According to the spatial economics theory,economic activities and economic growth always reveal significant spatial clustering characteristics.As a result,innovation interaction coming from spatial clustering can effectively improve green technological innovation output in provinces.Furthermore,due to the path-dependent and self-locking characteristics,industrial green technological innovation output shows a distinct spatial-temporal difference in different provinces.Therefore,it is necessary to explore spatial clustering and differentiation in order to promote the coordinated development of provincial industrial green technology innovation.In order to achieve this goal,using industrial level data of 30 provinces in China from 2004 to 2012,we apply the spatial econometrics method to analyze spatial-temporal evolution characteristics and impact factors of green technological innovation output.The main content includes the following:Firstly,giving the spatial correlation analysis principle and using idea obtained from Cobb-Douglas production function,we build up a spatial econometrics model to analyze the relationship between green technological innovation output of Chinese provincial industries and its factors.In this model,green technological innovation output is regarded as explained variable.Green R&D input and green innovation personnel input are considered as explanatory variables.Environmental regulation intensity,per capita GDP and log green technological innovation output are treated as controlled variables.In addition,we also describe these variables meaning and their measurement methods.Secondly,we concentrate on explaining spatial-temporal evolution characteristics of industrial green technological innovation output in different provinces.Using three phases of the inter-period data,we apply global Moran's I indexes,local Moran scatter diagrams,and LISA clustering maps to explore global and local spatial autocorrelation on green technological innovation output.The results show two important findings.First,in the aspect of global autocorrelation,provincial industrial green technological innovation output is part of the positive spatial autocorrelation distribution mode.This means that the province with higher or lower green technological innovation output tends to close on other provinces with the same characteristics.Second,in the aspect of local autocorrelation,green technological innovation output in most provinces maintains spatial-temporal stability,while only minor regions show spatial-temporal position migration.In the meantime,provincial green technological innovation output in China shows a downward gradient fiom the east to the west.In addition,three kinds of local spatial autocorrelation model appear in three stages,including a high-high concentration area around Jiangsu,a low-high concentration area around Hebei and a low-low concentration area around Xinjiang,Qinghai,Sichuan and Yunnan.Thirdly,we apply OLS,SLM and SEM to estimate the impact factors of Chinese provincial industrial green technological innovation output,based on the function established in the first part.First,the results show that SEM has a higher goodness of fit than OLS and SLM.Besides the effect of local related factors,industrial green technology innovation output of one province is also influenced by random shocks from innovation output of neighboring provinces.Second,green R&D input and per capita GDP have significant positive effect on the output of green technological innovation in all three stages.This finding indicates that improving green R&D input and per capita GDP can increase the output of green technological innovation.Third,environmental regulation has a "threshold effect" on green technological innovation output.Before the inflection point (the first stage),environmental regulation is regarded as an obstacle to green technological innovation.While crossing the inflection point (the second stage and the third stage),it is regarded as a stimulus for the output of green technological innovation.The effect of green innovation personnel input and log green technological innovation output are not significant.The reason might be that variable measurement method is inaccurate.Improving green technological innovation output depends not only on personnel innovation ability and innovative quality but also on personnel input quantity.In summary,it is crucial to study spatial-temporal evolution and the factors of industrial green technological innovation output.Along with the increasing of environmental pollution,each Chinese provincial government is concemed about not only the implementation of green technological innovation but also the formulation of R&D policy for green technological innovation and environmental regulation policy according to regional differences.These measures provide theoretical evidence for the realization of coordinated development and sustainable development ofgreen technological innovation in China.%本文以空间计量经济学为理论基础,并借鉴Cobb-Douglas生产函数,构建了省域工业绿色技术创新产出的影响因素理论模型,其中绿色技术创新产出为被解释变量,绿色R&D投入、绿色创新人员投入为解释变量,环境规制强度、人均GDP及滞后一期的绿色技术创新产出为控制变量;假设省域工业绿色技术创新产出具有空间自相关性,并基于2004-2012年三个阶段的跨期数据,运用全域Moran's I指数、局域Moran散点图和LISA集聚地图对绿色技术创新产出进行全域和局域空间自相关性检验,揭示了省域工业绿色技术创新产出的时空演化特征;应用OLS、SLM、SEM对省域工业绿色技术创新产出的影响因素进行估计.研究结果显示:我国省域工业绿色技术创新产出具有明显的空间依赖性,在地理空间上存在集聚现象;绿色R&D投入和人均GDP对省域工业绿色技术创新产出具有显著的影响效应,环境规制强度对绿色技术创新产出的影响存在“阈值效应”,而绿色创新人员投入和滞后一期绿色技术创新产出的影响效应不显著.
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