000102 000492 002251 002644 003883 002599 001869 003059 002182 000811Sichuan 013815 120089 089080 062134 041640 050456 058447 089865 072947 080944Guizhou 067189 103452 101573 075322 061933 071710 058609 099355 089806 076408Shaanxi 032877 139863 144296 124324 146291 138368 166540 163987 159181 164133Gansu 000131 002006 002578 000950 003964 004801 003686 003945 003435 001806Total 024011 047289 047566 039918 043898 042275 037957 053920 050624 046411East region 015002 021273 022137 013334 023746 021924 019174 037176 040106 039606Northeast region 068944 113873 099725 091822 106961 100704 083810 105912 100398 076887Central region 021643 036391 047001 048534 043271 040151 029796 041793 032657 026699West region 022823 073180 067956 053075 051542 053587 057830 072042 065510 064820

Table 3 -e estimation results of stochastic frontier function andefficiency function

Variable Estimatedcoefficient T-value

-e constant terms of efficiencyfunction 491200lowastlowastlowast 389404

ln lit(β1) 031119lowast 117270ln kit(β2) minus098319lowastlowastlowast 586939(ln lit)

2(β3) 002073 043071(ln kit)

2(β4) 006298lowastlowastlowast 373092ln lit ln kit(β5) minus004978 072683σ2 005548lowastlowastlowast 1187127c 099997lowastlowastlowast 3828302-e constant terms of efficiencyfunction 011597 054325

GS(σ1) 035956lowast 177509ES(σ2) minus001611lowastlowastlowast 550768MC(σ3) minus313288lowastlowastlowast 1314202RDL(σ4) minus00771lowastlowastlowast 275388EDS(σ5) 020847lowastlowastlowast 341828Log-likelihood function value 70446Unilateral LR test 509555lowastlowastlowast lowastlowast and lowast represent significance at the levels of 1 5 and 10respectively


region and the western region are 05247 and 04952 re-spectively which are at a high level also the average valuesof the central region and the eastern region are 04477 and04224 respectively which are at a low level and slightlyhigher in the central part

From the perspective of the provincial level there areconsiderable differences in the allocation of technologyinnovation resource in China -e provinces with the top 5evaluation rankings are Shaanxi Liaoning GuizhouHenan and Jiangsu and the bottom 5 provinces are HebeiHubei Chongqing Beijing and Shanghai Shaanxi prov-ince is a vital force in Chinarsquos national defense con-struction During the period of ldquo1st Five-Year Planrdquo themilitary construction projects in Shaanxi provinceaccounted for 239 of the total number of militaryprojects in the country Moreover in the course of theldquo2nd Five-Year Planrdquo period the state built 14 militaryprojects in Shaanxi province based on national defensesecurity and regional economic development For a longtime Shaanxi province has maintained a strong devel-opment momentum in the national defense science andtechnology industry aerospace industry and other do-mestic defense industries -e three areas with the highestefficiency in resource allocation for technology innovation

in Chinarsquos aerospace industry are located in the eco-nomically underdeveloped regions of the northwestnortheast and southwest while most of the provinces inthe economically developed regions are inefficient in re-source allocation at large such as the Beijing-Tianjin-Hebei region and the Yangtze River Delta and otherprovinces with the highest level of economy-e aerospaceindustry can neither effectively absorb the spillover effectof technology innovation nor timely acquire the advancetechnology in developed regions In addition under thecondition of the market economy it is difficult for thecentral and western regions to attract and retain high-levelscience and technological talents which is in line with thepreliminary analysis

From the perspective of the four regions as shown inFigure 2 in terms of the northeast region the allocationefficiency of technology innovation resource in the avi-ation and aerospace industry is in the leading position inmost years with the highest value of allocation efficiencyin 2008 being 0733 and the lowest amount of allocationefficiency in 2016 being 0359 -e allocation efficiency ofthe technology innovation resource in the aerospaceindustry in the eastern region showed a rising trend In2014 the allocation efficiency was the highest with a value

Table 4 Evaluation results of the allocation of interprovincial technology innovation resource in the aerospace industry

Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean RankBeijing 02614 03460 03354 02841 03420 03587 03022 03942 04629 04033 03490 19Tianjin 03866 03855 03854 03908 03888 03868 03951 06226 09054 06885 04936 7Hebei 03835 03810 03577 03584 04005 03709 03617 03747 03517 03492 03689 16Liaoning 04076 09891 06700 06328 07464 06259 05727 06409 06554 03690 06310 2Heilongjiang 04975 04770 05118 04160 04395 04367 03150 04048 03358 03491 04183 11Shanghai 03028 03027 03985 03480 03790 03327 02867 02812 02968 02727 03201 20Jiangsu 03665 06076 05147 05358 08346 06711 05974 04930 04332 04053 05459 5Zhejiang 03954 04602 04745 04006 04065 03952 03924 03914 03899 03891 04095 12Anhui 03948 04403 05575 04943 04077 04743 03857 03790 03690 03632 04266 10Jiangxi 03255 04111 03937 04071 03868 05077 04378 04698 03855 02437 03969 13Shandong 03702 03766 03734 03889 03713 03226 03774 04036 03950 03757 03755 14Henan 03236 05474 07581 08489 07121 05387 04014 04879 04723 05056 05596 4Hubei 02951 03976 03631 03621 04001 03365 03239 04037 03916 03686 03642 17Hunan 07627 05169 05625 05056 04787 04352 04565 04744 03647 03548 04912 8Guangdong 09423 05846 05065 03638 03447 03583 03570 06983 03607 06504 05167 6Chongqing 03719 03600 03679 03558 03626 03620 03626 03675 03649 03613 03636 18Sichuan 03095 08856 06417 04645 03644 03742 03847 05030 03949 04268 04749 9Guizhou 05793 08361 07961 05740 04972 05186 04341 06312 05597 04732 05899 3Shaanxi 02557 07401 07663 06202 07336 06422 08136 07598 06978 07116 06741 1Gansu 03828 03906 03915 03815 03877 03782 03670 03606 03518 03400 03732 15-e evaluation results in Table 4 are reserved for four decimal places the same as below

Table 5 Evaluation results of resource allocation of regional aerospace industry technology innovation

Year 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Mean RankTotal 04157 05218 05063 04567 04692 04413 04162 04771 04469 04201 04571East region 04261 04305 04183 03838 04334 03995 03837 04574 04494 04418 04224 4Northeast region 04526 07330 05909 05244 05929 05313 04439 05229 04956 03590 05247 1Central region 04203 04626 05270 05236 04771 04585 04011 04430 03966 03672 04477 3West region 03798 06425 05927 04792 04691 04550 04724 05244 04738 04626 04952 2-e eastern regions in Table 4 include Beijing Tianjin Hebei Shanghai Jiangsu Zhejiang Shandong and Guangdong the central areas include AnhuiJiangxi Henan Hubei andHunan the western areas include Chongqing Sichuan Guizhou Shaanxi and Gansu Northeast region includes Heilongjiang andLiaoning other regions were not included in the sample due to serious data missing


of 04574 and the allocation efficiency in 2013 was thelowest with a value of 03837 -e allocative ability oftechnology innovation resource in the aviation andaerospace industry in the central region showed a trend ofdecline with fluctuation -e allocative efficiency was thehighest with a value 0527 in 2009 and the lowest with avalue 03672 in 2016 In the western region the efficiencyvalue showed a fluctuating upward trend with the highestamount of 06425 in 2008 and the lowest value of 03798in 2007

Generally speaking the allocation of technology inno-vation resource in Chinarsquos aerospace industry presents astable and fluctuating trend with limited efficiency im-provement -e reasons are as follows firstly the ldquoboundaryconflictrdquo between resource sharing and safety managementof technology innovation subjects may be an eternal obstacleto the development of Chinarsquos space industry Secondly theaerospace industry itself fails to effectively integrate with themarket economy and absorb the spillover effect of high andnew technology in the process of development -irdly theregions with high-quality development of the aerospaceindustry are mostly the central and western regions withpoor economic growth unable to attract and retain high and

new technology talents resulting in a lack of developmentmomentum

54 Dynamic Comprehensive Evaluation of Resource Allo-cation Efficiency of Technology Innovation in the AerospaceIndustry Based on the static evaluation index the dynamicchange speed state index is analyzed from the accelerationangle of the resource allocation efficiency change (see Table 6)From the perspective of the change speed data of the aero-space industryrsquos innovation resource allocation the shift ininnovation speed in different regions is quite different Withthe time-lapse the development process of different areas istotally different in which the data vary considerably Byhorizontal comparison of the change speed of different re-gions in the same period it shows that the change speed ofBeijing and Tianjin is higher than the average of all regionsBesides the positive overall change speed indicates that theinnovation speed of the two regions is constantly increasingBy longitudinal comparison of the changing speed state in thedifferent areas of the same region it shows that the speedchanges in Henan Liaoning Guizhou Sichuan Tianjin andJiangsu fluctuate considerably while the speed states in other

001020304050607Heilongjiang

Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing

Figure 1 -e efficiency of resource allocation for technology innovation in Chinarsquos aerospace industry

0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016

NationwideEastern regionNortheast region

Central regionWestern region

Figure 2 Regional technology innovation resource allocation efficiency in the aerospace industry


regions are relatively stable From the perspective of the di-mensionality distribution of the four regions the mean valuecomparison shows that the velocity change state of thewestern region is at the highest value in each year while thatof the central region is at the negative value in recent years

-rough the measurement of the change speed trendvalue (as shown in Table 7) the change of the efficiency ofthe innovation resource allocation in the aerospace in-dustry is further explored and the ldquoincentiverdquo or ldquopun-ishmentrdquo mechanism is given for the evaluation of the stateof the speed change Most of the values fluctuated aroundthe value of 1 and the revision of the changing velocitystate in each region was continually changing BeijingShandong Gansu Liaoning and Heilongjiang are mainlyin the correction result of ldquopunishmentrdquo while Zhejiangand Shanghai are mostly in the correction result ofldquoincentiverdquo

