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Science and Technology Resource Allocation, Spatial Association, and Regional Innovation

Author

Listed:
  • Fuqin Zhang

    (Business School, University of Jinan, Jinan 250002, China)

  • Yue Wang

    (Business School, University of Jinan, Jinan 250002, China)

  • Wei Liu

    (Business School, University of Jinan, Jinan 250002, China)

Abstract

Considering the impact of science and technology resource allocation on regional innovation output, based on the inter-provincial panel data of 30 provinces in China from 1998 to 2017, this paper establishes a regional innovation output growth model, including science and technology resource input and science and technology resource allocation, and investigates the spatial relationship between regional innovation output and the allocation of science and technology resources, the effect of the inter-subjective configuration structure and inter-regional space re-allocation on regional innovation output. The research results show that there are obvious spatial autocorrelation agglomeration characteristics of China’s regional innovation output and science and technology resource input. The efficiency of the allocation of science and technology resources in the region is relatively low. The application-oriented research subjects with enterprise-oriented research are more efficient in investing in science and technology resources, and the promotion of regional innovation output is more significant. The investment in science and technology resources in neighboring provinces will have a significant inhibitory effect on the province’s innovation output. The regional mobility of science and technology resources has a significant role in promoting regional innovation output growth. The effect of science and technology personnel mobility on regional innovation output is better than that of technology capital flows.

Suggested Citation

  • Fuqin Zhang & Yue Wang & Wei Liu, 2020. "Science and Technology Resource Allocation, Spatial Association, and Regional Innovation," Sustainability, MDPI, vol. 12(2), pages 1-19, January.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:2:p:694-:d:310102
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    References listed on IDEAS

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    4. Zhang, Anming & Zhang, Yimin & Zhao, Ronald, 2003. "A study of the R&D efficiency and productivity of Chinese firms," Journal of Comparative Economics, Elsevier, vol. 31(3), pages 444-464, September.
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    Cited by:

    1. Yuanyuan Kou & Huiying Chen & Kai Liu & Huajie Xu, 2023. "Multinational Corporations and Technological Innovation Development of China’s High-Tech Industries: A Heterogeneity-Based Threshold Effect Analysis," Sustainability, MDPI, vol. 15(9), pages 1-19, April.
    2. Hongjie Bao & Ling Shan & Yufei Wang & Yuehua Jiang & Cheonjae Lee & Xufeng Cui, 2021. "How Does Local Real Estate Investment Influence Neighborhood PM 2.5 Concentrations? A Spatial Econometric Analysis," Land, MDPI, vol. 10(5), pages 1-21, May.
    3. Jun Gao & Ning Xu & Ju Zhou, 2023. "Innovative City Construction and Urban Environmental Performance: Empirical Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    4. Hu, Huiting & Yu, Gangning & Xiong, Xueli & Guo, Lijia & Huang, Jiashun, 2022. "Cultural Diversity and Innovation: An Empirical Study from Dialect," Technology in Society, Elsevier, vol. 69(C).
    5. Isabel Proença & Ludgero Glórias, 2021. "Revisiting the Spatial Autoregressive Exponential Model for Counts and Other Nonnegative Variables, with Application to the Knowledge Production Function," Sustainability, MDPI, vol. 13(5), pages 1-22, March.

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