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Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China

Author

Listed:
  • Nan Ke

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Xupeng Zhang

    (School of Public Administration, China University of Geosciences, Wuhan 430074, China)

  • Xinhai Lu

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

  • Bing Kuang

    (School of Public Administration, Central China Normal University, Wuhan 430079, China)

  • Bin Jiang

    (College of Public Administration, Huazhong University of Science and Technology, Wuhan 430074, China)

Abstract

Eco-efficiency of arable land utilization (EALU) emphasizes efficient coordination between land use systems and ecosystems. It is therefore of great significance for agricultural sustainability based on the systematic assessment of EALU. This study took carbon emissions and non-point source pollution resulting from arable land utilization into the measurement system of EALU, and a super-SBM model, kernel density estimation and Tobit regression model were used to analyze regional differences and influencing factors of EALU for 31 provinces in China from 2000 to 2019. The results showed that there was an upward trend in EALU in China from 0.4393 in 2000 to 0.8929 in 2019, with an average annual growth rate of 4.01%. At the regional level, the EALU of three categories of grain functional areas generally maintains an increasing trend, with the highest average value of EALU in main grain marketing areas (MGMAs), followed by grain producing and marketing balance areas (GPMBAs) and main grain producing areas (MGPAs). There are obvious differences in EALU among provinces, and the number of provinces with high eco-efficiency has increased significantly, showing a spatial distribution pattern of “block” clustering. In terms of dynamic evolution, kernel density curves reflect the evolution of EALU in China and grain functional areas with different degrees of polarization characteristics. The results of Tobit regression show that natural conditions, financial support for agriculture, science and technology inputs, level of industrialization, agricultural mechanization, and the living standards of farmers are significant factors resulting in regional disparities of EALU. Therefore, this study proposes the implementation of differentiated arable land use/agricultural management strategies to improve the sustainable utilization of arable land.

Suggested Citation

  • Nan Ke & Xupeng Zhang & Xinhai Lu & Bing Kuang & Bin Jiang, 2022. "Regional Disparities and Influencing Factors of Eco-Efficiency of Arable Land Utilization in China," Land, MDPI, vol. 11(2), pages 1-17, February.
    • Handle: RePEc:gam:jlands:v:11:y:2022:i:2:p:257-:d:744810
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    References listed on IDEAS

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    2. Yuling Wu & Pei Zhang & Jia Li & Jiao Hou, 2022. "Spatial Distribution Evolution and Optimization Path of Eco-Efficiency of Cultivated Land Use: A Case Study of Hubei Province, China," Sustainability, MDPI, vol. 14(18), pages 1-18, September.
    3. Min Zhou & Hanxiaoxue Sun & Nan Ke, 2022. "The Spatial and Temporal Evolution of Coordination Degree Concerning China’s Cultivated Land Green Utilization Efficiency and High-Quality Agricultural Development," Land, MDPI, vol. 12(1), pages 1-21, December.
    4. Jie Li & Zhengchuan Sun & Jie Zhou & Yaya Sow & Xufeng Cui & Haipeng Chen & Qianling Shen, 2023. "The Impact of the Digital Economy on Carbon Emissions from Cultivated Land Use," Land, MDPI, vol. 12(3), pages 1-18, March.
    5. Shuting Liu & Junsong Jia & Hanzhi Huang & Dilan Chen & Yexi Zhong & Yangming Zhou, 2023. "China’s CO 2 Emissions: A Thorough Analysis of Spatiotemporal Characteristics and Sustainable Policy from the Agricultural Land-Use Perspective during 1995–2020," Land, MDPI, vol. 12(6), pages 1-20, June.
    6. Mengna Li & Li Tan & Xi Yang, 2023. "The Impact of Environmental Regulation on Cultivated Land Use Eco-Efficiency: Evidence from China," Agriculture, MDPI, vol. 13(9), pages 1-20, August.
    7. Min Zhou & Hua Zhang & Nan Ke, 2022. "Cultivated Land Transfer, Management Scale, and Cultivated Land Green Utilization Efficiency in China: Based on Intermediary and Threshold Models," IJERPH, MDPI, vol. 19(19), pages 1-20, October.
    8. Yajuan Wang & Xi Wu & Hongbo Zhu, 2022. "Spatio-Temporal Pattern and Spatial Disequilibrium of Cultivated Land Use Efficiency in China: An Empirical Study Based on 342 Prefecture-Level Cities," Land, MDPI, vol. 11(10), pages 1-15, October.
    9. Min Zhou & Hua Zhang & Zixuan Zhang & Hanxiaoxue Sun, 2023. "Digital Financial Inclusion, Cultivated Land Transfer and Cultivated Land Green Utilization Efficiency: An Empirical Study from China," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    10. Mingtao Yan & Jianji Zhao & Jiajun Qiao & Dong Han & Qiankun Zhu & Yang Yang & Qi Liu & Zhipeng Wang, 2023. "Spatial Pattern Evolution and Influencing Factors on Agricultural Non-Point Source Pollution in Small Town Areas under the Background of Rapid Industrialization," IJERPH, MDPI, vol. 20(3), pages 1-19, February.
    11. Houtian Tang & Yuanlai Wu & Jinxiu Chen & Liuxin Deng & Minjie Zeng, 2022. "How Does Change in Rural Residential Land Affect Cultivated Land Use Efficiency? An Empirical Study Based on 42 Cities in the Middle Reaches of the Yangtze River," Land, MDPI, vol. 11(12), pages 1-20, December.

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