知识溢出下区域生态技术创新效率的测算及影响因素研究
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摘要
运用GML指数测算了2000—2015年知识溢出下的区域生态技术创新效率,并分解为技术进步和效率改进,与不考虑非合意产出的结果进行比较,分析了知识溢出下生态技术创新效率的影响因素.结果显示:我国区域生态技术创新效率呈梯度变动趋势;当考虑非合意产出时,技术进步占主导地位;当不考虑非合意产出时,大部分省市的技术进步下降,效率改进上升,生态技术创新效率来源于效率改进;知识溢出和吸收能力的交互作用及其空间效应能显著提升生态技术创新效率;地理统计距离空间加权矩阵模型的结果更佳,东部地区尤其凸显,东部地区的知识、技术、创新等要素集聚性较强,促使其知识溢出对生态技术创新效率的促进作用最强.
The regional eco-technology innovation efficiency from 2000 to 2015 under knowledge spillover is estimated by GML index. And the efficiency is decomposed into technical progress and efficiency change. It also compares the results under the condition that non-consensual output is not considered. Then the influencing factors of eco-technology innovation efficiency under knowledge spillover simultaneously are analyzed. The results show that China's regional eco-technology innovation efficiency presents gradient trends. Technical progress dominates when it considers non-consensual output,or else technical progress decreases and efficiency change improves in most provinces. Efficiency change plays a more important role in eco-technology innovation efficiency. The interaction and spatial effects of knowledge spillovers and absorptive capacity can significantly improve the eco-technology innovation efficiency. The model which uses geostatistical spatial distance weighting matrix displays better result especially in eastern region. Moreover,the clustering of knowledge,technology,innovation and other factors of eastern China is stronger. It makes eastern China own the strongest promotion of knowledge spillovers to eco-technology innovation efficiency.
The regional eco-technology innovation efficiency from 2000 to 2015 under knowledge spillover is estimated by GML index. And the efficiency is decomposed into technical progress and efficiency change. It also compares the results under the condition that non-consensual output is not considered. Then the influencing factors of eco-technology innovation efficiency under knowledge spillover simultaneously are analyzed. The results show that China's regional eco-technology innovation efficiency presents gradient trends. Technical progress dominates when it considers non-consensual output,or else technical progress decreases and efficiency change improves in most provinces. Efficiency change plays a more important role in eco-technology innovation efficiency. The interaction and spatial effects of knowledge spillovers and absorptive capacity can significantly improve the eco-technology innovation efficiency. The model which uses geostatistical spatial distance weighting matrix displays better result especially in eastern region. Moreover,the clustering of knowledge,technology,innovation and other factors of eastern China is stronger. It makes eastern China own the strongest promotion of knowledge spillovers to eco-technology innovation efficiency.
引文
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[17]郭亚军.基于三阶段DEA模型的工业生产效率研究[J].科研管理,2012,33(11):16-23.
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[19]Oh D.A global Malmquist-Luenberger productivity index[J].Journal of Productivity Analysis,2010,34(3):183-197.
[20]王兵,吴延瑞,颜鹏飞.中国区域环境效率与环境全要素生产率增长[J].经济研究,2010(5):95-109.
[21]陶锋.吸收能力、价值链类型与创新绩效:基于国际代工联盟知识溢出的视角[J].中国工业经济,2011(1):140-150.
[22]Getis A,Aldstadt J.Constructing the spatial weights matrix using a local statistic[M].Berlin,Heidelberg:Springer,2010:147-163.
[23]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.
[24]Qu Xi,Lee L F.Estimating a spatial autoregressive model with an endogenous spatial weight matrix[J].Journal of Econometrics,2015,184(2):209-232.
(1)CO2排放量使用化石燃料(煤炭、汽油、柴油、焦炭、天然气、燃料油和煤油)计算公式■测算得到,其中E为能源消耗量,CC为能源含碳量,NCV为能源净发热值,COF为能源碳氧化率,44和12分别指CO2和C的分子量.
(2)能源消耗量是指水电、煤炭、天然气和石油消耗量的总和.
(1)平均受教育年限采用选择大专以上、高中、初中、小学和文盲半文盲文化人口占总人口的比例与对应文化程度的受教育年数(16年、12年、9年、6年和2年)的加权和来测算.
(1)用■计算任意两地的变差函数,测算σ2,代入ρ(d)=1-V(d)/σ2测算区域关联度ρ(d),选择当ρ(d)最大时对应的σ2,代入V2(d),并用其构造空间加权矩阵W2. r代表变程,东部、中部、西部和东北部4个区域的r分别为1 856. 0 km,710. 0 km,2 297. 1 km和207. 3 km.
