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A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods

Author

Listed:
  • Haisheng Chen

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
    Zhejiang Provincial Credit Center, Hangzhou 310007, China)

  • Shuiping Zhu

    (College of Environmental Science and Engineering, Donghua University, Shanghai 201620, China)

  • Jianjun Sun

    (Guangdong Polytechnic of Environmental Protection Engineering, Foshan 528216, China)

  • Kaiyang Zhong

    (School of Economic Information Engineering, Southwestern University of Finance and Economics, Chengdu 611130, China)

  • Manhong Shen

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China)

  • Xiaoli Wang

    (College of Economics and Management, Zhejiang A&F University, Hangzhou 311300, China
    Business School, Jiyang College of Zhejiang A&F University, Zhuji 311800, China)

Abstract

As regional interaction increases in an open economy, a region’s green total factor productivity in agriculture must be considered alongside relationships with other regions. In this study, the slack-based model (SBM) global Malmquist–Luenberger (GML) index is used to measure the green total factor productivity of agriculture in each province of China, and the social network analysis (SNA) and vector autoregressive model (VAR) impulse response function (IRF) are used to examine the spatial network structure and regional interactivity. The research confirms that the absolute value and concentration of agricultural green total factor productivity are generally higher in the south than in the north of China, but the peak is lower in the south than in the north. The network density of agricultural green total factor productivity in China from 2008 to 2019 shows an increase, with the cut-off values of mean, 10, 50, and 100 treated as 4.97%, 2.57%, 3.30%, and 2.43%, respectively. From 2008 to 2019, the central potentials of network entry and network exit of green total factor productivity in China’s agriculture show a “V”-shaped and inverted “V”-shaped evolution path, respectively, with the density of cohesive subgroups growing, which demonstrates that the spatial structure of green total factor productivity in Chinese agriculture has experienced an evolutionary path from polycentric to monocentric to polycentric conditions. The spatial interaction of different cohesive subgroups is intensifying and has a certain degree of self-stability. In terms of regional interaction, the siphon effect of the east on the green development of agriculture in the central and western regions is significant, but the trickle-down effect is not obvious, and the interaction between the central and western regions has a catalytic effect on the efficiency of the green economy of agriculture in both regions. It is recommended that targeted policies be introduced to support the flow of agricultural factors and industrial division of labour between the central and western regions and the south and north, taking into account the actual situation. The novelty of this paper is that it focuses on the green total factor productivity of Chinese agriculture and combines the innovative use of the social network analysis paradigm to analyse the green development of agriculture in a country from a spatial dynamic evolutionary perspective. A limitation of the research methodology in this paper is its poor applicability to closed economy analysis.

Suggested Citation

  • Haisheng Chen & Shuiping Zhu & Jianjun Sun & Kaiyang Zhong & Manhong Shen & Xiaoli Wang, 2022. "A Study of the Spatial Structure and Regional Interaction of Agricultural Green Total Factor Productivity in China Based on SNA and VAR Methods," Sustainability, MDPI, vol. 14(12), pages 1-17, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:12:p:7508-:d:843230
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    References listed on IDEAS

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    2. Haisheng Chen & Dingqing Ni & Shuiping Zhu & Ying Ying & Manhong Shen, 2022. "Does the National Credit Demonstration Policy Affect Urban Green Economy Efficiency? Evidence from the Yangtze River Delta Region of China," IJERPH, MDPI, vol. 19(16), pages 1-20, August.
    3. Liping Zhu & Rui Shi & Lincheng Mi & Pu Liu & Guofeng Wang, 2022. "Spatial Distribution and Convergence of Agricultural Green Total Factor Productivity in China," IJERPH, MDPI, vol. 19(14), pages 1-16, July.
    4. Haisheng Chen & Manhong Shen, 2022. "Do Central Inspections of Environmental Protection Affect the Efficiency of the Green Economy? Evidence from China’s Yangtze River Delta," Sustainability, MDPI, vol. 15(1), pages 1-17, December.
    5. Zhuohui Yu & Qingning Lin & Changli Huang, 2022. "Re-Measurement of Agriculture Green Total Factor Productivity in China from a Carbon Sink Perspective," Agriculture, MDPI, vol. 12(12), pages 1-26, November.
    6. Qian Li & Jianping Wang & Guohong Gao & Yingying Lv & Zhiyu Li & Guanglan Chen, 2023. "Spatial Analysis of Tertiary Industry Evolution Based on Gravity Model: A Case of Central Plains Economic Region in China," Sustainability, MDPI, vol. 15(10), pages 1-22, May.

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