本地知识基础对新兴产业知识流动的影响——以中国燃料电池产业为例

doi:10.11821/dlxb202104015

地理学报 ›› 2021, Vol. 76 ›› Issue (4): 1006-1018.doi: 10.11821/dlxb202104015

本地知识基础对新兴产业知识流动的影响——以中国燃料电池产业为例

宓泽锋¹(), 曾刚²()

1.浙江工业大学经济学院,杭州 310000
2.华东师范大学中国现代城市研究中心,上海 200062

收稿日期:2019-08-18 修回日期:2020-09-13 出版日期:2021-04-25 发布日期:2021-06-25
通讯作者: 曾刚(1961-), 男, 湖北武汉人, 博士, 教授, 主要从事创新网络与产业集群、生态文明与区域发展模式研究。E-mail: gzeng@re.ecnu.edu.cn
作者简介:宓泽锋(1991-), 男, 浙江余姚人, 博士, 讲师, 研究方向为创新与区域发展。E-mail: mizefeng1991@163.com
基金资助:
国家自然科学基金项目(42001118);国家自然科学基金项目(41701140);国家自然科学基金项目(41901157);国家自然科学基金项目(71774145)

The impact of local knowledge base on knowledge flow of emerging industry: A case of China's fuel cell industry

MI Zefeng¹(), ZENG Gang²()

1. School of Economics, Zhejiang University of Technology, Hangzhou 310000, China
2. The Center for Modern Chinese City Studies, East China Normal University, Shanghai 200062, China

Received:2019-08-18 Revised:2020-09-13 Published:2021-04-25 Online:2021-06-25
Supported by:
National Nastrual Science Foundation of China(42001118);National Nastrual Science Foundation of China(41701140);National Nastrual Science Foundation of China(41901157);National Nastrual Science Foundation of China(71774145)

摘要/Abstract

摘要：

经济地理学者对于新兴产业的产生区位长期存在争论,在中美贸易战背景下,探究其区位规律既是重要的学科问题,又对国家培育新兴产业具有重要指导意义。通过梳理“区域产业分叉”和“区位机会窗口”的观点,辨析出知识流动是影响新兴产业区位的关键点;以燃料电池产业为例,结合“相关多样化”假说的观点,采用面板空间自回归模型对知识基础的具体影响进行测度,结论如下：① 北京、上海、大连是燃料电池技术创新的三大知识流动中心,各地区“增长极”城市间的强联系是当前知识流动的空间格局的基础,而地理邻近的作用并不显著;② 相关技术知识基础对知识吸收能力具有稳定且显著的积极作用,且除用工成本外的多数控制变量显著为正;而非相关技术知识基础在早期呈现显著负相关。正是由于早期不同知识基础与控制变量的不同作用,导致了既集中于发达城市、又具有一定自由度的空间格局。研究结果表明知识经济时代下,知识基础对创新的锚定效应明显,区域产业分叉出现的概率加大,区位机会窗口较难打开。③ 非相关多样化的作用会随着新兴产业技术的持续发展而产生变化,由早期的消极影响逐渐转变为积极影响,应从演化视角重新审视非相关多样化的作用。

关键词: 知识基础, 知识流动, 新兴产业, 相关多样化, 非相关多样化, 燃料电池产业

Abstract:

Economic geographers have long argued about the location of emerging industry. Under the background of Sino-US trade war, exploring the location laws of emerging industries is not only a critical theoretical issue, but also has an important guiding significance for the country to cultivate emerging industry. Starting from the debate between "Regional Branching" and "Windows of Locational Opportunity", this paper identifies that knowledge flow is the key to affecting the location of emerging industry. According to the discussion of the relationship between variety and technological innovation in the related variety hypothesis, we use patent citation data to define related and unrelated variety. Then we use panel spatial autoregressive model to measure the specific impact of each factor in this paper. Finally, we can draw the following conclusions: (1) Beijing, Shanghai and Dalian are the three major knowledge flow centers of China's fuel cell industry. The strong connection between these "growth pole" cities is the basis of the current spatial pattern of knowledge flow, while the role of geographical proximity is not significant. (2) The related technology knowledge base has a stable and significant positive effect on knowledge absorption capacity, and most of the control variables are significantly positive except labor cost. The unrelated technology knowledge base has a significant negative impact in the early stage (the regression coefficient in the first stage was -0.038). Because of the different roles of different knowledge bases and control variables in the early stage (more positive effects and less negative effects), the overall spatial pattern is not only concentrated in the developed cities, but also has some freedom. The research results show that the anchoring effect of knowledge base on innovation is obvious, so "Regional Branching" is more likely to appear in the era of knowledge economy. (3) The impact of unrelated variety has gradually changed from negative in the early stage (-0.038) to positive in the final stage (0.079), which shows that the role of unrelated variety may change with the technological development of emerging industry. We should re-examine the role of unrelated variety from an evolutionary perspective.

Key words: knowledge base, knowledge flow, emerging industry, related variety, unrelated variety, fuel cell industry

宓泽锋, 曾刚. 本地知识基础对新兴产业知识流动的影响——以中国燃料电池产业为例[J]. 地理学报, 2021, 76(4): 1006-1018.

MI Zefeng, ZENG Gang. The impact of local knowledge base on knowledge flow of emerging industry: A case of China's fuel cell industry[J]. Acta Geographica Sinica, 2021, 76(4): 1006-1018.

图/表 6

表1

表2

指标选取结果

指标	类型	表征含义	单位	缩写
城市燃料电池专利总引证量	被解释变量	知识吸收能力	项	$Absorb$
燃料电池领域学术论文数	解释变量	科学知识基础	篇	$Science$
相关IPC类别发明专利数	解释变量	相关技术知识基础	项	$Related$
非相关IPC类别发明专利数	解释变量	无关技术知识基础	项	$Unrelated$
科技支出占公共财政支出比例	控制变量	城市创新投入	%	$Ti$
教育支出占公共财政支出比例	控制变量	城市教育投入	%	$EDU$
职工人数	控制变量	劳动力	人	$Practitioner$
职工平均工资	控制变量	用工成本	元	$Wage$
固定资产投资	控制变量	资金充裕程度	万元	$FAI$