(2)对H的测算参照赵增耀等[23]的测算方式.
[2]Kuosmanen T,Kortelainen M.Measuring eco-efficiency of production with data envelopment analysis[J].Journal of Industrial Ecology,2005,9(4):59-72.
[3]Ng R,Yeo Z Q,Low J S C,et al.A method for relative eco-efficiency analysis and improvement:case study of bonding technologies[J].Journal of Cleaner Production,2015,99:320-332.
[4]Kulak M,Nemecek T,Frossard E,et al.Eco-efficiency improvement by using integrative design and life cycle assessment.The case study of alternative bread supply chains in France[J].Journal of Cleaner Production,2016,112(36):2452-2461.
[5]张江雪,朱磊.基于绿色增长的我国各地区工业企业技术创新效率研究[J].数量经济技术经济研究,2012(2):113-125.
[6]马勇,刘军.长江中游城市群产业生态化效率研究[J].经济地理,2015,35(6):124-129.
[7]许晖,王琳,张阳.国际新创企业创业知识溢出及知识整合机制研究:基于天士力国际公司海外员工成长及企业国际化案例[J].管理世界,2015(6):141-153.
[8]Wang Chen,Wu Aiqi.Geographical FDI knowledge spillover and innovation of indigenous firms in China[J].International Business Review,2015,25(4):895-906.
[9]Fukugawa N.Knowledge spillover from university research before the national innovation system reform in Japan:localisation,mechanisms,and intermediaries[J].Asian Journal of Technology Innovation,2016(4):1-23.
[10]王兵,罗佑军.中国区域工业生产效率、环境治理效率与综合效率实证研究:基于RAM网络DEA模型的分析[J].世界经济文汇,2015(1):99-119.
[11]Deng Guangyao,Li Lu,Song Yanan.Provincial water use efficiency measurement and factor analysis in China:based on SBM-DEA model[J].Ecological Indicators,2016,69:12-18.
[12]赵勇,白永秀.知识溢出:一个文献综述[J].经济研究,2009(1):144-156.
[13]唐国华.企业家才能配置与经济增长:基于省际面板数据的经验研究[J].科学学与科学技术管理,2012,33(11):110-116.
[14]沈能.局域知识溢出和生产性服务业空间集聚:基于中国城市数据的空间计量分析[J].科学学与科学技术管理,2013,34(5):61-69.
[15]董有德,孟醒.OFDI、逆向技术溢出与国内企业创新能力:基于我国分价值链数据的检验[J].国际贸易问题,2014(9):120-129.
[16]何枫,祝丽云,马栋栋,等.中国钢铁企业绿色技术效率研究[J].中国工业经济,2015(7):84-98.
[17]郭亚军.基于三阶段DEA模型的工业生产效率研究[J].科研管理,2012,33(11):16-23.
[18]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.
[19]Oh D.A global Malmquist-Luenberger productivity index[J].Journal of Productivity Analysis,2010,34(3):183-197.
[20]王兵,吴延瑞,颜鹏飞.中国区域环境效率与环境全要素生产率增长[J].经济研究,2010(5):95-109.
[21]陶锋.吸收能力、价值链类型与创新绩效:基于国际代工联盟知识溢出的视角[J].中国工业经济,2011(1):140-150.
[22]Getis A,Aldstadt J.Constructing the spatial weights matrix using a local statistic[M].Berlin,Heidelberg:Springer,2010:147-163.
[23]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.
[24]Qu Xi,Lee L F.Estimating a spatial autoregressive model with an endogenous spatial weight matrix[J].Journal of Econometrics,2015,184(2):209-232.
(1)CO2排放量使用化石燃料(煤炭、汽油、柴油、焦炭、天然气、燃料油和煤油)计算公式■测算得到,其中E为能源消耗量,CC为能源含碳量,NCV为能源净发热值,COF为能源碳氧化率,44和12分别指CO2和C的分子量.
(2)能源消耗量是指水电、煤炭、天然气和石油消耗量的总和.
(1)平均受教育年限采用选择大专以上、高中、初中、小学和文盲半文盲文化人口占总人口的比例与对应文化程度的受教育年数(16年、12年、9年、6年和2年)的加权和来测算.
(1)用■计算任意两地的变差函数,测算σ2,代入ρ(d)=1-V(d)/σ2测算区域关联度ρ(d),选择当ρ(d)最大时对应的σ2,代入V2(d),并用其构造空间加权矩阵W2. r代表变程,东部、中部、西部和东北部4个区域的r分别为1 856. 0 km,710. 0 km,2 297. 1 km和207. 3 km.
(2)对H的测算参照赵增耀等[23]的测算方式.
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