IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i24p16678-d1296695.html
   My bibliography  Save this article

Spatiotemporal Evolution and the Influencing Factors of China’s High-Tech Industry GDP Using a Geographical Detector

Author

Listed:
  • Yuan Shan

    (Shaanxi Key Laboratory of Surface System and Environmental Carrying Capacity, Xi’an 710127, China
    Institute of Surface Systems and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China)

  • Ninglian Wang

    (Shaanxi Key Laboratory of Surface System and Environmental Carrying Capacity, Xi’an 710127, China
    Institute of Surface Systems and Hazards, College of Urban and Environmental Sciences, Northwest University, Xi’an 710127, China
    Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China)

Abstract

With the rapid advancement of global technology, high-tech industries have become key drivers for the economic growth of many nations and regions. This study delves into the spatiotemporal dynamics and determinants influencing China’s high-tech sector from 2007 to 2021. The key findings include the following: (1) Nationally, the high-tech sector has been a cornerstone for China’s GDP growth over the preceding 15 years. The expansion rate of the high-tech domain consistently outpaces the broader economy. In particular, since 2015, the percentage of high-tech industries’ GDP has surged to approximately 42%. (2) At the provincial level, the spatial representation of the high-tech sector’s GDP predominantly leans towards the east and the south, revealing pronounced spatial autocorrelation. Nevertheless, the demarcations between east and west and between north and south are progressively diminishing. (3) Regarding influential determinants, R&D internal expenditure, operating revenue, and industry agglomeration have been instrumental in spearheading innovation and bolstering growth within the high-tech realm. These insights are invaluable for comprehending the evolutional nuances of China’s high-tech industry and devising pertinent policy measures.

Suggested Citation

  • Yuan Shan & Ninglian Wang, 2023. "Spatiotemporal Evolution and the Influencing Factors of China’s High-Tech Industry GDP Using a Geographical Detector," Sustainability, MDPI, vol. 15(24), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16678-:d:1296695
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/24/16678/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/24/16678/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ellison, Glenn & Glaeser, Edward L, 1997. "Geographic Concentration in U.S. Manufacturing Industries: A Dartboard Approach," Journal of Political Economy, University of Chicago Press, vol. 105(5), pages 889-927, October.
    2. Nicholas Bloom, 2009. "The Impact of Uncertainty Shocks," Econometrica, Econometric Society, vol. 77(3), pages 623-685, May.
    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. Maurizio Zanardi, 2004. "Antidumping law as a collusive device," Canadian Journal of Economics, Canadian Economics Association, vol. 37(1), pages 95-122, February.
    2. Zheng LU & Xiang DENG, 2017. "Regional Specialization: New Methods Of Measurement And The Trends In China 1987-2007," Applied Econometrics and International Development, Euro-American Association of Economic Development, vol. 17(2), pages 119-134.
    3. Nikolay Hristov & Markus Roth, 2019. "Uncertainty Shocks and Financial Crisis Indicators," CESifo Working Paper Series 7839, CESifo.
    4. Agarwalla, Astha, 2011. "Agglomeration Economies and Productivity Growth in India," IIMA Working Papers WP2011-01-08, Indian Institute of Management Ahmedabad, Research and Publication Department.
    5. Idriss Fontaine, 2021. "Uncertainty and Labour Force Participation," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 83(2), pages 437-471, April.
    6. Nicolas Legrand, 2023. "War in Ukraine: The rational “wait‐and‐see” mode of global food markets," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 45(2), pages 626-644, June.
    7. Wen Xu, 2016. "Estimation of Dynamic Panel Data Models with Stochastic Volatility Using Particle Filters," Econometrics, MDPI, vol. 4(4), pages 1-13, October.
    8. Carlos Medel, 2017. "Forecasting Chilean inflation with the hybrid new keynesian Phillips curve: globalisation, combination, and accuracy," Journal Economía Chilena (The Chilean Economy), Central Bank of Chile, vol. 20(3), pages 004-050, December.
    9. Salzmann, Leonard, 2020. "The Impact of Uncertainty and Financial Shocks in Recessions and Booms," VfS Annual Conference 2020 (Virtual Conference): Gender Economics 224588, Verein für Socialpolitik / German Economic Association.
    10. Yoshito Funashima, 2022. "Economic policy uncertainty and unconventional monetary policy," Manchester School, University of Manchester, vol. 90(3), pages 278-292, June.
    11. Sangyup Choi & Davide Furceri & João Tovar Jalles, 2022. "Heterogeneous gains from countercyclical fiscal policy: new evidence from international industry-level data [Optimal investment with costly reversibility]," Oxford Economic Papers, Oxford University Press, vol. 74(3), pages 773-804.
    12. Hartwell, Christopher A., 2014. "The impact of institutional volatility on financial volatility in transition economies : a GARCH family approach," BOFIT Discussion Papers 6/2014, Bank of Finland, Institute for Economies in Transition.
    13. Marfatia, Hardik A., 2015. "Monetary policy's time-varying impact on the US bond markets: Role of financial stress and risks," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 103-123.
    14. Joel M. David & Venky Venkateswaran, 2019. "The Sources of Capital Misallocation," American Economic Review, American Economic Association, vol. 109(7), pages 2531-2567, July.
    15. Li, Xiao-Ming, 2017. "New evidence on economic policy uncertainty and equity premium," Pacific-Basin Finance Journal, Elsevier, vol. 46(PA), pages 41-56.
    16. Yang, Yong, 2012. "Agglomeration density and tourism development in China: An empirical research based on dynamic panel data model," Tourism Management, Elsevier, vol. 33(6), pages 1347-1359.
    17. Diakonova, Marina & Molina, Luis & Mueller, Hannes & Pérez, Javier J. & Rauh, Christopher, 2024. "The information content of conflict, social unrest and policy uncertainty measures for macroeconomic forecasting," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(4).
    18. Martin Henning & Hans Westlund & Kerstin Enflo, 2023. "Urban–rural population changes and spatial inequalities in Sweden," Regional Science Policy & Practice, Wiley Blackwell, vol. 15(4), pages 878-892, May.
    19. Miescu, Mirela & Rossi, Raffaele, 2021. "COVID-19-induced shocks and uncertainty," European Economic Review, Elsevier, vol. 139(C).
    20. Christopher F Baum & Mustafa Caglayan & Bing Xu, 2017. "The Impact of Uncertainty on Financial Institutions," Boston College Working Papers in Economics 939, Boston College Department of Economics, revised 20 Sep 2018.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jsusta:v:15:y:2023:i:24:p:16678-:d:1296695. 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.