中国新能源汽车产业创新网络特征及其多维邻近性成因

doi:10.11821/dlyj020201065

摘要/Abstract

摘要：

随着知识经济的发展和创新驱动发展战略的实施,创新网络及其多维邻近性机制已成为政府和学者共同关注的焦点。利用Ucinet软件,从区域、主体性质等维度,刻画了2009—2014年产业快速发展时期的中国新能源汽车专利创新网络特征。运用PLS-SEM模型,探究全国及不同区域创新网络的多维邻近性影响过程及原因。研究发现：① 与中国新能源汽车产业创新活动集中在大城市和三大城市群不同,其合作创新网络主要集聚在京津冀与长三角地区,珠三角地区的创新网络明显较弱。同时,创新能力欠发达的区域出现了与其数量不匹配的合作创新规模。② 总的来看,全国创新网络呈现“核心-边缘”结构,国家电网是网络中最重要的创新主体。在京津冀,大型国企集团控制网络,“核心-边缘”结构突出,合作创新最活跃,网络外向程度高。在长三角,区域内主体类型混合多样,“核心-边缘”结构突出,合作创新活跃,网络外向程度较高。在珠三角,民营企业是网络的主要主体,区域网络结构松散,合作创新活跃度低,网络外向程度高。③ 从创新网络的多维邻近性成因看,在全国尺度,组织关系与知识搜寻是全国网络形成的两大动力。在京津冀,国电集团强有力推动跨区域网络形成。在长三角,国电系及区域大企业影响网络形成,丰富的知识源推动合作创新发生。在珠三角,地理邻近性对民营企业合作创新影响有限,知识需求推动了跨区域合作。

关键词: 区域创新网络, 国有企业, 民营企业, 多维邻近性, 新能源汽车, 中国

Abstract:

In the context of the knowledge-based economy and innovation-driven development strategy, innovation networks and their multidimensional proximity mechanisms have attracted increasing attention from governments and economic geographers. However, knowledge about their regional differences remains limited. Using Ucinet software, the authors describe the technological innovation network characteristics of the new energy vehicle (NEV) industry in China from 2009 to 2014, which corresponds to the period of rapid development of vehicle ownership. The authors use partial least squares structure equation modelling to explore the impacts of multidimensional proximities from national and regional perspectives. The study found that: (1) Unlike innovation, activities are primarily concentrated in metropolitan areas and three major urban agglomerations. The innovation networks of the NEV industry in China are primarily concentrated in the Beijing-Tianjin-Hebei and Yangtze River Delta regions; the innovation networks in the Pearl River Delta region are significantly weaker. Additionally, regions with poor innovation could achieve more innovation cooperatively. (2) For the network characteristics, the national innovation network presents a “core-edge” structure, and the State Grid is the most important innovation actor of the network. In the Beijing-Tianjin-Hebei region, large state-owned enterprise groups control the network which has a prominent “core-edge” structure, the most active collaborative innovation, and a high degree of network extroversion. Regarding the innovation network in the Yangtze River Delta, the types of actors in the region are diversified, the “core-edge” structure is prominent, the intensity of cooperative innovation is high, and the degree of network extroversion is relatively high. In the Pearl River Delta, private enterprises are the principal actors in the network, the regional network structure is loose, cooperative innovation is limited, and the network is highly extroverted. (3) For regional proximity mechanisms in the Beijing-Tianjin-Hebei region, the Grid Group strongly promoted the formation of cross-regional networks. In the Yangtze River Delta, the State Grid and large regional enterprises have influenced the formation of networks, and abundant knowledge sources have promoted cooperation. In the Pearl River Delta, the impact of geographical proximity on the cooperation and innovation in private enterprises is small, and knowledge demand has promoted cross-regional cooperation. Based on these results, we present some policy recommendations in industrial and regional dimensions.

Key words: regional innovation network, state-owned enterprise, private enterprise, proximity, new energy vehicle, China

张凯煌, 千庆兰. 中国新能源汽车产业创新网络特征及其多维邻近性成因[J]. 地理研究, 2021, 40(8): 2170-2187.

