中国中部地区技术关联对产业创新的影响研究

doi:10.11821/dlyj020190049

摘要/Abstract

摘要：

技术关联对地区产业创新和经济增长具有重要影响。本文从演化经济地理学认知邻近的视角出发,借助中部地区制造业企业数据和专利数据,运用技术关联分析方法,探究制造业四位数行业技术关联对产业创新的影响机制。结果表明：随着时间的推移,技术关联对创新能力较弱的中部地区产业创新的促进作用越来越强;对于资源型产业集中、经济发展前景不佳的山西省而言,技术关联的作用并不显著;技术关联对其他五省制造业创新具有正向作用,呈现中间强南北弱的空间格局;从产业门类看,技术关联对技术密集型产业创新促进作用最强,劳动密集型产业次之,资源密集型产业靠后,资本密集型产业最弱。

关键词: 产业创新, 技术关联, 认知邻近, 中部地区

Abstract:

Cognitive proximity based on technological relatedness has an important impact on the knowledge spillover effect between industries. This conclusion refutes the important influence of traditional geographical proximity based on knowledge externality on knowledge spillover. The existing research mainly focused on the promotion of high-tech industry innovation by technological relatedness at national spatial scale, but ignored the analysis of the relationship between traditional manufacturing technological relatedness and industrial innovation at regional spatial scales, especially for underdeveloped regions. This paper draws on the concept of cognitive proximity in evolutionary economic geography, and uses the improved technological relatedness analysis method to take central China as an example to investigate the impact mechanism of technological relatedness on industrial innovation in the four-digit classification level dimension of manufacturing industry. The results show that: (1) the industrial technological relatedness in central China has significantly improved industrial innovation performance, and the effect becomes more and more obvious with the passage of time. (2) From the perspective of regional heterogeneity, the technological relatedness of Shanxi Province, which is dominated by resource-based industries and has poor economic development prospects, is not significant. The level of innovation in manufacturing of another five provinces, namely Henan, Hubei, Hunan, Anhui, and Jiangxi, is obviously promoting, and the spatial pattern shows a strong central but a weak link between the north and south. (3) From the perspective of industry categories, technological relatedness not only plays a significant role in promoting high-tech industrial innovation, but also has a great impact on the promotion of resource-intensive, labor-intensive, and capital-intensive industrial innovations, although the degree of influence is different. The role of technological relatedness in promoting industrial innovation from strong to weak is followed by technology-intensive industries, labor-intensive industries, resource-intensive industries, and capital-intensive industries. (4) In addition, this paper finds that the fierce competitive environment and government policy support contribute to the improvement of manufacturing innovation. Local governments should take measures to promote industrial innovation from two aspects: establishing innovative platforms and technology service institutions to strengthen inter-industry and intra-industry enterprise contact and shorten the cognitive distance between industries; prioritizing the development of industries that have higher technological relatedness with local industries, and introducing related companies, thus to promote the transformation and upgrading of regional industries.

Key words: industrial innovation, technological relatedness, cognitive proximity, central China

郝均, 曾刚, 赵建吉, 胡志强. 中国中部地区技术关联对产业创新的影响研究[J]. 地理研究, 2020, 39(3): 601-610.

HAO Jun, ZENG Gang, ZHAO Jianji, HU Zhiqiang. The impacts of technological relatedness on industrial innovation in central China[J]. GEOGRAPHICAL RESEARCH, 2020, 39(3): 601-610.

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变量	2000年	2007年	2013年
技术关联	1.07	1.06	1.01
企业竞争强度	1.03	1.05	—
外资溢出效应	1.04	1.01	—
政府支持	1.01	1.03	1.02
市场自由化	1.06	1.01	—
企业创新投入	—	1.03	—

	观测值	均值	最大值	劳动密集型	资本密集型	资源密集型	技术密集型
2000年	9386	0.221	1	0.224	0.247	0.198	0.225
2007年	10894	0.188	1	0.183	0.231	0.170	0.188
2013年	14987	0.182	1	0.181	0.210	0.166	0.184

变量	2000年	2007年	2013年
技术关联	1.166334***	8.639486***	39.30765***
企业竞争强度	0.011049**	0.022598**	—
企业创新投入	—	0.000014*	—
政府支持	0.000027	0.000007	0.000235*
市场自由化	-0.070779	-0.828600*	—
外资溢出效应	0.258593**	0.049403	—
N	9386	10894	14987
R²	0.019409	0.058801	0.054576
Log L	-18239.15	-35888.62	-72698.78

变量	山西	河南	湖北	湖南	安徽	江西
技术关联	1.070479	26.256930***	60.696940***	26.316750***	90.981560***	15.380440***
政府支持	0.000042	0.000166***	0.000339***	0.000149***	0.000188**	-0.000006
N	820	3576	2910	2585	3256	1840
R²	0.000606	0.040975	0.140377	0.080001	0.084597	0.025720
Log L	-3309.77	-15837.70	-14030.38	-11304.17	-17300.23	-7318.22

类型	涵盖行业
劳动密集型	食品制造业,酒、饮料和精制茶制造业,纺织业,纺织服装服饰业,皮革、毛皮、羽毛及其制品和制鞋业,木材加工和木、竹、藤、棕、草制品业,家具制造业,造纸及纸制品业,橡胶和塑料制品业
资本密集型	印刷和记录媒介复制业,文教、工美、体育和娱乐用品制造业,化学纤维制造业,黑色金属冶炼及压延加工业,有色金属冶炼及压延加工业,仪器仪表制造业
资源密集型	农副食品加工业,烟草制造业,石油加工、炼焦及核燃料加工业,非金属矿物制造业,金属制品业
技术密集型	化学原料及化学制品制造业,医药制造业,通用设备制造业,专用设备制造业,汽车制造业,铁路、船舶、航空航天和其他运输设备制造业,电气机械及器材制造业,通信设备、计算机及其他电子设备制造业