中国城市技术创新能力的空间特征及影响因素——基于空间面板数据模型的研究

doi:10.13249/j.cnki.sgs.2017.01.002

摘要/Abstract

摘要：

基于2003~2013年城市专利数据采用基尼系数、趋势面分析、空间动态面板数据模型等方法探讨了中国城市技术创新能力的空间分布和影响因素。研究发现：① 中国创新能力高的城市高度集聚在沿海三大区域及内地的区域中心城市,随着时间推移,创新能力在空间上呈现扩散的趋势。城市技术创新能力的空间相关性逐渐增强,推动了创新的区域扩散和空间溢出。② 发明专利、外观专利和实用新型专利的创新水平依次降低,空间集聚程度依次提高,空间相关性依次提高。③ 固定效应面板数据的空间滞后模型和空间Durbin模型的计量结果发现,城市技术创新能力存在显著的空间溢出效应,邻近城市技术创新能力的提升有助于提升该市的创新能力。政府支持、工业基础、高等教育资源、创新投入、经济外向度显著影响城市技术创新能力水平的提升,且政府支持和城市高等教育资源对城市技术创新能力的影响出现增强趋势。

关键词: 城市, 专利, 技术创新能力, 空间面板数据, 趋势面分析

Abstract:

The article explored the spatial pattern and determinants of Chinese urban innovative capabilities based on Gini index, trend surface analysis, spatial panel data model methods using urban patents data during 2003 to 2013. The results show that: 1) Spatial pattern of Chinese urban capabilities is highly agglomerated in center cities in three coastal metropolitan areas and regional center cities inland. The hot spots of innovation are highly agglomerated in regions around Beijing, Shanghai, and Shenzhen. Innovation abilities are spreading to inland cities with the time goes, although high innovative cities still agglomerated in coastal region. The Gini index of three patent output have decreased since 2011. 2) The technology level decreases with the invention patent, design patent, utility-patent, while the agglomeration level proxy by the Gini index, rises in sequence. 3) Spatial correlation of three kinds of patents is all significantly positive, and the correlation has been strengthened especially for appearance patent and utility patent. The correlation of appearance patent, utility patent and invention patent decrease in turn which indicates that lower technology can be spread and spillover more easily. The spatial trend surface analysis shows that there are high east and low west trend of innovative abilities, the north-south trend is not obvious, except for the utility patent show the inverse U shape of “high middle and low end” trend. 4) The results of spatial panel econometric models show that there are significant spillovers among urban innovative capabilities. The main influential factors include government support, industrial foundation, higher education sources, innovation input and economic openness, in which the influences of government support and higher education resources have been reinforced. The results show that dependent and independent variables have significant spatial dependence, indicates that urban abilities are heavily affected by the surrounding areas, the higher innovative surrounding areas can promote local innovative abilities. The spatial lag effect denotes that the high education resources and industrial foundation of neighboring cities have positive effects on cities innovation output, while the government support of neighboring cities have negative effect on urban innovation output. 5) Therefore, to promote urban innovation capabilities, government should still put forward the concept of innovation-driven concept, try to attract and nurture innovative enterprises; second, to promote urban higher education qualities and manufacture foundations, encourage enterprises to promote R&D input, and encourage the cooperation between industry, school and research; third, government should induce the innovation cooperation among cities, regions, and universities, drive the free flow of talents and innovative elements and promote the innovative spillovers among cites.

Key words: urban, patent, innovation, spatial panel data, trend surface analysis

中图分类号:

K902

王俊松, 颜燕, 胡曙虹. 中国城市技术创新能力的空间特征及影响因素——基于空间面板数据模型的研究[J]. 地理科学, 2017, 37(1): 11-18.

Junsong Wang, Yan Yan, Shuhong Hu. Spatial Pattern and Determinants of Chinese Urban Innovative Capabilities Base on Spatial Panel Data Model[J]. SCIENTIA GEOGRAPHICA SINICA, 2017, 37(1): 11-18.

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年份	发明授权			实用新型			外观设计
年份	Moran’s I		Z		Moran’s I	Z	Moran’s I	Z
2003	0.001	0.48		0.039^***	3.24		0.061^***	7.02
2004	0.003	0.54		0.054^***	4.42		0.180^***	14.09
2005	0.006	0.80		0.063^***	5.11		0.158^***	12.67
2006	0.008	0.92		0.073^***	5.81		0.160^***	12.65
2007	0.007	0.89		0.079^***	6.31		0.164^***	13.36
2008	0.007	0.84		0.082^***	6.52		0.200^***	15.44
2009	0.008	0.92		0.092^***	7.30		0.203^***	15.73
2010	0.011	1.15		0.100^***	7.79		0.205^***	15.86
2011	0.015^*	1.49		0.150^***	11.46		0.272^***	22.41
2012	0.025^***	2.34		0.197^***	14.76		0.255^***	22.13
2013	0.034^***	3.13		0.204^***	15.28		0.270^***	23.03

	最小二乘法模型	固定效应模型	固定效应的空间滞后效应模型	固定效应的空间 Durbin模型	SDM
	最小二乘法模型	固定效应模型	固定效应的空间滞后效应模型	固定效应的空间 Durbin模型	2005	2012
ρ	-	-	0.046^***	0.041^***	0.004	0.001
研发投入比重	0.000^***	0.000^***	0.000^*	0.000^***	8.555^***	5.368^***
高等教育教师数	0.317^***	0.131^***	0.087^***	0.084^***	0.433^***	0.681^***
科技支出比重	36.686^***	33.047^***	8.989^***	13.937^***	40.710	49.088^***
外资比重	0.490^***	0.491^***	0.222^***	0.397^***	45.995^**	43.229^*
工业产值比重	1.060^***	1.293^***	0.223^***	0.204^***	0.737^***	0.215^**
w1x_研发投入比重	-	-	-	0.000^***	-	-
w1x_高等教育教师数	-	-	-	0.094^***	-	-
w1x_科技支出比重	-	-	-	-0.376^*	-	-
w1x_外资比重	-	-	-	-0.011	-	-
w1x_工业产值比重	-	-	-	0.071^***	-	-
常数	-1.062^**	0.025	0.025	-8.633^***	-1.670^***	-2.478^***
样本数	2280	2280	2280	2280	285	285
地区数	-	285	-	-	285	285
R-squared	0.653	0.451	0.983	0.979	0.870	0.967