ZHANG Kaihuang, QIAN Qinglan. Characteristics and proximities mechanism of China's new energy vehicle industry innovation network[J]. GEOGRAPHICAL RESEARCH, 2021, 40(8): 2170-2187.

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参考文献 61

[1]	Tracey P, Clark G L. Alliances, networks and competitive strategy: Rethinking clusters of innovation. Growth and Change, 2003, 34(1):1-16. DOI: 10.1111/1468-2257.00196. doi: 10.1111/1468-2257.00196
[2]	Freeman L C. Networks of innovators: A synthesis of research issues. Research Policy, 1991, 20(5):499-514. DOI: 10.1016/0048-7333(91)90072-X. doi: 10.1016/0048-7333(91)90072-X
[3]	Lin G C S, Wang C, Zhou Y, et al. Placing technological innovation in globalising China: Production linkage, knowledge exchange and innovative performance of the ICT industry in a developing economy. Urban Studies, 2011, 48(14):2999-3018. DOI: 10.1177/0042098010396232. doi: 10.1177/0042098010396232
[4]	[ Cao Xianzhong, Zeng Gang, Si Yuefang et al. Research progress on firm innovation networks and multi-dimensional proximity from the perspective of economic geography. World Regional Studies, 2019, 28(5):165-171.] DOI: 10.3969/j.issn.1004-9479.2019.05.2018122. doi: 10.3969/j.issn.1004-9479.2019.05.2018122
[5]	Bottazzi L, Peri G. Innovation and spillovers in regions: Evidence from European patent data. European Economic Review, 2003, 47(4):687-710. DOI: 10.1016/S0014-2921(02)00307-0. doi: 10.1016/S0014-2921(02)00307-0
[6]	Boschma R. Proximity and innovation: A critical assessment. Regional Studies, 2005, 39(1):61-74. DOI: 10.1080/0034340052000320887. doi: 10.1080/0034340052000320887
[7]	Balland P A. Proximity and the evolution of collaboration networks: Evidence from research and development projects within the global navigation satellite system (GNSS) industry. Regional Studies, 2012, 46(6):741-756. DOI: 10.1080/00343404.2010.529121. doi: 10.1080/00343404.2010.529121
[8]	Hinzmann S, Cantner U, Graf H. The role of geographical proximity for project performance: Evidence from the German leading-edge cluster competition. Journal of Technology Transfer, 2019, 44(12):1744-1783. DOI: 10.1007/s10961-017-9600-1. doi: 10.1007/s10961-017-9600-1
[9]	Lazzeretti L, Capone F. How proximity matters in innovation networks dynamics along the cluster evolution. A study of the high technology applied to cultural goods. Journal of Business Research, 2016, 69(12):5855-5865. DOI: 10.1016/j.jbusres.2016.04.068. doi: 10.1016/j.jbusres.2016.04.068
[10]	Feng Y, Wang X, Du W, et al. Effects of environmental regulation and FDI on urban innovation in China: A spatial durbin econometric analysis. Journal of Cleaner Production, 2019, 235:210-224. DOI: 10.1016/j.jclepro.2019.06.184. doi: 10.1016/j.jclepro.2019.06.184
[11]	Gao X, Guan J. Networks of scientific journals: An exploration of Chinese patent data. Scientometrics, 2009, 80(1):283-302. DOI: 10.1007/s11192-007-2013-4. doi: 10.1007/s11192-007-2013-4
[12]	[ Zhou Can, Zeng Gang, Cao Xianzhong. Chinese inter-city innovation networks structure and city innovation capability. Geographical Research, 2017, 36(7):1297-1308.] DOI: 10.11821/dlyj201707009. doi: 10.11821/dlyj201707009
[13]	Liang L, Zhu L. Major factors affecting China's inter-regional research collaboration: Regional scientific productivity and geographical proximity. Scientometrics, 2002, 55(2):287-316. DOI: 10.1023/A:1019623925759. doi: 10.1023/A:1019623925759
[14]	Hennemann S, Wang T, Liefner I. Measuring regional science networks in China: A comparison of international and domestic bibliographic data sources. Scientometrics, 2011, 88(2):535-554. DOI: 10.1007/s11192-011-0410-1. doi: 10.1007/s11192-011-0410-1
[15]	Sun Y. Geographic patterns of industrial innovation in China during the 1990s. Tijdschrift Voor Economische En Sociale Geografie, 2003, 94(3):376-389. DOI: 10.1111/1467-9663.00264. doi: 10.1111/1467-9663.00264