According to the sum of the product of changingvelocity state and changing velocity trend the dynamiccomprehensive evaluation index is calculated In generalTable 8 shows that 70 of the data are negative and only afew dynamic comprehensive evaluation indicators arepositive From the year 2007 to 2016 the allocation effi-ciency of technology innovation resource in the aerospaceindustry in most regions of China showed a decreasingtrend among which only Beijing Tianjin ShanghaiJiangsu Anhui and Guizhou showed an upward trend inthe overall dynamic changes In the ranking of 20 regionsShaanxi province ranks first while Guangdong provinceranks last As a major province of Chinarsquos aerospace in-dustry Shaanxi province has a large number of profes-sionals and sophisticated equipment Sufficient exclusiveindustrial resources sophisticated equipment high-endtalent flow and other fundamental factors combined withthe social policy support have prompted Xian to take theaerospace industry as the leading industry and lead the

innovation and development of the domestic aerospaceindustry Relatively Guangdongrsquos aerospace industrydeveloped late with a weak foundation In the past decadeChinarsquos ldquo12th Five-Year Planrdquo and ldquo13th Five-Year Planrdquohave all promoted the relevant aerospace industry inGuangdong province However because of the small scaleof the aerospace industry and the insignificant industrialagglomeration effect in Guangdong Province the overalldevelopment situation is still relatively weak

Dividing the provinces and cities into four regions it canbe observed intuitively that the polarization is more obviousin the regions except northeast China where the dynamiccomprehensive evaluation index generally lags behind Inthe eastern region for example Beijing Tianjin and Jiangsurank higher while other provinces and cities lag behindespecially the Guangdong province

By combining static evaluation with dynamic evalua-tion this paper makes a more comprehensive analysis ofthe resource allocation efficiency of technology innovationin the aerospace industry in 20 regions By comparing thestatic ranking and dynamic ranking of the same region asshown in Figure 3 and Table 9 two extreme phenomenacan be found the static ranking of Beijing and Shanghai isbehind while their dynamic rankings are at the front thestatic ranking of Liaoning Guangdong and Guizhou is atthe front while the dynamic ranking is behind -e dif-ference between the two extreme phenomena lies in thenecessary status quo and development trend of efficiencyunder the allocation of technology innovation resourceWith relatively developed economic development Beijingand Shanghai continue to attract the introduction ofdomestic and foreign talents and technologies rapidlyimproving their core capabilities in the process of devel-opment and gradually professionalizing the allocation ofspace resources In these regions the initial weak essentialcapacity is continually rising and the high-end RampD

Table 6 Change speed state of the aerospace industryrsquos technology innovation resource allocation efficiency

Region 2008ndash2009 2009ndash2010 2010ndash2011 2011ndash2012 2012ndash2013 2013ndash2014 2014ndash2015 2015ndash2016Beijing 00370 minus00309 00033 00373 minus00199 00177 00803 00046Tianjin minus00006 00026 00017 minus00020 00032 01179 02551 00329Hebei minus00129 minus00113 00214 00063 minus00194 00019 minus00050 minus00127Liaoning 01312 minus01782 00382 minus00034 minus00868 00075 00413 minus01360Heilongjiang 00071 minus00305 minus00361 00104 minus00622 minus00160 00104 minus00279Shanghai 00479 00227 minus00098 minus00077 minus00461 minus00258 00051 minus00042Jiangsu 00741 minus00359 01600 00676 minus01186 minus00890 minus00821 minus00439Zhejiang 00395 minus00298 minus00340 minus00027 minus00070 minus00019 minus00013 minus00011Anhui 00814 00270 minus00749 minus00100 minus00110 minus00477 minus00083 minus00079Jiangxi 00341 minus00020 minus00034 00503 00255 minus00190 minus00261 minus01130Shandong 00016 00062 minus00010 minus00332 00030 00405 00088 minus00140Henan 02172 01508 minus00230 minus01551 minus01553 minus00254 00354 00089Hubei 00340 minus00178 00185 minus00128 minus00381 00336 00339 minus00176Hunan minus01001 minus00056 minus00419 minus00352 minus00111 00196 minus00459 minus00598Guangdong minus02179 minus01104 minus00809 minus00028 00061 01700 00019 minus00239Chongqing minus00020 minus00021 minus00027 00031 00000 00028 00012 minus00031Sichuan 01661 minus02106 minus01387 minus00452 00102 00644 00051 minus00381Guizhou 01084 minus01310 minus01495 minus00277 minus00315 00563 00628 minus00790Shaanxi 02553 minus00600 minus00163 00110 00400 00588 minus00579 minus00241Gansu 00043 minus00045 minus00019 minus00016 minus00104 minus00088 minus00076 minus00103


capacity is continuously concentrated -e layout of theaerospace industry has constantly been adjusted so that theaerospace industry technology innovation resource allo-cation efficiency has a relatively rapid development po-tential In contrast Liaoning province as the cradle ofChinarsquos aerospace industry is the most concentrated areaof the aerospace industry in Chinarsquos planned economywith solid strength and development opportunities

