国家科技创新能力时空差异性评价——中国与全球十国对比分析
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摘要
科技创新规模扩张和科技创新效率提升是增强国家科技创新能力的两条关键途径。基于现有研究鲜有结合科技创新规模和科技创新效率开展国家科技创新能力评价的现状,采用因子分析法,系统筛选国家科技创新规模影响因素,并结合科技创新效率影响因素,完善国家科技创新能力评价指标体系,通过构建DEA-Malmquist指数模型,评价不同时期国家科技创新效率变化指数,以及中国与全球10个国家科技创新能力的时空差异性。研究表明,R&D投入强度、R&D研究人员、专利申请量、科技期刊文章数、高科技产品出口额占制成品出口总额的百分比、科技创新效率是影响国家科技创新能力的关键指标;1991-2014年中国科技创新能力水平呈指数增长态势,有力提升了国家科技实力;全球10个国家科技创新能力水平除日本出现一定波动外,其余九国整体呈小幅递增态势。
The scale expansion and the efficiency improvement of science and technology innovation are two key ways to enhance a country's science and technology innovation ability.Few scholars evaluate national science &technology innovation ability combined with two factors,such as the scale and the efficiency of science and technology innovation.Therefore,the paper ensures the scale indicators of science and technology innovation based on the factor analysis method.And combined with the efficiency indicators of science and technology innovation,the evaluation indicator system of science and technology innovation is improved.Then,the DEA-Malmquist model is given,to evaluate the changing of the efficiency index of science and technology innovation among different periods,and evaluate temporal and spatial differences of science and technology innovation ability between China and other ten countries in 1991-2014,and ensures the changing of influenced factors of science and technology innovation ability and the decomposition analysis of the efficiency factors.The evaluation results show that,the intensity of science &technology investment,R&D researchers,patent applications,the number of scientific journals,the high tech exports and the efficiency of science and technology innovation are the key influence factor of science and technology innovation ability.The ability index of China's science &technology innovation has increased exponentially in the 23 years,ranking ninth of 11 countries in 1991-1992 to ranking first of 11 countries in 2013-2014,to contribute to technological progress and scientific and technological strength in China.The ability index of science and technology innovation in global ten countries was relatively stable.Japan has seen a relatively large decline after a slight increase,and the other nine countries all presented a small incremental trend.
The scale expansion and the efficiency improvement of science and technology innovation are two key ways to enhance a country's science and technology innovation ability.Few scholars evaluate national science &technology innovation ability combined with two factors,such as the scale and the efficiency of science and technology innovation.Therefore,the paper ensures the scale indicators of science and technology innovation based on the factor analysis method.And combined with the efficiency indicators of science and technology innovation,the evaluation indicator system of science and technology innovation is improved.Then,the DEA-Malmquist model is given,to evaluate the changing of the efficiency index of science and technology innovation among different periods,and evaluate temporal and spatial differences of science and technology innovation ability between China and other ten countries in 1991-2014,and ensures the changing of influenced factors of science and technology innovation ability and the decomposition analysis of the efficiency factors.The evaluation results show that,the intensity of science &technology investment,R&D researchers,patent applications,the number of scientific journals,the high tech exports and the efficiency of science and technology innovation are the key influence factor of science and technology innovation ability.The ability index of China's science &technology innovation has increased exponentially in the 23 years,ranking ninth of 11 countries in 1991-1992 to ranking first of 11 countries in 2013-2014,to contribute to technological progress and scientific and technological strength in China.The ability index of science and technology innovation in global ten countries was relatively stable.Japan has seen a relatively large decline after a slight increase,and the other nine countries all presented a small incremental trend.
引文
[1]顾伟男,申玉铭,王书华,等.科技创新能力的空间演变及其与经济发展的关系[J].中国科技论坛,2017(9):23-29.
[2]黄丹,张其明.基于动态因子分析法的中国区域科技创新能力比较研究[J].邵阳学院学报:社会科学版,2017,16(4):78-85.
[3]吴丹.全球视野下中国科技投入规模演变态势分析与预测[J].科技进步与对策,2016,33(13):120-124.