[16]	[ Qian Qinglan, Chen Yingbiao. Study on technology innovation within SMEs and regional measures. Economic Geography, 2008, (3):384-387.] DOI: CNKI:SUN:JJDL.0.2008-03-010. doi: CNKI:SUN:JJDL.0.2008-03-010
[17]	Greeven M. The inside story of China's high-tech industry: Making Silicon Valley in Beijing. Regional Studies, 2008, 42(10):1399-1400. DOI: 10.1080/00343400802545499. doi: 10.1080/00343400802545499
[18]	Sun Y, Zhou Y. Innovation and inter-firm technological networking: Evidence from China's information communication technology industry. Erdkunde, 2011, 65(1):55-70. DOI: 10.3112/erdkunde.2011.01.05. doi: 10.3112/erdkunde.2011.01.05
[19]	Li Y, He J, Zhou L. The transfer of innovation capability from universities to enterprises in China: An institutional framework. International Journal of Management & Enterprise Development, 2010, 9(4):385-404. DOI: 10.1504/IJMED.2010.037565. doi: 10.1504/IJMED.2010.037565
[20]	Zhu S, He C, Luo Q. Good neighbors, bad neighbors: Local knowledge spillovers, regional institutions and firm performance in China. Small Business Economics, 2019, 52(3):617-632. DOI: 10.1007/s11187-017-9975-2. doi: 10.1007/s11187-017-9975-2
[21]	[ Ma Shuang, Zeng Gang. Analysis of China's urban innovation network pattern and its proximity mechanism from a multi-scale perspective. Human Geography, 2020, 35(1):95-103.] DOI: 10.13959/j.issn.1003-2398.2020.01.011. doi: 10.13959/j.issn.1003-2398.2020.01.011
[22]	[ Ruan Pingnan, Wang Wenli, Liu Xiaoyan. Dynamic evolution of technological innovation network based on multi-dimensional proximity: Based on OLED industry. R&D Management, 2018, 30(6):59-66.] DOI: 10.13581/j.cnki.rdm.2018.06.006. doi: 10.13581/j.cnki.rdm.2018.06.006
[23]	[ Chao Peipei, Gao Jinyan, Yang Yang er al. Research on the new energy vehicles industry's national development strategy. Strategic Study of CAE, 2016, 18(4):69-75.] DOI: 10.15302/J-SSCAE-2016.04.011. doi: 10.15302/J-SSCAE-2016.04.011
[24]	Zhang X, Rao R, Xie J, et al. The current dilemma and future path of China's electric vehicles. Sustainability, 2014, 6(3):1567-1593. DOI: 10.3390/su6031567. doi: 10.3390/su6031567
[25]	[ Zhang Ling, Li Shufeng, Ma Hongjia. Influence study of network structure on knowledge innovation process: A case of Changchun automobile industrial cluster. Library and Information Service, 2011, 55(4):62-66.] DOI: CNKI:SUN:TSQB.0.2011-04-014. doi: CNKI:SUN:TSQB.0.2011-04-014
[26]	[ Zhang Yongan, Wang Yanni. The innovation network comparative analysis of large automobile enterprises in the United States, Japan and China under the perspective of focal firm innovation network. Science of Science and Management of S.& T., 2011, 32(7):44-51.] DOI: CNKI:SUN:KXXG.0.2011-07-008. doi: CNKI:SUN:KXXG.0.2011-07-008
[27]	[ Wan Wei, Zeng Deming, Feng Ke. Study on the evolution of cliques in industrial innovation networks and its influence on technological innovation performance. Journal of Hunan University: Natural Sciences, 2013, 40(11):120-124.] DOI: 10.3969/j.issn.1674-2974.2013.11.020. doi: 10.3969/j.issn.1674-2974.2013.11.020
[28]	[ Li Jian, Yu Yue. Structural holes in collaboration network, cohesion of knowledge network and exploratory innovation performance: An empirical study on the Chinese automakers. Nankai Business Review, 2018, 21(6):121-130.] DOI: 10.3969/j.issn.1008-3448.2018.06.012. doi: 10.3969/j.issn.1008-3448.2018.06.012
[29]	Stokes L C, Breetz H L. Politics in the US energy transition: Case studies of solar, wind, biofuels and electric vehicles policy. Enery Policy, 2018, 113:76-86. DOI: 10.1016/j.enpol.2017.10.057. doi: 10.1016/j.enpol.2017.10.057
