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Urban Green Innovation Efficiency in China: Spatiotemporal Evolution and Influencing Factors

Author

Listed:
  • Shumin Dong

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

  • Yuting Xue

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

  • Guixiu Ren

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China)

  • Kai Liu

    (College of Geography and Environment, Shandong Normal University, Jinan 250358, China
    Collaborative Innovation Center of Human–Nature and Green Development in Universities of Shandong, Shandong Normal University, Jinan 250358, China)

Abstract

Investigating urban green innovation efficiency (UGIE) is imperative because it is correlated with the development of an ecological civilization and an innovative country. Spatiotemporal evolution and influencing factors of UGIE are two important scientific problems that are worth exploring. This study presents an indicator system for UGIE that includes input, expected output, and unexpected output, and employs a super-efficiency slacks-based measure (super-SBM) to calculate UGIE in 284 cities at or above the prefecture level in China from 2005 to 2020. Then, we adopted spatial auto-correlation to identify its spatial differences among these cities and Geodetector to evaluate its influencing factors. The results are as follows: (1) The overall UGIE tended to rise, except in northeastern China, megacities, and super large-sized cities. (2) The UGIE of Chinese cities exhibited remarkable spatial differences and auto-correlation, and the “low-low” type enjoyed the most local spatial auto-correlations. (3) Sociocultural factors represented by the number of collections in public libraries became the most important factors affecting the UGIE in China.

Suggested Citation

  • Shumin Dong & Yuting Xue & Guixiu Ren & Kai Liu, 2022. "Urban Green Innovation Efficiency in China: Spatiotemporal Evolution and Influencing Factors," Land, MDPI, vol. 12(1), pages 1-13, December.
    • Handle: RePEc:gam:jlands:v:12:y:2022:i:1:p:75-:d:1015689
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    1. Jie Wu & Dacheng Huang & Zhixiang Zhou & Qingyuan Zhu, 2020. "The regional green growth and sustainable development of China in the presence of sustainable resources recovered from pollutions," Annals of Operations Research, Springer, vol. 290(1), pages 27-45, July.
    2. Adedoyin, Festus Fatai & Erum, Naila & Ozturk, Ilhan, 2022. "Does higher innovation intensity matter for abating the climate crisis in the presence of economic complexities? Evidence from a Global Panel Data," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
    3. Zhang, Hongwei & Shao, Yanmin & Han, Xiping & Chang, Hsu-Ling, 2022. "A road towards ecological development in China: The nexus between green investment, natural resources, green technology innovation, and economic growth," Resources Policy, Elsevier, vol. 77(C).
    4. Liu, Kai & Xue, Mingyue & Peng, Mengjie & Wang, Chengxin, 2020. "Impact of spatial structure of urban agglomeration on carbon emissions: An analysis of the Shandong Peninsula, China," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    5. Sebastian Kobarg & Jutta Stumpf-Wollersheim & Christopher Schlägel & Isabell M. Welpe, 2020. "Green together? The effects of companies’ innovation collaboration with different partner types on ecological process and product innovation," Industry and Innovation, Taylor & Francis Journals, vol. 27(9), pages 953-990, October.
    6. Qu, Jingjing & Wang, Baohui & Liu, Xiaohong, 2022. "A modified super-efficiency network data envelopment analysis: Assessing regional sustainability performance in China," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    7. Isiksal, Aliya Zhakanova & Assi, Ala Fathi, 2022. "Determinants of sustainable energy demand in the European economic area: Evidence from the PMG-ARDL model," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    8. Lining Su, 2022. "The Impact of Coordinated Development of Ecological Environment and Technological Innovation on Green Economy: Evidence from China," IJERPH, MDPI, vol. 19(12), pages 1-15, June.
    9. Matthew G. Burgess & Amanda R. Carrico & Steven D. Gaines & Alessandro Peri & Steve Vanderheiden, 2021. "Prepare developed democracies for long-run economic slowdowns," Nature Human Behaviour, Nature, vol. 5(12), pages 1608-1621, December.
    10. Kevin Riehl & Florian Kiesel & Dirk Schiereck, 2022. "Political and Socioeconomic Factors That Determine the Financial Outcome of Successful Green Innovation," Sustainability, MDPI, vol. 14(6), pages 1-23, March.
    11. Song, Malin & Ai, Hongshan & Li, Xie, 2015. "Political connections, financing constraints, and the optimization of innovation efficiency among China's private enterprises," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 290-299.
    12. Riehl, Kevin & Kiesel, Florian & Schiereck, Dirk, 2022. "Political and Socioeconomic Factors That Determine the Financial Outcome of Successful Green Innovation," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 132099, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    13. Liyuan Zhang & Xiang Ma & Young-Seok Ock & Lingli Qing, 2022. "Research on Regional Differences and Influencing Factors of Chinese Industrial Green Technology Innovation Efficiency Based on Dagum Gini Coefficient Decomposition," Land, MDPI, vol. 11(1), pages 1-20, January.
    14. Chen, Chien-Ming, 2013. "Super efficiencies or super inefficiencies? Insights from a joint computation model for slacks-based measures in DEA," European Journal of Operational Research, Elsevier, vol. 226(2), pages 258-267.
    15. Lu Wang & Wenzhong Ye & Lingming Chen, 2021. "Research on Green Innovation of the Great Changsha-Zhuzhou-Xiangtan City Group Based on Network," Land, MDPI, vol. 10(11), pages 1-15, November.
    16. Zhifeng Zhang & Hongyan Duan & Shuangshuang Shan & Qingzhi Liu & Wenhui Geng, 2022. "The Impact of Green Credit on the Green Innovation Level of Heavy-Polluting Enterprises—Evidence from China," IJERPH, MDPI, vol. 19(2), pages 1-19, January.
    17. Tone, Kaoru, 2001. "A slacks-based measure of efficiency in data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 130(3), pages 498-509, May.
    18. Wenhao Song & Hongyan Yu, 2018. "Green Innovation Strategy and Green Innovation: The Roles of Green Creativity and Green Organizational Identity," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 25(2), pages 135-150, March.
    19. Wensheng Xiao & Haojia Kong & Lifan Shi & Valentina Boamah & Decai Tang, 2022. "The Impact of Innovation-Driven Strategy on High-Quality Economic Development: Evidence from China," Sustainability, MDPI, vol. 14(7), pages 1-20, April.
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    1. Jinchao Huang & Shuang Meng & Jiajie Yu, 2023. "The Effects of the Low-Carbon Pilot City Program on Green Innovation: Evidence from China," Land, MDPI, vol. 12(8), pages 1-26, August.

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