Nevertheless the lack of integration of industry withsystematic professional knowledge aging makes it lessoptimistic for Liaoning province to advance a profounddevelopment of the aerospace industry

In a nutshell the allocation efficiency of technologyinnovation resource in Chinarsquos aerospace industry is un-balanced and in most regions the allocation efficiency oftechnology innovation resource is in a dynamic declining

Table 7 -e trend of changing speed of resource allocation efficiency in the aerospace industry

Year 2008ndash2009 2009ndash2010 2010ndash2011 2011ndash2012 2012ndash2013 2013ndash2014 2014ndash2015 2015ndash2016Beijing 09525 09796 10546 09794 09634 10741 09883 09360Tianjin 10005 10027 09964 09999 10052 11091 10277 07552Hebei 09897 10120 10208 09641 10102 10111 09820 10102Liaoning 05779 11400 10753 08835 10336 10606 09731 08507Heilongjiang 10277 09348 10596 09869 09406 11053 09208 10411Shanghai 10479 09270 10407 09614 10001 10203 10106 09801Jiangsu 08345 10570 11379 07729 10449 09846 10223 10160Zhejiang 09747 09559 10399 09914 10043 10009 09997 10004Anhui 10359 09100 09883 10764 09225 10410 09983 10021Jiangxi 09486 10154 09832 10704 09049 10509 09420 09712Shandong 09952 10094 09834 09845 10517 09858 09825 09947Henan 09934 09402 08866 09817 10180 11114 09490 10245Hubei 09316 10167 10196 09492 10255 10461 09541 09945Hunan 11447 09488 10150 09917 10324 09983 09363 10499Guangdong 11390 09677 10617 10164 09925 11697 06730 13037Chongqing 10099 09900 10094 09964 10006 10022 09962 09995Sichuan 06115 10334 10385 10549 10003 10538 08872 10699Guizhou 08527 09092 10725 10491 09471 11399 08664 09926Shaanxi 07748 09141 11290 08979 11307 08879 09959 10378Gansu 09966 09946 10081 09922 09991 10024 09988 09985

Table 8 Dynamic comprehensive evaluation of resource allocation efficiency in the aerospace industry

Evaluation value RankEast regionBeijing 00316 5Tianjin 00641 3Hebei minus00288 11Shanghai 00032 7Jiangsu 00971 2Zhejiang minus00362 14Shandong minus00362 13Guangdong minus04685 20

Central regionAnhui 00064 6Jiangxi minus00054 8Henan 00349 4Hubei minus00350 12Hunan minus02725 16

West regionChongqing minus00068 9Sichuan minus03380 19Guizhou minus03209 18Shaanxi 01515 1Gansu minus00243 10

Northeast regionLiaoning minus02944 17Heilongjiang minus01370 15


trend -e main reason lies in the discontinuity of the in-dustrial chain in some regions the unreasonable distributionof the industrial structure and the urgent demand of pro-fessional personnel and technology

6 Conclusions

-is paper selects the panel data of 20 provinces from 2007to 2016 constructs a static and dynamic evaluation model tomeasure the efficiency of resource allocation of Chinarsquosaerospace industry technology innovation and then effec-tively identifies and empirically analyzes its critical influ-encing factors -e conclusions and policy implications aresummarized as follows

-e overall efficiency of the technical innovation re-source allocation of the aerospace industry is at a medium-to-lower level and there are problems such as resourceredundancy and waste as well as resource mismatch andimbalance -e efficiency of resource allocation of

technology innovation in the aerospace industry is obviouslydifferent among regions and has little coordination with thelevel of regional economic development -erefore it isunable to effectively absorb the technology spillover effectfrom the high-tech industry and the new economic form Inthe past ten years Chinarsquos aerospace industry technologyinnovation resource allocation efficiency has been improvedto some extent but the improvement effect is not visibleAccording to the above analysis we find that the develop-ment of Chinarsquos aerospace industry is still in its infancy andthe problem of technical innovation resource allocation isprominent At the same time the input-output ratio has notyet reached the ideal level which needs to be improvedprogressively

Government support and labor quality have adverseeffects on the allocation efficiency of technology innovationresource in the aerospace industry while enterprise scalemarket concentration and regional development levels allhave positive impact on the allocation efficiency of

ndash06

ndash04

ndash02

0

02

04

06

08

Static evaluation valueDynamic ealuation value

Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su

Figure 3 Static and dynamic evaluation of resource allocation efficiency in the aerospace industry

Table 9 Static and dynamic evaluation of resource allocation efficiency in the aerospace industry

