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Spatio-Temporal Evolution and Mechanism Analysis of China’s Regional Innovation Efficiency

Author

Listed:
  • Zhen Xu

    (School of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

  • Xiang Zhu

    (School of Geographic Sciences, Hunan Normal University, Changsha 410081, China)

  • Guoen Wei

    (College of Geography and Ocean Sciences, Nanjing University, Nanjing 210023, China)

  • Xiao Ouyang

    (Hunan Institute of Economic Geography, Hunan University of Finance and Economics, Changsha 410205, China)

Abstract

Improving regional innovation efficiency is the key to developing an innovative country. Exploring the spatio-temporal evolution characteristics of regional innovation efficiency is crucial in the formulation of regional policies and the choice of innovation models. This study used the superdata envelopment analysis method with undesirable outputs in evaluating the innovation efficiency of Chinese provinces. To assess the spatial spillover effects of innovation factors, the spatial autocorrelation and spatial Durbin model were adopted to characterize the spatio-temporal evolution, spatial correlation, and mechanisms of innovation efficiency. The highlights of the results are as follows: (1) The time-series changes in innovation efficiency showed a general trend from declining to increasing. (2) There were pronounced regional differences in innovation efficiency. The innovation efficiencies at the provincial level evolved from being decentralized to concentrated. The innovation efficiency was relatively stable in the eastern region and increased significantly in the central and western regions. The east–center–west evolution pattern gradually weakened. (3) The innovative efficiency exhibited spatial dependence, and the spatial agglomeration continued to increase. The extent of hot spots expanded, while cold spots shrunk slightly. (4) The scientific research environment, entrepreneurial environment, labor quality, and market environment were the essential elements that improved innovation efficiency. The impact of the different factors on innovation efficiency at different periods exhibited significant spatial heterogeneity.

Suggested Citation

  • Zhen Xu & Xiang Zhu & Guoen Wei & Xiao Ouyang, 2021. "Spatio-Temporal Evolution and Mechanism Analysis of China’s Regional Innovation Efficiency," Sustainability, MDPI, vol. 13(19), pages 1-12, October.
  • Handle: RePEc:gam:jsusta:v:13:y:2021:i:19:p:11089-:d:651348
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    References listed on IDEAS

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    1. Lv, Chengchao & Shao, Changhua & Lee, Chien-Chiang, 2021. "Green technology innovation and financial development: Do environmental regulation and innovation output matter?," Energy Economics, Elsevier, vol. 98(C).
    2. Veiga, Pedro Mota & Teixeira, Sérgio Jesus & Figueiredo, Ronnie & Fernandes, Cristina I., 2020. "Entrepreneurship, innovation and competitiveness: A public institution love triangle," Socio-Economic Planning Sciences, Elsevier, vol. 72(C).
    3. Zhao, Shu Liang & Song, Wei & Zhu, Dong Yun & Peng, Xiao Bao & Cai, Wenjing, 2013. "Evaluating China's regional collaboration innovation capability from the innovation actors perspective—An AHP and cluster analytical approach," Technology in Society, Elsevier, vol. 35(3), pages 182-190.
    4. Chen, Maozhi & Sinha, Avik & Hu, Kexiang & Shah, Muhammad Ibrahim, 2021. "Impact of technological innovation on energy efficiency in industry 4.0 era: Moderation of shadow economy in sustainable development," Technological Forecasting and Social Change, Elsevier, vol. 164(C).
    5. Moshirian, Fariborz & Tian, Xuan & Zhang, Bohui & Zhang, Wenrui, 2021. "Stock market liberalization and innovation," Journal of Financial Economics, Elsevier, vol. 139(3), pages 985-1014.
    6. Pan, Xiongfeng & Guo, Shucen & Li, Mengna & Song, Jinbo, 2021. "The effect of technology infrastructure investment on technological innovation ——A study based on spatial durbin model," Technovation, Elsevier, vol. 107(C).
    7. H. Fried & C. Lovell & S. Schmidt & S. Yaisawarng, 2002. "Accounting for Environmental Effects and Statistical Noise in Data Envelopment Analysis," Journal of Productivity Analysis, Springer, vol. 17(1), pages 157-174, January.
    8. Kleoniki Kalapouti & Konstantinos Petridis & Chrisovalantis Malesios & Prasanta Kumar Dey, 2020. "Measuring efficiency of innovation using combined Data Envelopment Analysis and Structural Equation Modeling: empirical study in EU regions," Annals of Operations Research, Springer, vol. 294(1), pages 297-320, November.
    9. J. Paul Elhorst, 2014. "Dynamic Spatial Panels: Models, Methods and Inferences," SpringerBriefs in Regional Science, in: Spatial Econometrics, edition 127, chapter 0, pages 95-119, Springer.
    10. Min, Sujin & Kim, Juseong & Sawng, Yeong-Wha, 2020. "The effect of innovation network size and public R&D investment on regional innovation efficiency," Technological Forecasting and Social Change, Elsevier, vol. 155(C).
    11. De Noni, Ivan & Orsi, Luigi & Belussi, Fiorenza, 2018. "The role of collaborative networks in supporting the innovation performances of lagging-behind European regions," Research Policy, Elsevier, vol. 47(1), pages 1-13.
    12. Novakova, Lucia, 2020. "The impact of technology development on the future of the labour market in the Slovak Republic," Technology in Society, Elsevier, vol. 62(C).
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