[4]吴丹.全球视野下中国研发投入强度的演变态势分析与预测[J].科技管理研究,2017,37(3):9-14.
[5]安宁,罗珊.主要创新型国家科技投入分析及经验借鉴[J].华南师范大学学报:社会科学版,2008(2):32-37.
[6] PARISI ML,SCHIANTARELLI F,SEMBENELLI A.Productivity,innovation and R&D:micro evidence for Italy[J].European Economic Review,2006,50(8):2037-2061.
[7] DIAZDIAZ NL,SAAPEREZ PD.Novelty of innovation and the effect of existing and recently hired R&D human resources[J].Innovation,2012,14(1):74-89.
[8]吴芸.政府科技投入对科技创新的影响研究——基于40个国家1982-2010年面板数据的实证检验[J].科学学与科学技术管理,2014,35(1):16-22.
[9]吴进红,解娟娟.苏中科技创新能力评价研究[J].扬州大学学报:人文社会科学版,2017(4):47-51.
[10] ZHONG W,YUAN W,LI SX,et al.The performance evaluation of regional R&D investments in China:an application of DEA based on the first official China economic census data[J].Omega,2011,39(4):447-455.
[11] WANG B,LIU WD,ZHANG JB.Evaluation on the efficiency for the allocation of science and technology resources in China based on DEA model[A]//Advances in intelligent systems and computing.Berlin Heidelberg:Springer Publishing,2012,138:135-140.
[12]李泽霞,董瑜,张薇.应用DEA方法评价我国区域科技创新效率[J].图书情报工作,2011,55(18):24-28.
[13]吕晨,曾明彬.基于DEA的中国区域科技投入产出相对效率研究[J].科学管理研究,2014,32(2):101-104.
[14]彭诚,郑长德.基于决策偏好两阶段网络DEA的我国科技投入效率研究[J].科技进步与对策,2014,31(8):125-129.
[15]朱鹏颐,刘东华,黄新焕.动态视角下城市科技创新效率评价研究——以福建九地级市为例[J].科研管理,2017,38(6):43-50.
[16]俞立平,熊德平,武夷山.中国地区科技效率的组合测度研究[J].科学学研究,2011,29(8):1141-1146.
[17]康楠,郑循刚,母培松.基于组合评价的我国区域科技资源配置效率研究[J].华中科技大学学报,2009,23(6):79-83.
[18] LEE SK,MOGI G,HUI KS.A fuzzy analytic hierarchy process(AHP)/data envelopment analysis(DEA)hybrid model for efficiently allocating energy R&D resources:in the case of energy technologies against high oil prices[J].Renewable&Sustainable Energy Reviews,2013,21:347-355.
[19] LEE H,PARK Y,CHOI H.Comparative evaluation of performance of national R&D programs with heterogeneous objectives:a DEA approach[J].European Journal of Operational Research,2009,196(3):847-855.
[20]张治河,冯陈澄,李斌,等.科技投入对国家创新能力的提升机制研究[J].科研管理,2014,35(4):149-160.
[21] CHEN CP,HU JL,YANG CH.Produce patents or journal articles?a cross-country comparison of R&D productivity change[J].Scientometrics,2013,94(3):833-849.
[22]毛良虎,姜莹.长江经济带省域科技创新能力评价及空间关联格局分析[J].科技进步与对策,2016,33(21):126-131.
[23]王业强,梁阁.区域科技创新能力评价与空间格局演变[J].发展研究,2016(4):34-40.
[24]陈艳华.基于熵权TOPSIS的区域科技创新能力实证研究[J].工业技术经济,2017,36(5):46-51.
[25]胡树华,解佳龙.武汉市高技术产业创新能力评价[J].武汉理工大学学报:信息与管理工程版,2011,33(2):258-262.
[26]黄亮,王振,范斐.基于突变级数模型的长江经济带50座城市科技创新能力测度与分析[J].统计与信息论坛,2017,32(4):73-80.
[2]黄丹,张其明.基于动态因子分析法的中国区域科技创新能力比较研究[J].邵阳学院学报:社会科学版,2017,16(4):78-85.
