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Can science and technology resources co-evolve with high-tech industries? Empirical evidence from China

Author

Listed:
  • Luo, Ting
  • Zhang, Yongqing
  • Zheng, Minggui
  • Zheng, Sujiang
  • Gong, Yinyin

Abstract

Based on the theories of synergetic and evolutionary economics, we build a theoretical model of the co-evolutionary mechanisms of the composite systems of science and technology resources and high-tech industries in China. According to the established index system of the composite systems, we calculated the steady-state solution of the co-evolve and drew the potential function curve of science and technology resources and high-tech industries in China during 2010–2022 using the Haken model. Our empirical results show that high-tech industries are the order parameter of co-evolve. During the sample period, there is a synergistic growth effect of two-way promotion between science and technology resources and high-tech industries in China. However, within the system, the positive feedback mechanism of science and technology resources to high-tech industries in China is not perfect, and the allocation of science and technology resources needs to be further optimized, with a low degree of system order and large room for improvement. In addition, the endowment of science and technology resources and the development degree of high-tech industries in China are different in the eight comprehensive economic zones, and their co-evolvement is different. Therefore, by combining the advantages and allocating the elements of regional science and technology resources, we can better leverage the comparative advantages of high-tech industries in China and improve the degree of co-evolve in the system.

Suggested Citation

  • Luo, Ting & Zhang, Yongqing & Zheng, Minggui & Zheng, Sujiang & Gong, Yinyin, 2024. "Can science and technology resources co-evolve with high-tech industries? Empirical evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
  • Handle: RePEc:eee:tefoso:v:208:y:2024:i:c:s0040162524004633
    DOI: 10.1016/j.techfore.2024.123665
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    References listed on IDEAS

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    1. Hong, Jin & Feng, Bing & Wu, Yanrui & Wang, Liangbing, 2016. "Do government grants promote innovation efficiency in China's high-tech industries?," Technovation, Elsevier, vol. 57, pages 4-13.
    2. Edmondson, Duncan L. & Kern, Florian & Rogge, Karoline S., 2019. "The co-evolution of policy mixes and socio-technical systems: Towards a conceptual framework of policy mix feedback in sustainability transitions," Research Policy, Elsevier, vol. 48(10).
    3. Zhang, Gongyi & Zhao, Shukuan & Xi, Yujuan & Liu, Na & Xu, Xiaobo, 2018. "Relating science and technology resources integration and polarization effect to innovation ability in emerging economies: An empirical study of Chinese enterprises," Technological Forecasting and Social Change, Elsevier, vol. 135(C), pages 188-198.
    4. Khoshnevis, Pegah & Teirlinck, Peter, 2018. "Performance evaluation of R&D active firms," Socio-Economic Planning Sciences, Elsevier, vol. 61(C), pages 16-28.
    5. Wang, Ya & Pan, Jiao-feng & Pei, Rui-min & Yi, Bo-Wen & Yang, Guo-liang, 2020. "Assessing the technological innovation efficiency of China's high-tech industries with a two-stage network DEA approach," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    6. Castellacci, Fulvio & Natera, Jose Miguel, 2013. "The dynamics of national innovation systems: A panel cointegration analysis of the coevolution between innovative capability and absorptive capacity," Research Policy, Elsevier, vol. 42(3), pages 579-594.
    7. Li, ChangZheng & Razzaq, Asif & Ozturk, Ilhan & Sharif, Arshian, 2023. "Natural resources, financial technologies, and digitalization: The role of institutional quality and human capital in selected OECD economies," Resources Policy, Elsevier, vol. 81(C).
    8. Haschka, Rouven E. & Herwartz, Helmut, 2020. "Innovation efficiency in European high-tech industries: Evidence from a Bayesian stochastic frontier approach," Research Policy, Elsevier, vol. 49(8).
    9. Chen, Zhuo & Yang, Zhenbing & Yang, Lili, 2020. "How to optimize the allocation of research resources? An empirical study based on output and substitution elasticities of universities in Chinese provincial level," Socio-Economic Planning Sciences, Elsevier, vol. 69(C).
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    2. Huang, Donglan & Xu, Guoteng & Li, Chengjiang & Yang, Shu, 2025. "Effects of high-tech industrial agglomeration and innovation on regional economic development in China: Evidence from spatial-temporal analysis and Spatial Durbin Model," Economic Analysis and Policy, Elsevier, vol. 86(C), pages 692-712.

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