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Analysis on the Agricultural Green Production Efficiency and Driving Factors of Urban Agglomerations in the Middle Reaches of the Yangtze River

Author

Listed:
  • Lei Wang

    (School of Business, Hohai University, Changzhou 213022, China)

  • Zengrui Qi

    (School of Business, Hohai University, Changzhou 213022, China)

  • Qinghua Pang

    (School of Business, Hohai University, Changzhou 213022, China)

  • Yibo Xiang

    (Department of Economics, University of Waterloo, Waterloo, ON N2L 3G1, Canada)

  • Yanli Sun

    (College of Management and Economics, Tianjin University, Tianjin 300072, China)

Abstract

As one of the main grain-producing areas in China, urban agglomeration in the middle reaches of the Yangtze River plays an important role in the development of agricultural production for China’s grain supply. The existing studies about agricultural production efficiency lack of regional coordination analysis at both macro and micro levels, and only few studies consider the impact of agricultural production environment pollution and other undesirable outputs. Based on the input–output index system of agricultural green production, Slacks-based model (SBM) was adopted to measure the agricultural green production efficiency of 31 prefecture level cities in the middle reaches of the Yangtze River from 2008 to 2018, and the Tobit model of panel fixed effect was used to analyze the driving effect of external factors that affect the agricultural green production efficiency of urban agglomeration in the middle reaches of the Yangtze River. At the same time, the research methods at both macro and micro levels provide ideas for the research of transregional production efficiency. The results showed that: (1) the agricultural green production efficiency of urban agglomeration in the middle reaches of the Yangtze river is relatively low, with 2009 and 2013 as the inflection points, showing a stable trend of rise and decline; (2) The green agricultural production efficiency of urban agglomeration in the middle reaches of the Yangtze River presents the spatial distribution characteristics of “high in the west and low in the east”. The regional efficiency difference is obvious, the gap gradually expands, develops from the equilibrium to the polarization; (3) Urbanization development and government intervention has a significant restraining effect on the improvement of agricultural green production efficiency, and opening to the outside world produces a remarkable influence on the improvement of agricultural green production efficiency, however, economic development and industrial structure have little impact on the improvement of agricultural green production efficiency. Therefore, it is necessary to increase investment in technological innovation, promote agricultural transformation and upgrading, promote rational factors allocation and promote coordinated development of agriculture based on regional production differences.

Suggested Citation

  • Lei Wang & Zengrui Qi & Qinghua Pang & Yibo Xiang & Yanli Sun, 2020. "Analysis on the Agricultural Green Production Efficiency and Driving Factors of Urban Agglomerations in the Middle Reaches of the Yangtze River," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:97-:d:467612
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    1. Vernon W. Ruttan, 2002. "Productivity Growth in World Agriculture: Sources and Constraints," Journal of Economic Perspectives, American Economic Association, vol. 16(4), pages 161-184, Fall.
    2. Dietrich Vollrath, 2007. "Land Distribution and International Agricultural Productivity," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 89(1), pages 202-216.
    3. Restuccia, Diego & Yang, Dennis Tao & Zhu, Xiaodong, 2008. "Agriculture and aggregate productivity: A quantitative cross-country analysis," Journal of Monetary Economics, Elsevier, vol. 55(2), pages 234-250, March.
    4. Ma, Shuzhong & Feng, Han, 2013. "Will the decline of efficiency in China's agriculture come to an end? An analysis based on opening and convergence," China Economic Review, Elsevier, vol. 27(C), pages 179-190.
    5. Yiannis Panagopoulos & Elias Dimitriou, 2020. "A Large-Scale Nature-Based Solution in Agriculture for Sustainable Water Management: The Lake Karla Case," Sustainability, MDPI, vol. 12(17), pages 1-22, August.
    6. Liu, Yansui & Zou, Lilin & Wang, Yongsheng, 2020. "Spatial-temporal characteristics and influencing factors of agricultural eco-efficiency in China in recent 40 years," Land Use Policy, Elsevier, vol. 97(C).
    7. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    8. Adom, Philip Kofi & Adams, Samuel, 2020. "Decomposition of technical efficiency in agricultural production in Africa into transient and persistent technical efficiency under heterogeneous technologies," World Development, Elsevier, vol. 129(C).
    9. Kuersteiner, Guido M., 2019. "Invariance principles for dependent processes indexed by Besov classes with an application to a Hausman test for linearity," Journal of Econometrics, Elsevier, vol. 211(1), pages 243-261.
    10. Xuhui Ding & Zhu Fu & Hongwen Jia, 2019. "Study on Urbanization Level, Urban Primacy and Industrial Water Utilization Efficiency in the Yangtze River Economic Belt," Sustainability, MDPI, vol. 11(23), pages 1-13, November.
    11. Xu, Li & Tan, Junlan, 2020. "Financial development, industrial structure and natural resource utilization efficiency in China," Resources Policy, Elsevier, vol. 66(C).
    12. Xia, Min & Zhang, Yanyuan & Zhang, Zihong & Liu, Jingjie & Ou, Weixin & Zou, Wei, 2020. "Modeling agricultural land use change in a rapid urbanizing town: Linking the decisions of government, peasant households and enterprises," Land Use Policy, Elsevier, vol. 90(C).
    13. Tamilselvi, R. & Ramesh, M. & Lekshmi, G.S. & Bazaka, Olha & Levchenko, Igor & Bazaka, Kateryna & Mandhakini, M., 2020. "Graphene oxide – Based supercapacitors from agricultural wastes: A step to mass production of highly efficient electrodes for electrical transportation systems," Renewable Energy, Elsevier, vol. 151(C), pages 731-739.
    14. Yin, Pengzhen & Sun, Jiasen & Chu, Junfei & Liang, Liang, 2016. "Evaluating the environmental efficiency of a two-stage system with undesired outputs by a DEA approach: An interest preference perspectiveAuthor-Name: Wu, Jie," European Journal of Operational Research, Elsevier, vol. 254(3), pages 1047-1062.
    15. 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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    4. Zaiyu Fan & Zhen Zhong, 2023. "Spatial Morphological Characteristics and Evolution of Policy-Oriented Urban Agglomerations—Take the Yangtze River Middle Reaches Urban Agglomeration as an Example," Sustainability, MDPI, vol. 15(18), pages 1-20, September.

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