Region Static evaluation value Dynamic evaluation value Static rank Dynamic rankBeijing 03490 00316 19 5Tianjin 04936 00641 7 3Hebei 03689 minus00288 16 11Liaoning 06310 minus02944 2 17Heilongjiang 04183 minus01370 11 15Shanghai 03201 00032 20 7Jiangsu 05459 00971 5 2Zhejiang 04095 minus00362 12 14Anhui 04266 00064 10 6Jiangxi 03969 minus00054 13 8Shandong 03755 minus00362 14 13Henan 05596 00349 4 4Hubei 03642 minus00350 17 12Hunan 04912 minus02725 8 16Guangdong 05167 minus04685 6 20Chongqing 03636 minus00068 18 9Sichuan 04749 minus03380 9 19Guizhou 05899 minus03209 3 18Shaanxi 06741 01515 1 1Gansu 03732 minus00243 15 10


technology innovation resource in the aerospace industryHence in terms of funds and policies the government oughtto give full play to the primary role of the market and helpthe aerospace industry to realize its ldquohematopoieticrdquo func-tion in a more effective way in combination with the de-velopment of the aerospace industry Secondly it isnecessary to encourage full cooperation within the aerospaceindustry and strengthen synergy with institutions of higherlearning and scientific research institutions to maximize theflow of technology innovation elements In the end based onthe overall situation of the country we should promote thebalanced development of the aviation and aerospace in-dustry in the east central and western regions andstrengthen the aviation and aerospace industry in the westand northeast regions to impart experience to the east andcentral areas At the same time it is indispensable to guidethe outflow of high-quality talents from the eastern regionand lead the development of the aerospace industry in otherregions

Data Availability

-e development data of Chinarsquos aerospace industry used tosupport the results of this study have been published in theChina high-tech statistics yearbook 2017 published by theNational Bureau of Statistics of China in 2017 -e data canbe downloaded from the national bureau of statisticswebsite

Conflicts of Interest

-e authors declare no conflicts of interest regarding thepublication of this paper

Acknowledgments

-is research was funded by the 2019 General ResearchTopic Projects of Partyrsquos Political Construction ResearchCenter of the Ministry of Industry and Information Tech-nology (grant no 19GZY313)

References

[1] D C Fan and M Y Du ldquoResearch on technology innovationresource allocation and its influencing factors in high-endequipment manufacturing industriesmdashbased on the empiricalanalysis of the two-stage stoned-tobit modelrdquo Chinese Journalof Management Science vol 26 no 1 pp 13ndash24 2018

[2] A Kontolaimou I Giotopoulos and A Tsakanikas ldquoA ty-pology of European countries based on innovation efficiencyand technology gaps the role of early-stage entrepreneur-shiprdquo Economic Modelling vol 52 pp 477ndash484 2016

[3] H Xu L N Xing and L Huang ldquoRegional science andtechnology resource allocation optimization based on im-proved genetic algorithmrdquo KSII Transactions on Internet andInformation Systems vol 11 no 4 pp 1972ndash1986 2017

[4] S N Afriat ldquoEfficiency estimation of production functionsrdquoInternational Economic Review vol 13 no 3 pp 568ndash5981972

[5] S Zemtsov and M Kotsemir ldquoAn assessment of regionalinnovation system efficiency in Russia the application of the

DEA approachrdquo Scientometrics vol 120 no 2 pp 375ndash4042019

[6] K H Chen and X L Fu ldquoEvaluation of multi-period regionalRampD efficiency an application dynamic DEA to Chinarsquosregional RampD systemsrdquo Omega-International Journal ofManagement Science vol 74 pp 103ndash114 2017

[7] W Q Liang T R Qin and P Liang ldquoClassified measurementand comparison of regional green low-carbon innovationefficiency in Chinardquo Journal of Arid Land Resources andEnvironment vol 11 pp 9ndash16 2019

[8] T Sueyoshi and M Goto ldquoResource utilization for sustain-ability enhancement in Japanese industriesrdquo Applied Energyvol 228 pp 2308ndash2320 2018

[9] C I P Martinez and W H A Pina ldquoRegional analysis acrossColombian departments a non-parametric study of energyuserdquo Journal of Cleaner Production vol 115 pp 130ndash1382016

[10] O Guliyev A Liu G Endelani Mwalupaso and J Niemildquo-e determinants of technical efficiency of hazelnut pro-duction in Azerbaijan an analysis of the role of NGOsrdquoSustainability vol 11 no 16 p 4332 2019

[11] M Y Zhang and D Zhang ldquoAnalysis of innovation efficiencyof above-scale industrial enterprises in districts of prefecture-level in beijing-tianjin-hebei regionrdquo Economic Surveyvol 36 no 1 pp 32ndash39 2019

[12] Y Wang X S Gong and J J Li ldquoAnalysis of xinjiangequipment manufacturing industries technology innovationefficiency and influencing factors based on SFA modelrdquoScience and Technology Management Research vol 12pp 146ndash151 2017

[13] M Yi J C Peng and C Wu ldquoInnovation efficiency of high-tech industries based on stochastic frontier method in ChinardquoScience Research Management vol 11 pp 22ndash31 2019

[14] S Y Yin and L T Chen ldquoTechnology innovation efficiency ofChinese pharmaceutical firms based on two-phase SFAstudyrdquo Soft Science vol 30 no 5 pp 54ndash58 2016

[15] Z L Yu ldquoStudy on innovation efficiency and influencingfactors of universities based on PP-SFArdquo Science-Technologyand Management vol 108 no 2 pp 18ndash22+109 2018

