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Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities

Author

Listed:
  • Yingshi Shang

    (School of Economics and Management, Qingdao University of Science and Technology, Qingdao 266061, China)

  • Yanmin Niu

    (School of Mathematical Sciences, Ocean University of China, Qingdao 266100, China)

  • Peng Song

    (School of Economics and Management, Qingdao University of Science and Technology, Qingdao 266061, China)

Abstract

Green innovation is an important concept of high-quality development to achieve resource conservation and ecological protection. To explore whether there is an imbalance in the development of green innovation in China and find the reasons for this phenomenon, it is of vital importance to investigate the regional differences of green innovation efficiency (GIE) and its influencing factors. Many scholars have studied the performance of green innovation from the efficiency perspective but concentrated on provincial-level analysis and lacked heterogeneity analysis of the influencing factors. To fill this gap, this study explores the regional differences of GIE of 285 prefecture-level and above cities in China during the period 2003–2019, and then employs the spatial error model (SEM) to explore the heterogeneity of influencing factors. The results show that: (1) The GIE in China and its three regions underwent an overall increasing process, revealing regional heterogeneity, with most efficient cities agglomerated in the Eastern region. (2) The spatial difference of GIE in China was narrowing, and the within-region Gini coefficient in the three regions presented a similar trend. Between-region difference contributed the largest to the regional differences, especially between the Central and Western regions. The kernel density estimation results showed that GIE presents significant spatial characteristic of polarization. (3) The SEM model analysis indicated that economic development, government motivation, industrial structure, financial support, and population scale affected GIE profoundly in China, and there was significant spatial heterogeneity in the impact of each influencing factor. Western cities were mainly driven by governmental support in green innovation, while Eastern and Central cities were driven by economic development and improved industrial structure.

Suggested Citation

  • Yingshi Shang & Yanmin Niu & Peng Song, 2023. "Regional Differences and Influencing Factors of Green Innovation Efficiency in China’s 285 Cities," Sustainability, MDPI, vol. 16(1), pages 1-19, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2023:i:1:p:334-:d:1310318
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    References listed on IDEAS

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    1. Mengchao Yao & Jinjun Duan & Qingsong Wang, 2022. "Spatial and Temporal Evolution Analysis of Industrial Green Technology Innovation Efficiency in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 19(11), pages 1-20, May.
    2. Rennings, Klaus, 2000. "Redefining innovation -- eco-innovation research and the contribution from ecological economics," Ecological Economics, Elsevier, vol. 32(2), pages 319-332, February.
    3. Rennings, Klaus & Ziegler, Andreas & Ankele, Kathrin & Hoffmann, Esther, 2006. "The influence of different characteristics of the EU environmental management and auditing scheme on technical environmental innovations and economic performance," Ecological Economics, Elsevier, vol. 57(1), pages 45-59, April.
    4. Bin Liao & Lin Li, 2023. "Urban green innovation efficiency and its influential factors: the Chinese evidence," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6551-6573, July.
    5. Dagum, Camilo, 1997. "A New Approach to the Decomposition of the Gini Income Inequality Ratio," Empirical Economics, Springer, vol. 22(4), pages 515-531.
    6. Na Zhang & Jinqian Deng & Fayyaz Ahmad & Muhammad Umar Draz, 2020. "Local Government Competition and Regional Green Development in China: The Mediating Role of Environmental Regulation," IJERPH, MDPI, vol. 17(10), pages 1-22, May.
    7. Seiford, Lawrence M. & Zhu, Joe, 2002. "Modeling undesirable factors in efficiency evaluation," European Journal of Operational Research, Elsevier, vol. 142(1), pages 16-20, October.
    8. 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.
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