表2

图1

表3

表4

表5

参考文献 30

[1]	Xie Sixin, Yan Jun. Research on strategy development of TechFin based on regional differences from the perspective of Sino-US trade war. Science and Technology Management Research, 2019,39(6):22-28.
	[ 谢泗薪, 闫君. 中美贸易战视角下基于区域差异的科技金融战略发展对策研究. 科技管理研究, 2019,39(6):22-28.]
[2]	Zhang Jinyan. The promotion of the core competitiveness of competitive state-owned enterprises: Status quo, source and improvement of anti-monopoly law: From the ZTE event in Sino-US trade trade war. Taxation and Economy, 2018(6):7-14.
	[ 张金艳. 竞争性国企核心竞争力的提升: 现状、探源及反垄断法制完善: 由中美贸易战中兴事件说起. 税务与经济, 2018(6):7-14.]
[3]	Tang Chang'an, Zhang Lijia, Yin Qiang. Evolution and optimization of spatial layout of strategic emerging industry in China. Economic Geography, 2018,38(5):101-107.
	[ 汤长安, 张丽家, 殷强. 中国战略性新兴产业空间格局演变与优化. 经济地理, 2018,38(5):101-107.]
[4]	Tanner A N. The emergence of new technology-based industries: The case of fuel cells and its technological relatedness to regional knowledge bases. Journal of Economic Geography, 2016,16(3):611-635. doi: 10.1093/jeg/lbv011
[5]	Zhou Yi, He Canfei. Evolution of urban export product in China. Acta Geographica Sinica, 2019,74(6):1097-1111. doi: 10.11821/dlxb201906003
	[ 周沂, 贺灿飞. 中国城市出口产品演化. 地理学报, 2019,74(6):1097-1111.]
[6]	He Canfei. Regional industrial development and evolution: Path dependence or path creation? Geographical Research, 2018,37(7):1253-1267.
	[ 贺灿飞. 区域产业发展演化: 路径依赖还是路径创造? 地理研究, 2018,37(7):1253-1267.]
[7]	Zhao Jianji, Wang Yanhua, Lv Kewen, et al. The evolutionary mechanism of financial industry agglomeration in central cities of inland China: A case study of Zhengdong New District in Zhengzhou. Acta Geographica Sinica, 2017,72(8):1392-1407. doi: 10.11821/dlxb201708005
	[ 赵建吉, 王艳华, 吕可文, 等. 内陆区域中心城市金融产业集聚的演化机理: 以郑东新区为例. 地理学报, 2017,72(8):1392-1407.]
[8]	Zhao Jianji, Wang Yanhua, Miao Changhong. Formation mechanism of new emerging industry: From the perspective of evolutionary economic geography. Economic Geography, 2019,39(6):36-45.
	[ 赵建吉, 王艳华, 苗长虹. 区域新兴产业形成机理: 演化经济地理学的视角. 经济地理, 2019,39(6):36-45.]
[9]	Scott A J, Storper M. High technology industry and regional development: A theoretical critique and reconstruction. International Social Science Journal, 1987,39:215-232.
[10]	Boschma R A, Van der Knaap G A. New high-tech industries and windows of locational opportunity: The role of labour markets and knowledge institutions during the industrial era. Geografiska Annaler: Series B, Human Geography, 1999,81(2):73-89. doi: 10.1111/j.0435-3684.1999.00050.x
[11]	Frenken K, Van Oort F, Verburg T. Related variety, unrelated variety and regional economic growth. Regional Studies, 2007,41(5):685-697. doi: 10.1080/00343400601120296
[12]	Boschma R A. Competitiveness of regions from an evolutionary perspective. Regional Studies, 2004,38(9):1001-1014. doi: 10.1080/0034340042000292601
[13]	Dosi G, Freeman C, Nelson R R, et al. Technical Change and Economic Theory. London: Pinter Publishers, 1988.
[14]	Wang Yongjian, Xie Weihong, Wang Tianhui, et al. Research on the relationship between strong/weak ties and radical innovation: The mediating effect. Management Review, 2016,28(10):111-122.