[30]	Lauer J, Liefner I. State-led innovation at the city level: Policy measures to promote new energy vehicles in Shenzhen, China. Geographical Review, 2019, 109(3):436-456. DOI: 10.1111/gere.12320. doi: 10.1111/gere.12320
[31]	Gong H, Wang M Q, Wang H. New energy vehicles in China: Policies, demonstration, and progress. Mitigation & Adaptation Strategies for Global Change, 2013, 18(2):207-228. DOI: 10.1007/s11027-012-9358-6. doi: 10.1007/s11027-012-9358-6
[32]	Sun H, Geng Y, Hu L, et al. Measuring China's new energy vehicle patents: A social network analysis approach. Energy, 2018, 153:685-693. DOI: 10.1016/j.energy.2018.04.077. doi: 10.1016/j.energy.2018.04.077
[33]	[ Guo Yanqing, He Di. Classification of strategic emerging industry innovation network from the perspective of “subject-technology” collaboration: Take the new energy automobile industry as an example. Journal of Industrial Technological Economics, 2017, 36(4):146-152.] 10.3969/j.issn.1004-910X.2017.04.020. doi: 10.3969/j.issn.1004-910X.2017.04.020
[34]	[ Yu Qian, Bai Mengping, Qin Yidong. Does multi-dimensional proximity promote innovation cooperation between new energy automobile enterprises in China?. R&D Management, 2018, 30(6):67-74.] DOI: CNKI:SUN:YJYF.0.2018-06-007. doi: CNKI:SUN:YJYF.0.2018-06-007
[35]	[ He Canfei, Jin Lulu, Liu Ying, How does multi-proximity affect the evolution of export product space in China?. Geographical Research, 2017, 36(9):1613-1626.] DOI: 10.11821/dlyj201709002. doi: 10.11821/dlyj201709002
[36]	[ Shi Yanwen, Li Erling, Li Xiaojian. Impact of geographical proximity and relational proximity on innovation in agriculture industrial cluster based on the Shouguang vegetable industrial cluster. Scientia Geographica Sinica, 2016, 36(5):751-759.] DOI: 10.13249/j.cnki.sgs.2016.05.013. doi: 10.13249/j.cnki.sgs.2016.05.013
[37]	[ LI Lin, Multi-dimensional Proximities and Industrial Cluster Innovation. Beijing: Peking University Press, 2014: 21.]
[38]	Cohen W M, Levinthal D A. Absorptive capacity: A new perspective on learning and innovation. Administrative Science Quarterly, 1990, 35(1):128-152. DOI: 10.1016/B978-0-7506-7223-8.50005-8. doi: 10.1016/B978-0-7506-7223-8.50005-8
[39]	Cantwell J, Santangelo G D. The new geography of corporate research in information and communications technology (ICT). Journal of Evolutionary Economics, 2002, 12(1-2):163-197. DOI: 10.1007/s00191-002-0109-9. doi: 10.1007/s00191-002-0109-9
[40]	Zhang K, Qian Q, Zhao Y. Evolution of Guangzhou biomedical industry innovation network structure and its proximity mechanism. Sustainability, 2020, 12(6):2456. DOI: 10.3390/su12062456. doi: 10.3390/su12062456
[41]	[ Li Lin, Yang Tian. Effect of geographical proximity and organisational proximity on innovations of industrial cluster: An empirical research based on automobile industry clusters in China. China Soft Science, 2011, (9):133-143.] DOI: 10.3969/j.issn.1002-9753.2011.09.014. doi: 10.3969/j.issn.1002-9753.2011.09.014
[42]	Blanc H, Sierra C. The internationalisation of R&D by multinationals: A trade-off between external and internal proximity. Cambridge Journal of Economics, 1999, 23(2):187-206. DOI: 10.1093/cje/23.2.187. doi: 10.1093/cje/23.2.187
[43]	Uzzi B. Social structure and competition in interfirm networks: The paradox of embeddedness. Administrative Science Quarterly, 1997, 42(1):35-67. DOI: 10.2307/2393808. doi: 10.2307/2393808
[44]	Wang C C, Lin G C S. The growth and spatial distribution of China's ICT industry: New geography of clustering and innovation. Issues & Studies, 2008, 44(2):145-192.
[45]	Lin G C S, Yang F F, Hu F Z Y. The new geography of information and consulting services in China: Comparing Beijing and Guangzhou. Habitat International, 2012, 36(4):481-492. DOI: 10.1016/j.habitatint.2012.03.001. doi: 10.1016/j.habitatint.2012.03.001