[16] B Z Li and H M Dong ldquoldquoAn empirical study of the scientificresearch institutesrsquovalue creation efficiency based on PP-SFAby taking the 12 CAS branchesrdquo Science Research Manage-ment vol 9 pp 60ndash68 2017

[17] Y Y Ding T C Li and R Wang ldquoTechnical innovationefficiency and its influence factors of civil-military integrationbased on DEA-Malmquist two-step model in electronic in-formation manufacturing industryrdquo Systems Engineeringvol 6 pp 1ndash17 2019

[18] M Dilling-Hansen E S Madsen and V Smith ldquoEfficiencyRampD and ownership - some empirical evidencerdquo Interna-tional Journal of Production Economics vol 83 no 1pp 85ndash94 2003

[19] X H Sun and Y Wang ldquoOwnership and firm innovationefficiencyrdquo Chinese Journal of Management vol 10 no 7pp 1041ndash1047 2013

[20] Y Z Yu ldquoInnovation cluster government support and thetechnology innovation efficiency based on spatial econo-metrics of panel data with provincial datardquo Economic Reviewvol 2 pp 93ndash101 2011

[21] D C Fang F Zhang and M J Rui ldquoldquoEmpirical Study on thehigh-tech industry innovation efficiency and its influencingfactorsmdashbased on the panel data analysis of stochastic frontiermodelrdquo Science and Technology Management Researchvol 36 no 7 pp 66ndash70 2016


[22] W H Li and F Liu ldquo-e comprehensive measurement ofinnovation efficiency of Chinarsquos High-tech industry based onenvironmental factorsrdquo Science amp Technology Progress andPolicy vol 36 no 4 pp 75ndash81 2019

[23] T Li L Liang and D Han ldquoResearch on the efficiency ofgreen technology innovation in Chinarsquos provincial high-endmanufacturing industry based on the Raga-PP-SFA modelrdquoMathematical Problems in Engineering vol 2018 Article ID9463707 13 pages 2018

[24] L Ma and Y P Zheng ldquoResearch on the effect of RampD in-vestment intensity on innovation efficiency and its mecha-nism in high-tech industriesrdquo Science amp Technology forDevelopment vol 15 no 07 pp 660ndash668 2019

[25] Y W Zhu and K N Xu ldquo-e empirical research on RampDefficiency of Chinese high-tech industriesrdquo China IndustrialEconomy vol 11 pp 38ndash45 2006

[26] A G Campolina P C De Soarez F V Do Amaral andJ M Abe ldquoMulti-criteria decision analysis for health tech-nology resource allocation and assessment so far and sonearrdquo Cadernos de Saude Publica vol 33 no 10 Article IDe00045517 2017

[27] C Chaminade and M Plechero ldquoDo regions make a differ-ence Regional innovation systems and global innovationnetworks in the ICT industryrdquo European Planning Studiesvol 23 no 2 pp 215ndash237 2015

[28] D Pelinan ldquo-e evolution of firm growth dynamics in the USpharmaceutical industryrdquo Regional Studies vol 8 pp 1053ndash1066 2010

[29] E Endo ldquoInfluence of resource allocation in PV technologydevelopment of Japan on the world and domestic PVmarketrdquoElectrical Engineering in Japan vol 198 no 1 pp 12ndash24 2017

[30] F Fan J Q Zhang G Q Yang and Y Y Sun ldquoResearch onspatial spillover effect of regional science and technologyresource allocation efficiency under the environmental con-straintsrdquo China Soft Science vol 4 pp 110ndash123 2016

[31] J X Li and X N Wen ldquoResearch on the relationship betweenthe allocation efficiency and influencing factors of Chinarsquosscience and technology financerdquo China Soft Science vol 1pp 164ndash174 2019

[32] Q Q Chen J B Zhang L L Cheng and Z L Li ldquoRegionaldifference analysis and itrsquos driving factors decomposition onthe allocation ability of agricultural science and technologyrdquoScience Research Management vol 37 no 3 pp 110ndash1232016

[33] H X Huang and Z H Zhang ldquoResearch on science andtechnology resource allocation efficiency in Chinese emergingstrategic industries based on DEA modelrdquo China Soft Sciencevol 1 pp 150ndash159 2015

[34] P Peykani E Mohammadi A Emrouznejad M S Pishvaeeand M Rostamy-Malkhalifeh ldquoFuzzy data envelopmentanalysis an adjustable approachrdquo Expert Systems with Ap-plications vol 136 pp 439ndash452 2019

[35] J Liu K LU and S Cheng ldquoInternational RampD spillovers andinnovation efficiencyrdquo Sustainability vol 10 no 11 p 39742018

[36] W W Liu C S Shi and J Li ldquoEvaluation matrix with thespeed feature based on double inspiriting control linesrdquoJournal of Systems Engineering and Electronics vol 24 no 6pp 962ndash970 2016