	[ 王永健, 谢卫红, 王田绘, 等. 强弱关系与突破式创新关系研究: 吸收能力的中介作用和环境动态性的调节效应. 管理评论, 2016,28(10):111-122.]
[15]	Wang Jianping, Wu Xiaoyun. The influencing mechanism of knowledge search on incremental and radical innovation of manufacturing enterprises. Business Management Journal, 2017,39(12):58-72.
	[ 王建平, 吴晓云. 制造企业知识搜寻对渐进式和突破式创新的作用机制. 经济管理, 2017,39(12):58-72.]
[16]	Tanner A N. Regional branching reconsidered: Emergence of the fuel cell industry in European regions. Economic Geography, 2014,90(4):403-427. doi: 10.1111/ecge.12055
[17]	Antonelli C. The Microeconomics of Technological Systems. Oxford: Oxford University Press, 2001.
[18]	Wang Junsong. Agglomeration economy, correlation variety and urban economic growth: Empirical analysis based on panel data of 279 cities at prefecture level or above. Journal of Finance and Economics, 2016,42(5):135-144.
	[ 王俊松. 集聚经济、相关性多样化与城市经济增长: 基于279个地级及以上城市面板数据的实证分析. 财经研究, 2016,42(5):135-144.]
[19]	Mi Zefeng. The impact of local knowledge base on China's fuel cell technology innovation[D]. Shanghai: East China Normal University, 2019.
	[ 宓泽锋. 本地知识基础对中国燃料电池技术创新的影响[D]. 上海: 华东师范大学, 2019.]
[20]	Guo Jiang, Xu Zeqing. Research on the influence of industrial related variety on regional economic resilience: Analysis of threshold effect based on regional innovation level. Science & Technology Progress and Policy, 2019,36(13):39-47.
	[ 郭将, 许泽庆. 产业相关多样性对区域经济韧性的影响: 地区创新水平的门槛效应. 科技进步与对策, 2019,36(13):39-47.]
[21]	Jacobs J. Economy of Cities. New York: Random House, 1969.
[22]	Lyu Lachang, Sun Feixiang, Huang Ru. Innovation-based urbanization: Evidence from 270 cities at the prefecture level or above in China. Acta Geographica Sinica, 2018,73(10):1910-1922. doi: 10.11821/dlxb201810007
	[ 吕拉昌, 孙飞翔, 黄茹. 基于创新的城市化: 中国270个地级及以上城市数据的实证分析. 地理学报, 2018,73(10):1910-1922.]
[23]	Hidalgo C. Why Information Grows: The Evolution of Order, from Atoms to Economies. New York: Basic Books, 2015.
[24]	Xiao J, Boschma R, Andersson M. Industrial diversification in Europe: The differentiated role of relatedness. Economic Geography, 2018,94(5):514-549. doi: 10.1080/00130095.2018.1444989
[25]	Boschma R, Balland P A, Kogler D F. Relatedness and technological change in cities: The rise and fall of technological knowledge in US metropolitan areas from 1981 to 2010. Industrial and Corporate Change, 2015,24(1):223-250. doi: 10.1093/icc/dtu012
[26]	Montresor S, Quatraro F. Regional branching and key enabling technologies: Evidence from European patent data. Economic Geography, 2017,93(4):367-396. doi: 10.1080/00130095.2017.1326810
[27]	Boschma R, Iammarino S. Related variety, trade linkages, and regional growth in Italy. Economic Geography, 2009,85(3):289-311. doi: 10.1111/ecge.2009.85.issue-3
[28]	Saviotti P P, Pyka A. Economic development by the creation of new sectors. Journal of Evolutionary Economics, 2004,14(1):1-35. doi: 10.1007/s00191-003-0179-3
[29]	Storper M, Walker R. The Capitalist Imperative: Territory, Technology, and Industrial Growth. New York: Blackwell, 1989.
[30]	Zhou Kaiguo, Lu Yunzhi, Yang Haisheng. Financial constraints, innovative capability and firms' collaborative innovation. Economic Research Journal, 2017,52(7):94-108.
	[ 周开国, 卢允之, 杨海生. 融资约束、创新能力与企业协同创新. 经济研究, 2017,52(7):94-108.]