[46]	[ Hu Yang, Li Xun. The impact of multi-dimensional proximities on university industry cooperative innovation: Case studies of high-tech enterprises in Guangzhou. Geographical Research, 2017, 36(4):695-706.] DOI: 10.11821/dlyj201704008. doi: 10.11821/dlyj201704008
[47]	[ Wang Kai, Hu Chidi, Chen Aihua. How universities networking capabilities affect knowledge synergy innovation performance from universities and industry. Science Research Management, 2019, 40(8):166-178.] DOI: CNKI:SUN:KYGL.0.2019-08-017. doi: CNKI:SUN:KYGL.0.2019-08-017
[48]	Pouder R, St John C H. Hot spots and blind spots: Geographical clusters of firms and innovation. The Academy of Management Review, 1997, 21(4):1192-1225. DOI: 10.2307/259168. doi: 10.2307/259168
[49]	Breschi S, Catalini C. Tracing the links between science and technology: An exploratory analysis of scientists' and inventors' networks. Research Policy, 2010, 39(1):14-26. DOI: 10.1016/j.respol.2009.11.004. doi: 10.1016/j.respol.2009.11.004
[50]	Antonelli C. Collective knowledge communication and innovation: The evidence of technological districts. Regional Studies, 2000, 34(6):535-547. DOI: 10.1080/00343400050085657. doi: 10.1080/00343400050085657
[51]	[ Ye Qin, Zeng Gang, Chen Hongting. Impacts of organizational proximity and cognitive proximity on the innovation network of petroleum equipment manufacturing industry in Dongying. Human Geography, 2017, 32(1):116-122.] DOI: 10.13959/j.issn.1003-2398.2017.01.016. doi: 10.13959/j.issn.1003-2398.2017.01.016
[52]	Li Y, Wang X. Innovation in suburban development zones: Evidence from Nanjing, China. Growth and Change, 2019, 50(1):114-129. DOI: 10.1111/grow.12270. doi: 10.1111/grow.12270
[53]	[ Zhou Can, Cao Xianzhong, Zeng Gang. Cluster networks and evolution path of China's electronic information industry innovation. Geographical Research, 2019, 38(9):2212-2225.] DOI: 10.11821/dlyj020180964. doi: 10.11821/dlyj020180964
[54]	Ter Wal A L J. Cluster emergence and network evolution: A longitudinal analysis of the inventor network in Sophia-Antipolis. Regional Studies, 2013, 47(5):651-668. DOI: 10.1080/00343401003614258. doi: 10.1080/00343401003614258
[55]	Ter Wal A L J. The dynamics of the inventor network in German biotechnology: Geographical proximity versus triadic closure. Journal of Economic Geography, 2013, 14(3):589-620. DOI: 10.1093/jeg/lbs063. doi: 10.1093/jeg/lbs063
[56]	[ Bai Yunfei, Pan Zhongzhi. A study on the time lag effect of China's R&D input on science and technology innovation. Science and Technology Management Research, 2015, 35(4):1-5] DOI: 10.3969/j.issn.1000-7695.2015.04.001. doi: 10.3969/j.issn.1000-7695.2015.04.001
[57]	Hair J F, Ringle C M, Sarstedt M. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory & Practice, 2011, 19(2):139-152. DOI: 10.2753/MTP1069-6679190202. doi: 10.2753/MTP1069-6679190202
[58]	[ Zhao Yijing. The study on dynamic evolution of cooperation innovation network of bio-pharmaceutical cluster in Guangzhou: Based on multidimensional proximities perspective. Guangzhou: Master Dissertation of Guangzhou University, 2019: 104-107.]
[59]	Hong W, Su Y. The effect of institutional proximity in non-local university-industry collaborations: An analysis based on Chinese patent data. Research Policy, 2013, 42(2):454-464. DOI: 10.1016/j.respol.2012.05.012. doi: 10.1016/j.respol.2012.05.012
[60]	Jaffe A B, Henderson R M, Trajtenberg M. Geographic localization of knowledge spillovers as evidenced by patent citations. Quaterly Journal of Economics, 1993, 108(3):577-598. DOI: 10.2307/2118401. doi: 10.2307/2118401
[61]	Balland P, Boschma R, Frenken K. Proximity and innovation: From statics to dynamics. Regional Studies, 2015, 49(6):907-920. DOI: 10.1080/00343404.2014.883598. doi: 10.1080/00343404.2014.883598

