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City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China

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  • Haisen Wang

    (Institute for the Development of Central China, Wuhan University, Wuhan 430072, China
    Development Research Center of the Yangtze River Economic Belt, Wuhan University, Wuhan 430072, China)

  • Gangqiang Yang

    (Institute for the Development of Central China, Wuhan University, Wuhan 430072, China
    Development Research Center of the Yangtze River Economic Belt, Wuhan University, Wuhan 430072, China)

  • Jiaying Qin

    (Institute for the Development of Central China, Wuhan University, Wuhan 430072, China
    Development Research Center of the Yangtze River Economic Belt, Wuhan University, Wuhan 430072, China)

Abstract

Based on the panel data of 106 cities in the Yangtze River Economic Belt of China from 2007 to 2016, this paper explores the impact of city centrality on the green innovation efficiency and proves the mediation effect of migrants by using spatial econometric model. The results show that there are more and more innovation contacts between cities, and the innovation network is becoming more and more dense. The core cities of the downstream innovation network are mainly Yangzhou, Zhenjiang, Wuxi, Changzhou, Suzhou and Hangzhou; the core cities in the midstream are mainly Wuhan, Changsha and Yichun; the core cities in the upstream are Chengdu and Bazhong. There is an inverted U-shaped relationship between city centrality and green innovation efficiency. In addition, the influence curve of city centrality on the green innovation efficiency of surrounding cities is also inverted U-shaped. Cities with high city centrality attract a large number of migrants that come from cities with lower centrality to improve the green innovation efficiency, but the green innovation efficiency of cities with low city centrality will decline due to lack of talents.

Suggested Citation

  • Haisen Wang & Gangqiang Yang & Jiaying Qin, 2020. "City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China," IJERPH, MDPI, vol. 17(2), pages 1-21, January.
  • Handle: RePEc:gam:jijerp:v:17:y:2020:i:2:p:652-:d:310745
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    as
    1. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    2. Freeman, C., 1991. "Networks of innovators: A synthesis of research issues," Research Policy, Elsevier, vol. 20(5), pages 499-514, October.
    3. Guan, Jiancheng & Zhang, Jingjing & Yan, Yan, 2015. "The impact of multilevel networks on innovation," Research Policy, Elsevier, vol. 44(3), pages 545-559.
    4. Binz, Christian & Truffer, Bernhard, 2017. "Global Innovation Systems—A conceptual framework for innovation dynamics in transnational contexts," Research Policy, Elsevier, vol. 46(7), pages 1284-1298.
    5. Miao, Chenglin & Fang, Debin & Sun, Liyan & Luo, Qiaoling, 2017. "Natural resources utilization efficiency under the influence of green technological innovation," Resources, Conservation & Recycling, Elsevier, vol. 126(C), pages 153-161.
    6. Dong, John Qi & Yang, Chia-Han, 2016. "Being central is a double-edged sword: Knowledge network centrality and new product development in U.S. pharmaceutical industry," Technological Forecasting and Social Change, Elsevier, vol. 113(PB), pages 379-385.
    7. Wu, Haoyi & Guo, Huanxiu & Zhang, Bing & Bu, Maoliang, 2017. "Westward movement of new polluting firms in China: Pollution reduction mandates and location choice," Journal of Comparative Economics, Elsevier, vol. 45(1), pages 119-138.
    8. Yu-Shan Chen & Shyh-Bao Lai & Chao-Tung Wen, 2006. "The Influence of Green Innovation Performance on Corporate Advantage in Taiwan," Journal of Business Ethics, Springer, vol. 67(4), pages 331-339, September.
    9. Bi, Kexin & Huang, Ping & Wang, Xiangxiang, 2016. "Innovation performance and influencing factors of low-carbon technological innovation under the global value chain: A case of Chinese manufacturing industry," Technological Forecasting and Social Change, Elsevier, vol. 111(C), pages 275-284.
    10. Junhong Bai, 2013. "On Regional Innovation Efficiency: Evidence from Panel Data of China's Different Provinces," Regional Studies, Taylor & Francis Journals, vol. 47(5), pages 773-788, May.
    11. Liu, Xiaohui & Buck, Trevor, 2007. "Innovation performance and channels for international technology spillovers: Evidence from Chinese high-tech industries," Research Policy, Elsevier, vol. 36(3), pages 355-366, April.
    12. Jian Cheng Guan & Xia Gao, 2009. "Exploring the h‐index at patent level," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 35-40, January.
    13. Michelle Gittelman & Bruce Kogut, 2003. "Does Good Science Lead to Valuable Knowledge? Biotechnology Firms and the Evolutionary Logic of Citation Patterns," Management Science, INFORMS, vol. 49(4), pages 366-382, April.
    14. Pierre-Alexandre Balland & José Antonio Belso-Martínez & Andrea Morrison, 2016. "The Dynamics of Technical and Business Knowledge Networks in Industrial Clusters: Embeddedness, Status, or Proximity?," Economic Geography, Taylor & Francis Journals, vol. 92(1), pages 35-60, January.
    15. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    16. Elizabeth Garnsey & Yuen Yoong Leong, 2008. "Combining Resource-Based and Evolutionary Theory to Explain the Genesis of Bio-networks," Industry and Innovation, Taylor & Francis Journals, vol. 15(6), pages 669-686.
    17. Vanessa Oltra & Maïder Saint Jean, 2009. "Sectoral systems of environmental innovation: an application to the French automotive industry," Post-Print hal-00274413, HAL.
    18. Chih-Hung Yuan & Yenchun Jim Wu & Kune-muh Tsai, 2019. "Supply Chain Innovation in Scientific Research Collaboration," Sustainability, MDPI, vol. 11(3), pages 1-12, January.
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    Cited by:

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    5. Ming Yi & Yiqian Wang & Modan Yan & Lina Fu & Yao Zhang, 2020. "Government R&D Subsidies, Environmental Regulations, and Their Effect on Green Innovation Efficiency of Manufacturing Industry: Evidence from the Yangtze River Economic Belt of China," IJERPH, MDPI, vol. 17(4), pages 1-17, February.

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