IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0283853.html
   My bibliography  Save this article

Characteristics and evolution of knowledge innovation network in the Yangtze River Delta urban agglomeration——A case study of China National Knowledge Infrastructure

Author

Listed:
  • Ya Gao
  • Lei Ye

Abstract

With the development of economic globalization, urban agglomerations have become growth poles and core areas of economic development. By building knowledge innovation networks in urban agglomerations, we can effectively improve the strength of inter-city knowledge innovation links and better realize the integrated and synergistic development of the region. This study selected core cities in the Yangtze River Delta urban agglomeration as the study area, constructed the knowledge innovation network based on inter-city dissertation cooperation data from 2010 to 2020, and analyzed the characteristics and evolution of its knowledge network by combining social network analysis and geospatial analysis. The research results show that: (1) with changes in policies and investment in scientific research and innovation, intra-regional thesis cooperation in the Yangtze River Delta urban agglomeration has been increasing and the scale of the knowledge innovation cooperation network is growing; (2) in addition to the core cities radiating innovation resources outward to drive the development of other node cities, other cities are continuously improving their own innovation capabilities, taking the initiative to strengthen knowledge innovation cooperation with core cities and enhancing their own position in the network; (3) there are no longer isolated cities within the Yangtze River Delta urban agglomeration, and a multi-core knowledge network structure centered on Shanghai, Nanjing, Hangzhou, and Suzhou has initially formed, but the network is still spatially heterogeneous; (4) there are still problems within the Yangtze River Delta urban agglomeration such as uneven development of knowledge innovation and low participation of peripheral cities, which need to be addressed jointly by all regions. The article concludes with some suggestions for countermeasures to provide a reference for the Yangtze River Delta urban agglomeration to continuously strengthen intra-regional knowledge cooperation in the future, enhance regional competitiveness, and ultimately achieve synergistic development among cities.