As efficiency is the core index for evaluating industrialtechnology innovation capability the study on the efficiencyof resource allocation of technology innovation is conduciveto comprehensively investigating the process of resourceinput and innovation output in technology innovation ac-tivities and then finding out the existing institutional con-straints and institutional barriers [2 3] However there arefew papers to evaluate the resource allocation efficiency oftechnology innovation with the aerospace industry as theresearch object -erefore it is necessary to explore thetechnology innovation resource allocation situation of Chinarsquosaerospace industry and to reveal the key factors influencingthe allocation efficiency and mechanism which are of the-oretical significance to enrich the evaluation of innovationefficiency and of practical relevance to both coordinate theoptimal allocation of the aerospace industry resources andrealize the development pattern of aerospace industry power

2 Literature Review

At present there is no authoritative definition of the con-notation of resource allocation efficiency in academic circles[1] Instead relevant research results mainly focus on theefficiency of technology innovation and the allocation ofscientific and technical resources

-e concept of technology innovation efficiency was firstproposed by Afriat referring to the input-output variable ofeffective technology in RampD innovation activities [4] -emeasurement of technology innovation efficiency includesmostly two methods the parametric method and thenonparametric method One of the nonparametric tech-niques which are commonly used is the data envelopmentanalysis (DEA) [5] Scholars have carried out a lot of studieson regional innovation efficiency [5ndash7] industrial tech-nology innovation efficiency [8 9] and other issues based ondifferent research perspectives building different indicatorsystems and selecting various sample data In addition oneof the parametric methods which are commonly used is thestochastic frontier analysis (SFA) [10] At present the SFAmethod has been widely applied to evaluate innovationefficiency at many levels such as regional level [11] industriallevel [12ndash14] and research structure [15 16] Besidesscholars have also carried out some research on the influ-encing factors of technology innovation efficiency mainlyinvolving some traditional industrial organization factorssuch as ownership structure [17ndash19] enterprise scale[20ndash23] and market competition degree [24 25]

Regarding the allocation of technological resources theresearch priorities by domestic and foreign scholars aredifferent International scholars paid more attention to theoptimization effect of the implementation of technologypolicies and plans on the allocation of national technologyresources from the national level Campolina et al [26]Chaminade and Plechero [27] and other scholars proposedthe key factors to optimize the allocation path of science andtechnology resources by comparing the allocation methodsand expenditure on science and technology of variouscountries Meantime because enterprises play an indis-pensable role in the market economy of developed countries

and basically represent the science and technology inno-vation ability of the whole society many scholars started tostudy the allocation of science and technology resourcesfrom the perspective of enterprises Pelinand [28] Endo[29] and other scholars verified the mechanism of theimpact of government RampD subsidies corporate strategyand cost advantages on RampD resource allocation Howeverdomestic scholars mostly paid more attention on the scienceand technology resource allocation system mechanism andthe ability from the regional level which is based on the factsof Chinarsquos unbalanced local economic development Fanet al [30] Li andWen [31] and Chen et al [32] evaluated theregional science and technology resource allocation effi-ciency in different periods as well as analyzed the mainfactors affecting the improvement of the local science andtechnology resource allocation efficiency In addition Chenet al [32] and Huang and Zhang [33] respectively analyzedthe allocation efficiency of science and technological re-sources in Chinarsquos strategic emerging industries agriculturalcolleges and universities A brief presentation of previousstudies and their findings is presented in Table 1

In summary scholars have conducted in-depth researchon technology innovation efficiency and technology re-source allocation from the enterprise regional and nationallevels However there are few relevant analyses on the ef-ficiency of resource allocation of technology innovation andtechnology innovation in the aerospace industry in theliterature on the factors influencing the effectiveness oftechnology innovation scholars hold different views on thesame influencing variable -e influencing mechanism ofresource allocation efficiency in the aerospace industry ismore like a ldquoblack boxrdquo Accordingly this study compre-hensively considers both the technology innovation and theefficiency of science and technology resource allocationBased on the sample data of 20 provinces and cities thispaper uses the RAGA-PP-SFA model and the dynamiccomprehensive evaluation model to evaluate the resourceallocation efficiency of technology innovation in the aero-space industry from the regional level as well as to assess theextensive dynamic development trend during the wholeevaluation period so as to enrich the relevant studies

3 Research Model

31 Static EvaluationModel DEA and SFA are two effectivemethods of efficiency evaluation which are constantly im-proved in the application process For example PDEA solvesthe problem of inaccurate and ambiguous data in the tra-ditional DEA model [34] However the DEA method sets adeterministic boundary in the efficiency measurement sothat measurement errors are not allowed that is all devi-ations from the production boundary or cost boundary areattributed to low efficiency which is not in line with theactual situation [23] Compared with the DEA methodlacking risk considerations and statistical characteristics theSFA has the following two advantages in empirical analysisOn the one hand the SFA divides the actual output intothree parts production function random perturbation andtechnical inefficiency and divides the error term into two






(RIS)2009ndash2012


















Two MalmquistDEA










Tianjin-Hebei

Wang et al[12] SFA


2000ndash2014





2000ndash2015



different














significantly



2001ndash2014








z(i)t 1113944

p

j1a(j)ty(i j)t (1)


Q(a) SzDz (2)




st 11139363

j1a2(j)t 1

⎧⎪⎪⎨

⎪⎪⎩(3)





