浙江省创新空间分异特征分析与驱动机制研究
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摘要
基于浙江省90个县区2005—2015年面板数据,采用熵权综合指数法分析县域层级的创新空间分布特征,识别创新的热点空间及惰性空间;进一步利用数据包络分析方法测评创新空间的效率水平,并运用二元选择模型探究有利于创新活力提升的适配性创新生态环境,寻求浙江省创新驱动发展机制与提升路径。实证结果表明:浙江省创新热点空间日趋集中在浙东北地区,各县区的创新效率呈上升趋势但存在地区分异性;政府对创新效率的推动作用显著大于人才储备和产业高度化的推动作用,同时创新氛围、企业家创新精神对各县区的创新效率也具有正的影响,而金融环境质量、风险承担能力与各县区的区域创新效率则存在负相关关系。
Based on the panel data of 90 counties in Zhejiang province from 2005 to 2015, this paper analyzes the spatial distribution characteristics of innovation at county level in Zhejiang province by entropy weight synthesis index method, and identifies the hot spot space and inert space of innovation; the efficiency level of innovation space is further evaluated by means of data envelopment analysis, and the adaptive innovation ecological environment which is conducive to the promotion of innovation vitality is explored by using the binary selection model, and the innovationdriven development mechanism and promotion path in Zhejiang are sought. The empirical results show that, the innovation hot spots in Zhejiang are increasingly concentrated in the northeast, the innovation efficiency of each county shows an upward trend, but there is regional differentiation. The role of government in promoting innovation efficiency is significantly greater than that of talent reserve and industrial upgrading, at the same time, innovation atmosphere, entrepreneurial innovation spirit also has a positive impact on innovation efficiency in all counties and districts, and the quality of financial environment, there is a negative correlation between the risk-bearing capacity and the regional innovation efficiency of each county.
Based on the panel data of 90 counties in Zhejiang province from 2005 to 2015, this paper analyzes the spatial distribution characteristics of innovation at county level in Zhejiang province by entropy weight synthesis index method, and identifies the hot spot space and inert space of innovation; the efficiency level of innovation space is further evaluated by means of data envelopment analysis, and the adaptive innovation ecological environment which is conducive to the promotion of innovation vitality is explored by using the binary selection model, and the innovationdriven development mechanism and promotion path in Zhejiang are sought. The empirical results show that, the innovation hot spots in Zhejiang are increasingly concentrated in the northeast, the innovation efficiency of each county shows an upward trend, but there is regional differentiation. The role of government in promoting innovation efficiency is significantly greater than that of talent reserve and industrial upgrading, at the same time, innovation atmosphere, entrepreneurial innovation spirit also has a positive impact on innovation efficiency in all counties and districts, and the quality of financial environment, there is a negative correlation between the risk-bearing capacity and the regional innovation efficiency of each county.
引文
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[2]HALL P,MARSHALL D,MARTIN R.Merging and splitting eigenspace models[J].IEEE Trans on Pattern Analysis & Machine Intelligence, 2000,22(9):1042-1049.
[3]SCHUMPETER J A,NICHOL A J.Robinson's economics of imperfect competition[J].Journal of Political Economy,1934,42(2):249-259.
[4]LIM U.The spatial distribution of innovation activity in U.S. metropolitan areas:evidence from patent data[J].Journal of Regional Analysis and Policy,2003,33(2):97-126.
[5]MORENO R,PACI R,USAI S.Geographical and sectoral clusters of innovation in Europe[J].Annals of Regional Science, 2005,39(4):715-739.
[6]李国平,王春杨.我国省域创新产出的空间特征和时空演化:基于探索性空间数据分析的实证[J].地理研究,2012,31(1):95-106.
[7]何键芳,张虹鸥,叶玉瑶,等.广东省区域创新产出的空间相关性研究[J].经济地理,2013,33(2):117-121,140.
[8]丛海彬,邹德玲,蒋天颖.浙江省区域创新平台空间分布特征及其影响因素[J].经济地理,2015,35(1):112-118.
[9]TODLTING F, KAUFMANNA. Innovation systems in regions of europe: a comparative perspective[J]. European Planning Studies, 1999,7(6):699-717.
[10]LEE S S, KIM Y. The impact of efficiency parameters on firms' innovative activities: evidence from Korean firm-level data[J].Innovation, 2010,12(3):283-297.
[11]NISHIMURA J, OKAMURO H. R&D productivity and the organization of cluster policy: an empirical evaluation of the industrial cluster project in Japan[J].The Journal of Technology Transfer, 2011,36(2):117-144.
[12]桂黄宝.我国高技术产业创新效率及其影响因素空间计量分析[J].经济地理,2014,34(6):100-107.
[13]樊华,周德群.中国省域科技创新效率演化及其影响因素研究[J].科研管理,2012,33(1):10-18,26.
[14]王锐淇,彭良涛,蒋宁.基于SFA与Malmquist方法的区域技术创新效率测度与影响因素分析[J].科学学与科学技术管理, 2010,31(9):121-128.
[15]顾伟男,申玉铭.我国中心城市科技创新能力的演变及提升路径[J].经济地理,2018,38(2):113-122.
[16]吴延兵.不同所有制企业技术创新能力考察[J].产业经济研究, 2014(2):53-64.
[17]王春杨,张超.中国地级区域创新产出的时空模式研究:基于ESDA的实证[J].地理科学,2014,34(12):1438-1444.
[18]白俊红,卞元超.要素市场扭曲与中国创新生产的效率损失[J].中国工业经济,2016(11):39-55.
[19]肖文,林高榜.政府支持、研发管理与技术创新效率:基于中国工业行业的实证分析[J].管理世界,2014(4):71-80.
[20]余泳泽,刘大勇.我国区域创新效率的空间外溢效应与价值链外溢效应:创新价值链视角下的多维空间面板模型研究[J].管理世界, 2013(7):6-20,70.
[21]赵增耀,章小波,沈能.区域协同创新效率的多维溢出效应[J].中国工业经济,2015(1):32-44.
[22]谭俊涛,张平宇,李静.中国区域创新绩效时空演变特征及其影响因素研究[J].地理科学,2016,36(1):39-46.
[23]孙志红,吴悦.财政投入、银行信贷与科技创新效率:基于空间GWR模型的实证分析[J].华东经济管理,2017,31(4):74-81.
[24]应寿英,刘锦扬.基于熵权综合指数法的四川农业现代化发展水平评价[J].农村经济与科技,2018,29(3):168-171.
[25]赵树宽,余海晴,巩顺龙.基于DEA方法的吉林省高技术企业创新效率研究[J].科研管理,2013,34(2):36-43,104.
[26]孙东.我国区域创新的效率及影响因素研究[D].南京:南京大学,2015.
[27]王兴元,于伟,LI F.栖息地特征、市场分布结构及其与企业经营绩效关系实证研究[J].中国工业经济,2010(1):104-113.
[28]白俊红,江可申,李婧,等.区域创新效率的环境影响因素分析:基于DEA Tobit两步法的实证检验[J].研究与发展管理, 2009,21(2):96-102.
[29]关祥勇,王正斌.区域创新环境对区域创新效率影响的实证研究[J].科技管理研究,2011,31(21):16-19,23.
[30]王鹏,陆浩然.区域创新效率的空间差异及其影响因素研究:以广佛肇经济圈为例[J].科技管理研究,2012,32(22):82-86,97.
[31]ATUAHENEGIMA K, KO A.An empirical investigation of the effect of market orientation and entrepreneurship orientation alignment on product innovation[J].Organization Science, 2001, 12(1):54-74.