Suggested Citation

  • Ya Gao & Lei Ye, 2023. "Characteristics and evolution of knowledge innovation network in the Yangtze River Delta urban agglomeration——A case study of China National Knowledge Infrastructure," PLOS ONE, Public Library of Science, vol. 18(4), pages 1-16, April.
  • Handle: RePEc:plo:pone00:0283853
    DOI: 10.1371/journal.pone.0283853
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0283853
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0283853&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0283853?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Manfred M. Fischer & Daniel A. Griffith, 2008. "Modeling Spatial Autocorrelation In Spatial Interaction Data: An Application To Patent Citation Data In The European Union," Journal of Regional Science, Wiley Blackwell, vol. 48(5), pages 969-989, December.
    2. Thomas Scherngell & Yuanjia Hu, 2011. "Collaborative Knowledge Production in China: Regional Evidence from a Gravity Model Approach," Regional Studies, Taylor & Francis Journals, vol. 45(6), pages 755-772.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel A. Griffith & Manfred M. Fischer & James LeSage, 2017. "The spatial autocorrelation problem in spatial interaction modelling: a comparison of two common solutions," Letters in Spatial and Resource Sciences, Springer, vol. 10(1), pages 75-86, March.
    2. Na Li & Haiyan Lu & Yongxin Lv, 2022. "High-Speed Railway Facilities, Intercity Accessibility and Urban Innovation Level—Evidence from Cities in Three Chinese Megacity Regions," Land, MDPI, vol. 11(8), pages 1-16, July.
    3. Tamara Mata & Carlos Llano, 2013. "Social networks and trade of services: modelling interregional flows with spatial and network autocorrelation effects," Journal of Geographical Systems, Springer, vol. 15(3), pages 319-367, July.
    4. Shu Yu & Takaya Yuizono, 2021. "A Proximity Approach to Understanding University-Industry Collaborations for Innovation in Non-Local Context: Exploring the Catch-Up Role of Regional Absorptive Capacity," Sustainability, MDPI, vol. 13(6), pages 1-19, March.
    5. Eduardo A. Haddad & Jesus P. Mena-Chalco, Otávio J.G. Sidone, 2016. "Produção Científica e Redes de Colaboração dos Docentes Vinculados aos Programas de Pós-graduação em Economia no Brasil," Working Papers, Department of Economics 2016_10, University of São Paulo (FEA-USP).
    6. Bhatt, Ayushman & Kato, Hironori, 2021. "High-speed rails and knowledge productivity: A global perspective," Transport Policy, Elsevier, vol. 101(C), pages 174-186.
    7. Wolfgang Polasek & Richard Sellner, 2013. "The Does Globalization Affect Regional Growth? Evidence for NUTS-2 Regions in EU-27," DANUBE: Law and Economics Review, European Association Comenius - EACO, issue 1, pages 23-65, March.
    8. Philipp Otto & Wolfgang Schmid, 2018. "Spatiotemporal analysis of German real-estate prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 60(1), pages 41-72, January.
    9. Montobbio, Fabio & Sterzi, Valerio, 2013. "The Globalization of Technology in Emerging Markets: A Gravity Model on the Determinants of International Patent Collaborations," World Development, Elsevier, vol. 44(C), pages 281-299.
    10. Paula Margaretic & Christine Thomas-Agnan & Romain Doucet, 2017. "Spatial dependence in (origin-destination) air passenger flows," Papers in Regional Science, Wiley Blackwell, vol. 96(2), pages 357-380, June.
    11. Lorenzo Cassi & Andrea Morrison & Roberta Rabellotti, 2015. "Proximity and Scientific Collaboration: Evidence from the Global Wine Industry," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 106(2), pages 205-219, April.
    12. Gao, Xue & Zhang, Yi, 2022. "What is behind the globalization of technology? Exploring the interplay of multi-level drivers of international patent extension in the solar photovoltaic industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 163(C).
    13. Scherngell, Thomas & Borowiecki, Martin & Hu, Yuanjia, 2014. "Effects of knowledge capital on total factor productivity in China: A spatial econometric perspective," China Economic Review, Elsevier, vol. 29(C), pages 82-94.
    14. Cui, Weijun & Li, Lu & Chen, Guang, 2022. "Market-value oriented or technology-value oriented? ——Location impacts of industry-university-research (IUR) cooperation bases on innovation performance," Technology in Society, Elsevier, vol. 70(C).
    15. Yongwan Chun, 2008. "Modeling network autocorrelation within migration flows by eigenvector spatial filtering," Journal of Geographical Systems, Springer, vol. 10(4), pages 317-344, December.
    16. Nina Liu & Jiwu Wang & Yan Song, 2019. "Organization Mechanisms and Spatial Characteristics of Urban Collaborative Innovation Networks: A Case Study in Hangzhou, China," Sustainability, MDPI, vol. 11(21), pages 1-18, October.
    17. Rodolfo Metulini & Roberto Patuelli & Daniel A. Griffith, 2018. "A Spatial-Filtering Zero-Inflated Approach to the Estimation of the Gravity Model of Trade," Econometrics, MDPI, vol. 6(1), pages 1-15, February.
    18. Mendieta, Rodrigo & Ontaneda, Diego & Pontarollo, Nicola, 2019. "Canton growth in Ecuador and the role of spatial heterogeneity," Revista CEPAL, Naciones Unidas Comisión Económica para América Latina y el Caribe (CEPAL), December.
    19. Bin Zhu & Jinlin Liu & Yang Fu & Bo Zhang & Ying Mao, 2018. "Spatio-Temporal Epidemiology of Viral Hepatitis in China (2003–2015): Implications for Prevention and Control Policies," IJERPH, MDPI, vol. 15(4), pages 1-17, April.
    20. Luigi Aldieri & Gennaro Guida & Maxim Kotsemir & Concetto Paolo Vinci, 2019. "An investigation of impact of research collaboration on academic performance in Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(4), pages 2003-2040, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pone00:0283853. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.