[3]吴丹.全球视野下中国科技投入规模演变态势分析与预测[J].科技进步与对策,2016,33(13):120-124.
[4]吴丹.全球视野下中国研发投入强度的演变态势分析与预测[J].科技管理研究,2017,37(3):9-14.
[5]安宁,罗珊.主要创新型国家科技投入分析及经验借鉴[J].华南师范大学学报:社会科学版,2008(2):32-37.
[6] PARISI ML,SCHIANTARELLI F,SEMBENELLI A.Productivity,innovation and R&D:micro evidence for Italy[J].European Economic Review,2006,50(8):2037-2061.
[7] DIAZDIAZ NL,SAAPEREZ PD.Novelty of innovation and the effect of existing and recently hired R&D human resources[J].Innovation,2012,14(1):74-89.
[8]吴芸.政府科技投入对科技创新的影响研究——基于40个国家1982-2010年面板数据的实证检验[J].科学学与科学技术管理,2014,35(1):16-22.
[9]吴进红,解娟娟.苏中科技创新能力评价研究[J].扬州大学学报:人文社会科学版,2017(4):47-51.
[10] ZHONG W,YUAN W,LI SX,et al.The performance evaluation of regional R&D investments in China:an application of DEA based on the first official China economic census data[J].Omega,2011,39(4):447-455.
[11] WANG B,LIU WD,ZHANG JB.Evaluation on the efficiency for the allocation of science and technology resources in China based on DEA model[A]//Advances in intelligent systems and computing.Berlin Heidelberg:Springer Publishing,2012,138:135-140.
[12]李泽霞,董瑜,张薇.应用DEA方法评价我国区域科技创新效率[J].图书情报工作,2011,55(18):24-28.
[13]吕晨,曾明彬.基于DEA的中国区域科技投入产出相对效率研究[J].科学管理研究,2014,32(2):101-104.
[14]彭诚,郑长德.基于决策偏好两阶段网络DEA的我国科技投入效率研究[J].科技进步与对策,2014,31(8):125-129.
[15]朱鹏颐,刘东华,黄新焕.动态视角下城市科技创新效率评价研究——以福建九地级市为例[J].科研管理,2017,38(6):43-50.
[16]俞立平,熊德平,武夷山.中国地区科技效率的组合测度研究[J].科学学研究,2011,29(8):1141-1146.
[17]康楠,郑循刚,母培松.基于组合评价的我国区域科技资源配置效率研究[J].华中科技大学学报,2009,23(6):79-83.
[18] LEE SK,MOGI G,HUI KS.A fuzzy analytic hierarchy process(AHP)/data envelopment analysis(DEA)hybrid model for efficiently allocating energy R&D resources:in the case of energy technologies against high oil prices[J].Renewable&Sustainable Energy Reviews,2013,21:347-355.
[19] LEE H,PARK Y,CHOI H.Comparative evaluation of performance of national R&D programs with heterogeneous objectives:a DEA approach[J].European Journal of Operational Research,2009,196(3):847-855.
[20]张治河,冯陈澄,李斌,等.科技投入对国家创新能力的提升机制研究[J].科研管理,2014,35(4):149-160.
[21] CHEN CP,HU JL,YANG CH.Produce patents or journal articles?a cross-country comparison of R&D productivity change[J].Scientometrics,2013,94(3):833-849.
[22]毛良虎,姜莹.长江经济带省域科技创新能力评价及空间关联格局分析[J].科技进步与对策,2016,33(21):126-131.
[23]王业强,梁阁.区域科技创新能力评价与空间格局演变[J].发展研究,2016(4):34-40.
[24]陈艳华.基于熵权TOPSIS的区域科技创新能力实证研究[J].工业技术经济,2017,36(5):46-51.
[25]胡树华,解佳龙.武汉市高技术产业创新能力评价[J].武汉理工大学学报:信息与管理工程版,2011,33(2):258-262.
[26]黄亮,王振,范斐.基于突变级数模型的长江经济带50座城市科技创新能力测度与分析[J].统计与信息论坛,2017,32(4):73-80.