IDEAS home Printed from https://ideas.repec.org/a/gam/jlands/v12y2023i5p1091-d1150349.html
   My bibliography  Save this article

Analysis of the China’s Interprovincial Innovation Connection Network Based on Modified Gravity Model

Author

Listed:
  • Kai Zhu

    (School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310023, China)

  • Zhiling Gu

    (School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jingang Li

    (School of Architecture, Southeast University, Nanjing 210096, China)

Abstract

With the arrival of the era of innovative economy, innovation is of great significance for the development of China, and even the world. Based on statistical data and taking 2009, 2014, and 2019 as node years, this study uses the entropy weight method and a modified gravity model to study the pattern and evolution characteristics of China’s interprovincial innovation network. The results showed that: 1. The weights of innovation output, innovation input, and innovation environment were 0.253, 0.340, and 0.407, respectively. Currently, innovation output is the most representative of the interprovincial innovation connections, but the weight of innovation environment has been increasing year by year, and its importance is constantly highlighted. 2. The overall spatial structure of China’s interprovincial innovation network shows a “core periphery” feature, radiating from coastal provinces such as Jiangsu, Beijing, and Zhejiang to inland provinces, and exhibiting an overall pattern of “strong in the east and weak in the west”. 3. In terms of evolutionary characteristics, Guangdong and Jiangsu ranked in the top two in terms of outward innovation scale from 2009 to 2019. The combined total innovation connections of the top five provinces in 2009, 2014, and 2019 accounted for 70.79%, 64.29%, and 64.24%, respectively. Although the phenomenon of uneven innovation connections exists, it has slowed down. In addition, China’s interprovincial innovation network is gradually becoming enriched, with the most significant change being the number of innovation links, with a gravity level increasing from four in 2009 to twenty-six in 2019. The interprovincial innovation links continue to strengthen, but the focus has not changed significantly; it is still concentrated in the Bohai Rim region and the Yangtze River Delta region. 4. In terms of maximum gravitational lines, Guangdong Province had the highest number of maximum gravitational lines in 2009, 2014, and 2019, with a total of six. The maximum gravitational line change from 2009 to 2019 took place from 2014 to 2019, transitioning from “Jilin-Liaoning” to “Jilin-Heilongjiang”.

Suggested Citation

  • Kai Zhu & Zhiling Gu & Jingang Li, 2023. "Analysis of the China’s Interprovincial Innovation Connection Network Based on Modified Gravity Model," Land, MDPI, vol. 12(5), pages 1-19, May.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:5:p:1091-:d:1150349
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2073-445X/12/5/1091/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2073-445X/12/5/1091/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Weidong Zhu & Zilin Yue & Naiying He & Kuifeng Luan & Li Ye & Chuyi Qian, 2022. "Analysis of China’s Urban Innovation Connection Network Evolution: A Case Study of Henan Province," Sustainability, MDPI, vol. 14(3), pages 1-19, January.
    2. Yingcheng Li & Rui Du, 2022. "Polycentric urban structure and innovation: evidence from a panel of Chinese cities," Regional Studies, Taylor & Francis Journals, vol. 56(1), pages 113-127, January.
    3. Benhabib, Jess & Spiegel, Mark M., 1994. "The role of human capital in economic development evidence from aggregate cross-country data," Journal of Monetary Economics, Elsevier, vol. 34(2), pages 143-173, October.
    4. Chen, Yanguang, 2009. "Urban gravity model based on cross-correlation function and Fourier analyses of spatio-temporal process," Chaos, Solitons & Fractals, Elsevier, vol. 41(2), pages 603-614.
    5. Freeman, C., 1991. "Networks of innovators: A synthesis of research issues," Research Policy, Elsevier, vol. 20(5), pages 499-514, October.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Difan Liu & Yuejian Wang & Lei Wang & Liping Xu & Huanhuan Chen & Yuxiang Ma, 2023. "Analysis of Spatiotemporal Changes in the Gravitational Structure of Urban Agglomerations in Northern and Southern Xinjiang Based on a Gravitational Model," Land, MDPI, vol. 13(1), pages 1-20, December.

