Journal of Geographical Sciences, Volume. 30, Issue 4, 535(2020)

Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints

Xiang LUO1... Xinhe AO1, Zuo ZHANG1, Qing WAN2,* and Xingjian LIU3 |Show fewer author(s)
Author Affiliations
  • 1College of Public Administration, Central China Normal University, Wuhan 430079, China
  • 2School of Management, Wuhan Institute of Technology, Wuhan 430205, China
  • 3Department of Urban Planning and Design, University of Hong Kong, Hong Kong 999077, China
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    Figures & Tables(8)
    Geographical location of the Yangtze River Economic Belt (YREB)
    Spatiotemporal variations of cultivated CEs in the Yangtze River Economic Belt
    Kernel density map of CLU efficiency (CLUE) in the Yangtze River Economic Belt
    • Table 1.

      Carbon emission (CE) coefficients of major carbon sources arising from cultivated land use (CLU)

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      Table 1.

      Carbon emission (CE) coefficients of major carbon sources arising from cultivated land use (CLU)

      SourceCoefficientUnitReference
      Tillage312.6kg/km2Wu et al., 2007
      Machinery0.18kg/kWWest et al., 2002
      Fertilizers0.8956kg/kgWest et al., 2002
      Pesticides4.9341kg/kgPost et al., 2000
      Plastic sheets5.18kg/kgLi et al., 2011
      Irrigation25kg/hm2Li et al., 2011
    • Table 2.

      Indicators and data sources

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      Table 2.

      Indicators and data sources

      IndicatorsData sources
      InputI1China Rural Statistical Yearbook (2008-2017)
      I2China Rural Statistical Yearbook (2008-2017)
      I3China Rural Statistical Yearbook (2008-2017)
      I4China Rural Statistical Yearbook (2008-2017)
      I5China Rural Statistical Yearbook (2008-2017)
      I6China Rural Statistical Yearbook (2008-2017)
      I7China Rural Statistical Yearbook (2008-2017)
      OutputO1China Rural Statistical Yearbook (2008-2017)
      O2China Rural Statistical Yearbook (2008-2017)
      O3$E=\sum{{{E}_{i}}}=\sum{{{T}_{i}}\cdot {{\delta }_{i}}}$, where Ti and δi are the values of each carbon source and the CE coefficient, respectively.
      Influencing factorsPCLand Survey Results Sharing Application Service Platform
      PGStatistical yearbooks of the provinces and cities in the YREB from 2008 to 2017
      PPStatistical yearbooks of the provinces and cities in the YREB from 2008 to 2017
      MPStatistical yearbooks of the provinces and cities in the YREB from 2008 to 2017
      ATEPS data platform
      PIChina Environmental Protection Database
    • Table 3.

      CEs from CLU in the Yangtze River Economic Belt

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      Table 3.

      CEs from CLU in the Yangtze River Economic Belt

      RegionsYearAverage
      2007200820092010201120122013201420152016
      Shanghai28.2928.5526.0925.6325.1123.3622.8222.0220.9819.6024.24
      Jiangsu408.22408.29415.04414.50412.49409.01405.91404.10396.96389.90406.44
      Zhejiang145.38147.53149.13148.23149.11150.71151.63147.96145.89139.67147.52
      Anhui374.26379.77386.80398.38410.56416.34425.00426.57423.85410.36405.19
      Jiangxi189.09195.18199.31206.38207.07209.46209.83209.38210.11207.22204.30
      Hubei373.63401.05413.84425.33429.67429.97422.50420.44406.07397.02411.95
      Hunan295.49301.53311.39318.65325.22335.36337.70337.73336.21334.77323.40
      Chongqing103.84108.08113.40114.42119.03119.84120.78121.96122.94121.38116.57
      Sichuan304.20310.62319.02321.84328.94332.38330.97331.29331.91331.06324.23
      Yunnan201.27216.21222.93238.92257.23274.89285.33296.04302.46307.35260.26
      Huizhou99.88109.56112.01107.26117.48122.94122.89127.33130.09130.90118.03
      Total2523.542606.352668.972719.562781.902824.262835.342844.842827.472789.222742.14
    • Table 4.

      CLUE for each province or city of the Yangtze River Economic Belt in specific years

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      Table 4.

      CLUE for each province or city of the Yangtze River Economic Belt in specific years

      Region2007201020132016
      CCRSBMCCRSBMCCRSBMCCRSBM
      Shanghai0.91350.67280.98440.88531111
      Jiangsu110.98220.79220.99860.933811
      Zhejiang0.69570.41850.79970.57490.93210.779811
      Anhui0.90060.58600.91130.64190.89740.66330.95270.7483
      Jiangxi110.96530.79871111
      Hubei0.89220.56890.90170.57170.95760.758611
      Hunan0.94870.73180.94820.77200.94010.773811
      Chongqing0.99050.92670.98240.90870.99740.931911
      Sichuan110.97010.88810.97840.937111
      Yunnan0.71850.49930.65190.47530.74410.54310.78210.5461
      Huizhou110.98190.85950.86720.714111
    • Table 5.

      Regression results of CLUE using the Tobit model

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      Table 5.

      Regression results of CLUE using the Tobit model

      VariablesCoefficientStd. Err.ZSignificance
      PC-0.00044090.0001838-2.40.016
      PG3.14E-061.08E-062.90.004
      PP0.00007040.00002792.520.012
      MP-0.02083010.0070656-2.950.003
      AT0.05732150.02584762.220.027
      PI-1.16E-060.0000392-0.030.976
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    Xiang LUO, Xinhe AO, Zuo ZHANG, Qing WAN, Xingjian LIU. Spatiotemporal variations of cultivated land use efficiency in the Yangtze River Economic Belt based on carbon emission constraints[J]. Journal of Geographical Sciences, 2020, 30(4): 535

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    Paper Information

    Received: Apr. 25, 2019

    Accepted: Dec. 29, 2019

    Published Online: Sep. 30, 2020

    The Author Email: WAN Qing (wanqing1989@126.com)

    DOI:10.1007/s11442-020-1741-8

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