Efficiency Analysis of China Deep-Sea Cage Aquaculture Based on the SBM-Malmquist Model

被引:3
|
作者
Zhang, Ying [1 ,2 ]
Li, Meng-Fei [1 ]
Fang, Xiao-Han [2 ]
机构
[1] Ocean Univ China, Sch Management, Qingdao 266100, Peoples R China
[2] Ocean Univ China, Inst Marine Dev, Qingdao 266100, Peoples R China
关键词
deep-sea cage aquaculture (DSCA); SBM-Malmquist model; production efficiency; total factor productivity;
D O I
10.3390/fishes8100529
中图分类号
S9 [水产、渔业];
学科分类号
0908 ;
摘要
Deep-sea cage aquaculture (DSCA) is an important way to expand new space for marine aquaculture, promote the transformation and upgrade of the fishery industry, and optimize the structure of marine aquaculture. Using the panel data of DSCA in China's coastal areas from 2013 to 2021, this study constructs the SBM-Malmquist model to measure the DSCA production efficiency and analyzes its total factor productivity. The results show that the overall DSCA production efficiency exhibited an increasing trend in spite of a sharp decline in 2019. The efficiency exhibited regional differences, being the strongest in the Bohai Sea region, followed by in the Yellow Sea, the South China Sea, and the East China Sea regions. The overall total factor productivity remained generally stable, although a large fluctuation occurred between 2019 and 2021. Both pure technological efficiency and scale efficiency promoted the total factor productivity in 2019-2021, while the efficiency of technological changes in societal aspects declined. This study shows that the DSCA production efficiency is significantly influenced by input factors such as labor and capital investment. In addition, natural disasters inhibit the improvement of the production efficiency to some extent.
引用
收藏
页数:16
相关论文
共 50 条
  • [31] Hydrodynamic Characteristics Analysis and Mooring System Optimization of an Innovative Deep-Sea Aquaculture Platform
    Zhang, Lixin
    Zhen, Xingwei
    Duan, Qiuyang
    Huang, Yi
    Chen, Chao
    Li, Yangyang
    JOURNAL OF MARINE SCIENCE AND ENGINEERING, 2024, 12 (06)
  • [32] Environmental efficiency evaluation for Xiangjiang River basin cities based on an improved SBM model and Global Malmquist index
    An, Qingxian
    Wu, Qifan
    Li, Jinlin
    Xiong, Beibei
    Chen, Xiaohong
    ENERGY ECONOMICS, 2019, 81 : 95 - 103
  • [33] Many-objective optimization for a deep-sea aquaculture vessel based on an improved RBF neural network surrogate model
    Wang, Penghui
    Chen, Zuogang
    Feng, Yukun
    JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, 2021, 26 (02) : 582 - 605
  • [34] Many-objective optimization for a deep-sea aquaculture vessel based on an improved RBF neural network surrogate model
    Penghui Wang
    Zuogang Chen
    Yukun Feng
    Journal of Marine Science and Technology, 2021, 26 : 582 - 605
  • [35] Provincial water use efficiency measurement and factor analysis in China: Based on SBM-DEA model
    Deng, Guangyao
    Li, Lu
    Song, Yanan
    ECOLOGICAL INDICATORS, 2016, 69 : 12 - 18
  • [36] Land-Use Efficiency in Shandong (China): Empirical Analysis Based on a Super-SBM Model
    Pang, Yayuan
    Wang, Xinjun
    SUSTAINABILITY, 2020, 12 (24) : 1 - 22
  • [37] Regional environmental efficiency evaluation in China: Analysis based on the Super-SBM model with undesirable outputs
    Li, Hong
    Fang, Kuangnan
    Yang, Wei
    Wang, Di
    Hong, Xiaoxin
    MATHEMATICAL AND COMPUTER MODELLING, 2013, 58 (5-6) : 1018 - 1031
  • [38] Efficiency evaluation of hotel operations in Mainland China based on the superefficiency SBM model
    Deng, Zutao
    Gao, Yan
    Liang, Bin
    Morrison, Alastair M.
    TOURISM ECONOMICS, 2020, 26 (02) : 276 - 298
  • [39] Agricultural Efficiency in Different Regions of China: An Empirical Analysis Based on Dynamic SBM-DEA Model
    Hsu, Shao-Yin
    Yang, Chih-Yu
    Chen, Yueh-Ling
    Lu, Ching-Cheng
    SUSTAINABILITY, 2023, 15 (09)
  • [40] Calculation of China's environmental efficiency based on the sbm model with undesirable outputs
    Zou, Hai Lei, 1600, Transport and Telecommunication Institute, Lomonosova street 1, Riga, LV-1019, Latvia (18):