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Spatial–Temporal Evolution and Sustainable Type Division of Fishery Science and Technology Innovation Efficiency in China

Author

Listed:
  • Wendong Zhu

    (Management College, Ocean University of China, Qingdao 266100, China)

  • Dahai Li

    (Marine Development Research Institute, Ocean University of China, Qingdao 266100, China)

  • Limin Han

    (Management College, Ocean University of China, Qingdao 266100, China
    Marine Development Research Institute, Ocean University of China, Qingdao 266100, China)

Abstract

Science and technology innovation is an important driving force to promote the development of fishery industry, and is very important to improve the quality of fishery development. In this study, the Super-SBM model was used to evaluate the fishery science and technology innovation efficiency of 30 provinces and cities in China (excluding Hong Kong, Macao, Taiwan and Tibet) from 2011 to 2020. Combined with the kernel density estimation, the spatial and temporal differentiation characteristics were analyzed. Then, from the two dimensions of investment scale and innovation efficiency, the sustainable development types of fishery science and technology innovation were classified. The results show the following: (1) From the perspective of efficiency change, the overall efficiency of fishery science and technology innovation in China increased first and then decreased during 2011–2020, but the overall efficiency level was low, and the efficiency difference between regions gradually widened, and the eastern coastal regions became the development core of fishery science and technology innovation. (2) From the perspective of spatial differentiation characteristics, there was a large gap between the coastal and inland areas in China. The high-efficiency areas were mainly concentrated in the coastal provinces and cities, such as Guangdong, Jiangsu, Shandong, Shanghai and Tianjin, showing a decreasing trend from east to west. (3) From the perspective of investment scale and innovation efficiency, the study regions can be divided into four types: leading area, breakthrough area, catch-up area and backward area. This paper mainly calculates the efficiency of fishery science and technology innovation in various regions, and divides the type areas of fishery science and technology innovation and development. According to the advantages and problems of different types of areas, different development strategies and correction measures are proposed, which can effectively improve the efficiency of resource utilization, avoid resource waste and realize the sustainable development of fishery.

Suggested Citation

  • Wendong Zhu & Dahai Li & Limin Han, 2022. "Spatial–Temporal Evolution and Sustainable Type Division of Fishery Science and Technology Innovation Efficiency in China," Sustainability, MDPI, vol. 14(12), pages 1-19, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7277-:d:838427
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    as
    1. Pengshan Li & Yahui Lv & Chao Zhang & Wenju Yun & Jianyu Yang & Dehai Zhu, 2016. "Analysis and Planning of Ecological Networks Based on Kernel Density Estimations for the Beijing-Tianjin-Hebei Region in Northern China," Sustainability, MDPI, vol. 8(11), pages 1-17, October.
    2. Siran Fang & Xiaoshan Xue & Ge Yin & Hong Fang & Jialin Li & Yongnian Zhang, 2020. "Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model," Sustainability, MDPI, vol. 12(18), pages 1-22, September.
    3. Kao, Chiang, 2016. "Efficiency decomposition and aggregation in network data envelopment analysis," European Journal of Operational Research, Elsevier, vol. 255(3), pages 778-786.
    4. Shijin Wang & Guihong Hua & Cunfang Li, 2019. "Urbanization, Air Quality, and the Panel Threshold Effect in China Based on Kernel Density Estimation," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 55(15), pages 3575-3590, December.
    5. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    6. Hsuan-Shih Lee, 2021. "An integrated model for SBM and Super-SBM DEA models," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 72(5), pages 1174-1182, May.
    7. Kim, Junyung & Shah, Asad Ullah Amin & Kang, Hyun Gook, 2020. "Dynamic risk assessment with bayesian network and clustering analysis," Reliability Engineering and System Safety, Elsevier, vol. 201(C).
    8. W. Na & Z. C. Zhao, 2021. "The comprehensive evaluation method of low-carbon campus based on analytic hierarchy process and weights of entropy," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 23(6), pages 9308-9319, June.
    9. Yongfeng Zhu & Zilong Wang & Shilei Qiu & Lingling Zhu, 2019. "Effects of Environmental Regulations on Technological Innovation Efficiency in China’s Industrial Enterprises: A Spatial Analysis," Sustainability, MDPI, vol. 11(7), pages 1-19, April.
    10. Zhefan Piao & Yueqin Lin, 2020. "Financing innovation and enterprises’ efficiency of technological innovation in the internet industry: Evidence from China," PLOS ONE, Public Library of Science, vol. 15(9), pages 1-19, September.
    11. Du, Juan & Liang, Liang & Zhu, Joe, 2010. "A slacks-based measure of super-efficiency in data envelopment analysis: A comment," European Journal of Operational Research, Elsevier, vol. 204(3), pages 694-697, August.
    12. Weijiang Liu & Yue Bai, 2021. "An Analysis on the Influence of R&D Fiscal and Tax Subsidies on Regional Innovation Efficiency: Empirical Evidence from China," Sustainability, MDPI, vol. 13(22), pages 1-24, November.
    13. Willem Boshoff & Rossouw van Jaarsveld, 2019. "Market Definition Using Consumer Characteristics and Cluster Analysis," South African Journal of Economics, Economic Society of South Africa, vol. 87(3), pages 302-325, September.
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    Cited by:

    1. Yuegang Song & Songlin Jin & Zhenhui Li, 2022. "Venture Capital and Chinese Firms’ Technological Innovation Capability: Effective Evaluation and Mechanism Verification," Sustainability, MDPI, vol. 14(16), pages 1-20, August.
    2. Yunyao Li & Yanji Ma, 2022. "Research on Industrial Innovation Efficiency and the Influencing Factors of the Old Industrial Base Based on the Lock-In Effect, a Case Study of Jilin Province, China," Sustainability, MDPI, vol. 14(19), pages 1-23, October.
    3. Hong Chen & Haowen Zhu & Tianchen Sun & Xiangyu Chen & Tao Wang & Wenhong Li, 2023. "Does Environmental Regulation Promote Corporate Green Innovation? Empirical Evidence from Chinese Carbon Capture Companies," Sustainability, MDPI, vol. 15(2), pages 1-24, January.

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