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Efficiency Evaluation and Influencing Factors of Green Innovation in Chinese Resource-Based Cities: Based on SBM-Undesirable and Spatial Durbin Model

Author

Listed:
  • Yaguai Yu

    (Business School, Ningbo University, Ningbo 315211, China
    Donghai Academy, Ningbo University, Ningbo 315211, China)

  • Zanzan Xu

    (Business School, Ningbo University, Ningbo 315211, China)

  • Panyi Shen

    (Business School, Ningbo University, Ningbo 315211, China)

  • Lening Zhang

    (Business School, Ningbo University, Ningbo 315211, China)

  • Taohan Ni

    (Business School, University of Nottingham, Ningbo 315199, China)

Abstract

Based on data from 64 resource-based cities in China from 2010 to 2019, the efficiency of green innovation is evaluated by using the super-efficiency SBM Model with undesired outputs, while influencing factors of green innovation efficiency are analyzed by the spatial Durbin model. The results are as follows. First, as for the efficiency evaluation, the average green innovation efficiency in 62 resource-based cities from 2010 to 2019 is only 0.5689, while the green innovation efficiency of declining cities is the highest, and the growth type is the lowest in the comprehensive planning cities. Second, based on spatial self-correlation in resource-based cities, the government support, and the influencing factors including the industrial structure and economic development, have positive impacts, while the environmental regulations and opening to the outside world will inhibit the urban green innovation. Therefore, to enhance the green innovation efficiency in resource-based cities, some suggestions include formulating differentiated development strategies, forming regional cooperation mechanisms, increasing government scientific and technological support, determining the reasonable intensity of environmental regulations, setting entry barriers for polluting enterprises, and optimizing industrial structure.

Suggested Citation

  • Yaguai Yu & Zanzan Xu & Panyi Shen & Lening Zhang & Taohan Ni, 2022. "Efficiency Evaluation and Influencing Factors of Green Innovation in Chinese Resource-Based Cities: Based on SBM-Undesirable and Spatial Durbin Model," IJERPH, MDPI, vol. 19(21), pages 1-17, October.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:21:p:13772-:d:950916
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    References listed on IDEAS

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    1. Zeng, Shouzhen & Hu, Yingjie & Llopis-Albert, Carlos, 2023. "Stakeholder-inclusive multi-criteria development of smart cities," Journal of Business Research, Elsevier, vol. 154(C).
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    Cited by:

    1. Yu Zhang & Xi Cai & Yanying Mao & Liudan Jiao & Liu Wu, 2023. "What Is the State of Development of Eco-Wellbeing Performance in China? An Analysis from a Three-Stage Network Perspective," Land, MDPI, vol. 12(8), pages 1-18, July.
    2. Zetian Cui & Yancheng Ning & Jia Song & Jun Yang, 2024. "Impact of National Innovative City Policy on Enterprise Green Technology Innovation—Mediation Role of Innovation Environment and R&D Investment," Sustainability, MDPI, vol. 16(4), pages 1-16, February.
    3. Ling Hu & Tai-Wei Chang & Yue-Shi Lee & Show-Jane Yen & Chih-Wen Ting, 2023. "How Does Sustainable Leadership Affect Environmental Innovation Strategy Adoption? The Mediating Role of Environmental Identity," IJERPH, MDPI, vol. 20(1), pages 1-12, January.
    4. Bing Rong & Chentao Zhang & Shuhao Yang & Tongyi Liu & Chengjun Chu, 2023. "Spatial Characteristics and Influencing Factors of Green Development Progress Level of Private Enterprises in China: Based on Large Collection Surveys," Sustainability, MDPI, vol. 15(15), pages 1-21, July.
    5. Yang Yang & Simo Li & Zhaoxian Su & Hao Fu & Wenbin Wang & Yun Wang, 2023. "Research on the Ecological Innovation Efficiency of the Zhongyuan Urban Agglomeration: Measurement, Evaluation and Optimization," Sustainability, MDPI, vol. 15(19), pages 1-24, September.

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