典型主体	结点中心度	紧密中心度	间距中心度
国家电网公司	31.712	0.309	15.838
中国电力科学研究院	5.766	0.308	0.222
清华大学	3.604	0.308	2.340
南京南瑞集团公司	3.423	0.308	0.042
江苏省电力公司	3.243	0.308	0.037

区域	结网比例(%)	整体外向度(%)	网络密度(%)	中心势(%)
京津冀	16.02	68.70	11.89	4.08
长三角	6.97	54.01	3.73	4.24
珠三角	2.65	64.00	1.25	1.13

区域	典型主体	结点中心度	紧密中心度	间距中心度
京津冀	国家电网公司	65.672	1.512	54.678
	中国电力科学研究院	11.94	1.498	0.715
	清华大学	7.463	1.498	8.142
长三角	国家电网公司	27.604	0.969	16.25
	南京南瑞集团公司	9.375	0.966	2.627
	东南大学	7.292	0.967	1.389
	浙江吉利控股集团有限公司	4.167	0.543	0.125
珠三角	深圳市沃尔核材股份有限公司	4.255	0.735	0.005
	清华大学	3.546	0.741	0.111
	南方电网科学研究院有限责任公司	3.546	0.741	0.081

潜变量		全国	京津冀	长三角	珠三角
地理邻近	Composite reliability	0.91	0.79	0.89	0.90
	AVE	0.84	0.88	0.81	0.81
地理邻近*认知邻近	Composite reliability	0.90	0.86	0.93	0.95
	AVE	0.95	0.92	0.87	0.90
n		826	366	389	71
路径关系
地理邻近→创新网络		0.04	0.06	-0.02	-0.15
纵向组织邻近→创新网络		0.16^***	0.23^***	0.22^***	0.31^***
横向组织邻近→创新网络		0.03	0.01	0.04
认知邻近→创新网络		0.14^***	0.23^***	0.13^***	0.19^***
纵向组织邻近→地理邻近		0.01	-0.05	-0.09	0.31^***
横向组织邻近→地理邻近		0.04	-0.03	-0.04
纵向组织邻近→认知邻近		0.03	-0.10^*	0.06	0.33^***
横向组织邻近→认知邻近		0.02	-0.02	0.02
地理邻近×认知邻近→创新网络		0.15	0.22	0.05	-0.11

	对比	\|diff\|	t	P
纵向组织邻近→创新网络	京津冀-长三角	0.01	0.20	0.84
	京津冀-珠三角	0.09	1.27	0.21
	长三角-珠三角	0.10	1.10	0.27
认知邻近→创新网络	京津冀-长三角	0.09	2.22	0.03
	京津冀-珠三角	0.04	0.53	0.59
	长三角-珠三角	0.05	0.83	0.41
纵向组织邻近→认知邻近	京津冀-珠三角	0.42	3.17	0.00