    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. 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.
    2. Li Liu & Jin Luo & Xin Xiao & Bisong Hu & Shuhua Qi & Hui Lin & Xiaofang Zu, 2022. "Spatio-Temporal Evolution of Urban Innovation Networks: A Case Study of the Urban Agglomeration in the Middle Reaches of the Yangtze River, China," Land, MDPI, vol. 11(5), pages 1-21, April.
    3. Geoffrey G. Gachino, 2010. "Technological spillovers from multinational presence," Progress in Development Studies, , vol. 10(3), pages 193-210, July.
    4. Gachino, Geoffrey, 2007. "Technological spillovers from multinational presence - Towards a conceptual framework," MERIT Working Papers 2007-017, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).
    5. Kristinn Hermannsson & Patrizio Lecca, 2016. "Human Capital in Economic Development: From Labour Productivity to Macroeconomic Impact," Economic Papers, The Economic Society of Australia, vol. 35(1), pages 24-36, March.
    6. Iamsiraroj, Sasi, 2016. "The foreign direct investment–economic growth nexus," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 116-133.
    7. Raffaello Bronzini & Paolo Piselli, 2006. "Determinants of long-run regional productivity: the role of R&D, human capital and public infrastructure," Temi di discussione (Economic working papers) 597, Bank of Italy, Economic Research and International Relations Area.
    8. Emanuela Marrocu & Raffaele Paci & Stefano Usai, 2013. "Productivity Growth In The Old And New Europe: The Role Of Agglomeration Externalities," Journal of Regional Science, Wiley Blackwell, vol. 53(3), pages 418-442, August.
    9. Kellee S. Tsai, 2017. "Elite Returnees in Beijing and Bangalore: Information Technology and Beyond," HKUST IEMS Working Paper Series 2017-47, HKUST Institute for Emerging Market Studies, revised Dec 2017.
    10. Samuel O. Okafor & Kenneth Jegbefumwen & Olisaemeka D. Maduka & Ambrose C. Okeke, 2016. "A Three-Factor Model of Inclusive, Sustainable and Resilient Economic Development for Developing Countries," Applied Economics and Finance, Redfame publishing, vol. 3(4), pages 57-72, November.
    11. Erica Perego & Lionel Fontagné & Gianluca Santoni, 2022. "MaGE 3.1: Long-term macroeconomic projections of the World economy," International Economics, CEPII research center, issue 172, pages 168-189.
    12. Bethencourt, Carlos & Perera-Tallo, Fernando, 2020. "Human Capital, Economic Growth, and Public Expenditure," ADBI Working Papers 1066, Asian Development Bank Institute.
    13. Günther Rehme, 2007. "Education, Economic Growth and Measured Income Inequality," Economica, London School of Economics and Political Science, vol. 74(295), pages 493-514, August.
    14. Władysław Welfe, 2008. "A Knowledge-Based Economy: New Directions of Macromodelling," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 14(2), pages 167-180, May.
    15. 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.
    16. Marelli Enrico & Sciulli Dario & Signorelli Marcello, 2014. "Skill mismatch of graduates in a local labour market," Экономика региона, CyberLeninka;Федеральное государственное бюджетное учреждение науки «Институт экономики Уральского отделения Российской академии наук», issue 2, pages 181-194.
    17. Chen, Yanguang, 2014. "An allometric scaling relation based on logistic growth of cities," Chaos, Solitons & Fractals, Elsevier, vol. 65(C), pages 65-77.
    18. Rapanna, Patta, 2018. "The Development of Tourism Based on Local Wisdom in Indonesia," INA-Rxiv 5ayft, Center for Open Science.
    19. Marattin, Luigi & Marzo, Massimiliano & Zagaglia, Paolo, 2013. "Distortionary tax instruments and implementable monetary policy," International Review of Economics & Finance, Elsevier, vol. 25(C), pages 219-243.
    20. Sai Ding & John Knight, 2011. "Why has China Grown So Fast? The Role of Physical and Human Capital Formation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 73(2), pages 141-174, April.

    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:gam:jlands:v:12:y:2023:i:5:p:1091-:d:1150349. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.