序号	引证专利主分类号	占比(%)	数量(项)
1	H01M	44.31	18864
2	B01J	8.93	3802
3	C01B	3.27	1392
4	C02F	2.40	1022
5	B01D	1.95	830
6	C08J	1.94	826
7	C08L	1.87	796
8	C25B	1.80	766
9	H02J	1.78	758
10	C08G	1.44	613
11	B60L	1.20	511
12	C08K	1.09	464
13	G01R	1.04	443
14	C04B	1.02	434
15	G01N	1.01	430

	P	Z	L^*	Pm
lnAbsorb	0.0000	0.0000	0.0000	0.0000
lnScience	0.0000	0.0000	0.0000	0.0000
lnRelated	0.0000	0.0000	0.0000	0.0000
lnUnrelated	0.0000	0.0000	0.0000	0.0000
lnTi	0.0000	0.0000	0.0000	0.0000
lnEDU	0.0000	0.0000	0.0000	0.0000
lnPractitioner	0.0000	0.0000	0.0000	0.0000
lnWage	0.0000	0.0003	0.0000	0.0000
lnFAI	0.0000	0.0000	0.0000	0.0000

变量	被解释变量：lnAbsorb
变量	第一阶段	第二阶段	第三阶段	整体
lnScience	0.800^***	0.205^***	0.252^***	0.865^***
	(0.040)	(0.058)	(0.071)	(0.033)
lnRelated	0.056^***	0.031^***	0.033^***	0.127^***
	(0.018)	(0.012)	(0.013)	(0.016)
lnUnrelated	-0.038^**	0.078	0.079^**	0.013
	(0.017)	(0.049)	(0.034)	(0.017)
lnTi	0.002	0.118^**	0.056^***	0.262^***
	(0.028)	(0.055)	(0.017)	(0.028)
lnEDU	0.001	0.071	0.003	0.217^***
	(0.027)	(0.093)	(0.106)	(0.026)
lnPractitioner	0.090	0.089	0.282^***	0.308^***
	(0.063)	(0.075)	(0.089)	(0.040)
lnWage	-0.015	-0.002	-0.022	-0.030^**
	(0.022)	(0.020)	(0.022)	(0.014)
lnFAI	0.010	0.018	0.030	0.096^***
	(0.014)	(0.025)	(0.027)	(0.011)
Spatial	0.222	0.308^*	0.216	0.255^***
	(0.171)	(0.176)	(0.201)	(0.073)
sigma2_e	0.098^***	0.195^***	0.478^***	0.398^***
	(0.003)	(0.007)	(0.016)	(0.008)
样本数	1746	1455	1746	4947
R²	0.675	0.598	0.609	0.643
城市数	291	291	291	291

变量	被解释变量：lnAbsorb
变量	第一阶段	第二阶段	第三阶段	整体
lnScience	0.800^***	0.205^*	0.252^***	0.865^***
	(0.152)	(0.124)	(0.087)	(0.072)
lnRelated	0.056^**	0.031^***	0.033^***	0.127^***
	(0.0235)	(0.013)	(0.012)	(0.018)
lnUnrelated	-0.038^**	0.078	0.079^**	0.013
	(0.018)	(0.050)	(0.039)	(0.0214)
lnTi	0.002	0.118	0.056^***	0.262^***
	(0.027)	(0.086)	(0.018)	(0.051)
lnEDU	0.001	0.071	0.003	0.217^***
	(0.024)	(0.139)	(0.077)	(0.046)
lnPractitioner	0.090	0.089	0.282	0.308^***
	(0.093)	(0.058)	(0.180)	(0.081)
lnWage	-0.015	-0.002	-0.022	-0.030
	(0.015)	(0.009)	(0.021)	(0.0188)
lnFAI	0.010	0.018	0.030	0.096^***
	(0.013)	(0.026)	(0.026)	(0.019)
Spatial	0.222^*	0.308^*	0.216	0.255^**
	(0.122)	(0.161)	(0.145)	(0.110)
sigma2_e	0.098^***	0.195^***	0.478^***	0.398^***
	(0.018)	(0.026)	(0.042)	(0.030)
样本数	1746	1455	1746	4947
R²	0.675	0.598	0.609	0.643
城市数	291	291	291	291

本地知识基础对新兴产业知识流动的影响——以中国燃料电池产业为例

The impact of local knowledge base on knowledge flow of emerging industry: A case of China's fuel cell industry

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 6

参考文献 30

相关文章 3

Metrics

本文评价

[1]	彭如霞, 夏丽丽, 林剑铬. 创新政策环境对外商直接投资区位选择的影响——以珠江三角洲核心区六市为例[J]. 地理学报, 2021, 76(4): 992-1005.
[2]	朱晟君, 金文纨. 地方出口产品结构及制度环境与企业出口相关多样化[J]. 地理学报, 2021, 76(2): 398-414.
[3]	张翼鸥,谷人旭. 中国城市知识复杂性的空间特征及影响研究[J]. 地理学报, 2018, 73(8): 1421-1432.