Table 1 Continued






improvement

Chen et al[32]



1999ndash2011


generally declined








c σ2uσ2u

+ σ2v (6)



+ β4 ln kit( 11138572


(7)







R rij1113960 1113961m(n+1)


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦


tj+1 minus tj


0 relatively stable


⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

V vij1113960 1113961mn


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)



Siv tj tj+11113872 1113873 1113946

tj+1

tj


tj+1minustj

⎡⎢⎣ ⎤⎥⎦dt

aij

0 tj+1 1

vi(j+1)minusvij

tj+1minustj

tj+1 gt 1

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)



ω aij1113872 1113873 θ

1 + eminus aij (11)






Fi 1113944

jnminus1

j1fij

(12)


























RegionYear






2(β3) 002073 043071(ln kit)


















Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing


0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


























6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References












































(RIS)2009ndash2012


















Two MalmquistDEA










Tianjin-Hebei

Wang et al[12] SFA


2000ndash2014





2000ndash2015



different














significantly



2001ndash2014








z(i)t 1113944

p

j1a(j)ty(i j)t (1)


Q(a) SzDz (2)




st 11139363

j1a2(j)t 1

⎧⎪⎪⎨

⎪⎪⎩(3)





Table 1 Continued






improvement

Chen et al[32]



1999ndash2011


generally declined








c σ2uσ2u

+ σ2v (6)



+ β4 ln kit( 11138572


(7)







R rij1113960 1113961m(n+1)


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦


tj+1 minus tj


0 relatively stable


⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

V vij1113960 1113961mn


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)



Siv tj tj+11113872 1113873 1113946

tj+1

tj


tj+1minustj

⎡⎢⎣ ⎤⎥⎦dt

aij

0 tj+1 1

vi(j+1)minusvij

tj+1minustj

tj+1 gt 1

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)



ω aij1113872 1113873 θ

1 + eminus aij (11)






Fi 1113944

jnminus1

j1fij

(12)


























RegionYear






2(β3) 002073 043071(ln kit)


















Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing


0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


























6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References











































z(i)t 1113944

p

j1a(j)ty(i j)t (1)


Q(a) SzDz (2)




st 11139363

j1a2(j)t 1

⎧⎪⎪⎨

⎪⎪⎩(3)





Table 1 Continued






improvement

Chen et al[32]



1999ndash2011


generally declined








c σ2uσ2u

+ σ2v (6)



+ β4 ln kit( 11138572


(7)







R rij1113960 1113961m(n+1)


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦


tj+1 minus tj


0 relatively stable


⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

V vij1113960 1113961mn


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)



Siv tj tj+11113872 1113873 1113946

tj+1

tj


tj+1minustj

⎡⎢⎣ ⎤⎥⎦dt

aij

0 tj+1 1

vi(j+1)minusvij

tj+1minustj

tj+1 gt 1

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)



ω aij1113872 1113873 θ

1 + eminus aij (11)






Fi 1113944

jnminus1

j1fij

(12)


























RegionYear






2(β3) 002073 043071(ln kit)


















Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing


0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


























6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References










































c σ2uσ2u

+ σ2v (6)



+ β4 ln kit( 11138572


(7)







R rij1113960 1113961m(n+1)


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦


tj+1 minus tj


0 relatively stable


⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

V vij1113960 1113961mn


⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(9)



Siv tj tj+11113872 1113873 1113946

tj+1

tj


tj+1minustj

⎡⎢⎣ ⎤⎥⎦dt

aij

0 tj+1 1

vi(j+1)minusvij

tj+1minustj

tj+1 gt 1

⎧⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎩

(10)



ω aij1113872 1113873 θ

1 + eminus aij (11)






Fi 1113944

jnminus1

j1fij

(12)


























RegionYear






2(β3) 002073 043071(ln kit)


















Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing


0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


























6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References









































ω aij1113872 1113873 θ

1 + eminus aij (11)






Fi 1113944

jnminus1

j1fij

(12)


























RegionYear






2(β3) 002073 043071(ln kit)


















Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing


0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


























6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References

























































RegionYear






2(β3) 002073 043071(ln kit)


















Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing


0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


























6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References






















































Liaoning

Gansu

Shaanxi

Guizhou

Sichuan

Chongqing

Hunan

HenanJiangxi

Anhui

Guangdong

Shandong

Zhejiang

Jiangsu

Shanghai

Hebei

Hubei

TianjingBeijing


0

02

04

06

08

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016


























6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References





























































6 Conclusions





ndash06

ndash04

ndash02

0

02

04

06

08


Beiji

ng

Tian

jin

Heb

ei

Liao

ning

Hei

long

jiang

Shan

ghai

Jiang

su

Zhej

iang

Anh

ui

Jiang

xi

Shan

dong

Hen

an

Hub

ei

Hun

an

Gua

ngdo

ng

Chon

gqin

g

Sich

uan

Gui

zhou

Shaa

nxi

Gan

su






Data Availability




Acknowledgments


References









































Data Availability




Acknowledgments


References








































researchondynamiccomprehensiveevaluationofresource...

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