Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (4): 535-552.doi: 10.1007/s11442-020-1741-8
• Special Issue: Development and Protection of Territorial Space in the Yangtze River Economic Belt • Previous Articles Next Articles
LUO Xiang1, AO Xinhe1, ZHANG Zuo1, WAN Qing2,*(), LIU Xingjian3
Received:
2019-04-25
Accepted:
2019-12-29
Online:
2020-04-25
Published:
2020-06-25
Contact:
WAN Qing
E-mail:wanqing1989@126.com
About author:
Luo Xiang (1978-), Associate Professor, specialized in regional economics and development of economics. E-mail: philiplaw@163.com
Supported by:
LUO Xiang, AO Xinhe, ZHANG Zuo, WAN Qing, LIU Xingjian. 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): 535-552.
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Table 1
Carbon emission (CE) coefficients of major carbon sources arising from cultivated land use (CLU)"
Source | Coefficient | Unit | Reference |
---|---|---|---|
Tillage | 312.6 | kg/km2 | |
Machinery | 0.18 | kg/kW | |
Fertilizers | 0.8956 | kg/kg | |
Pesticides | 4.9341 | kg/kg | |
Plastic sheets | 5.18 | kg/kg | |
Irrigation | 25 | kg/hm2 |
Table 2
Indicators and data sources"
Indicators | Data sources | |
---|---|---|
Input | I1 | China Rural Statistical Yearbook (2008-2017) |
I2 | China Rural Statistical Yearbook (2008-2017) | |
I3 | China Rural Statistical Yearbook (2008-2017) | |
I4 | China Rural Statistical Yearbook (2008-2017) | |
I5 | China Rural Statistical Yearbook (2008-2017) | |
I6 | China Rural Statistical Yearbook (2008-2017) | |
I7 | China Rural Statistical Yearbook (2008-2017) | |
Output | O1 | China Rural Statistical Yearbook (2008-2017) |
O2 | China 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 factors | PC | Land Survey Results Sharing Application Service Platform |
PG | Statistical yearbooks of the provinces and cities in the YREB from 2008 to 2017 | |
PP | Statistical yearbooks of the provinces and cities in the YREB from 2008 to 2017 | |
MP | Statistical yearbooks of the provinces and cities in the YREB from 2008 to 2017 | |
AT | EPS data platform | |
PI | China Environmental Protection Database |
Table 3
CEs from CLU in the Yangtze River Economic Belt"
Regions | Year | Average | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|
2007 | 2008 | 2009 | 2010 | 2011 | 2012 | 2013 | 2014 | 2015 | 2016 | ||
Shanghai | 28.29 | 28.55 | 26.09 | 25.63 | 25.11 | 23.36 | 22.82 | 22.02 | 20.98 | 19.60 | 24.24 |
Jiangsu | 408.22 | 408.29 | 415.04 | 414.50 | 412.49 | 409.01 | 405.91 | 404.10 | 396.96 | 389.90 | 406.44 |
Zhejiang | 145.38 | 147.53 | 149.13 | 148.23 | 149.11 | 150.71 | 151.63 | 147.96 | 145.89 | 139.67 | 147.52 |
Anhui | 374.26 | 379.77 | 386.80 | 398.38 | 410.56 | 416.34 | 425.00 | 426.57 | 423.85 | 410.36 | 405.19 |
Jiangxi | 189.09 | 195.18 | 199.31 | 206.38 | 207.07 | 209.46 | 209.83 | 209.38 | 210.11 | 207.22 | 204.30 |
Hubei | 373.63 | 401.05 | 413.84 | 425.33 | 429.67 | 429.97 | 422.50 | 420.44 | 406.07 | 397.02 | 411.95 |
Hunan | 295.49 | 301.53 | 311.39 | 318.65 | 325.22 | 335.36 | 337.70 | 337.73 | 336.21 | 334.77 | 323.40 |
Chongqing | 103.84 | 108.08 | 113.40 | 114.42 | 119.03 | 119.84 | 120.78 | 121.96 | 122.94 | 121.38 | 116.57 |
Sichuan | 304.20 | 310.62 | 319.02 | 321.84 | 328.94 | 332.38 | 330.97 | 331.29 | 331.91 | 331.06 | 324.23 |
Yunnan | 201.27 | 216.21 | 222.93 | 238.92 | 257.23 | 274.89 | 285.33 | 296.04 | 302.46 | 307.35 | 260.26 |
Huizhou | 99.88 | 109.56 | 112.01 | 107.26 | 117.48 | 122.94 | 122.89 | 127.33 | 130.09 | 130.90 | 118.03 |
Total | 2523.54 | 2606.35 | 2668.97 | 2719.56 | 2781.90 | 2824.26 | 2835.34 | 2844.84 | 2827.47 | 2789.22 | 2742.14 |
Table 4
CLUE for each province or city of the Yangtze River Economic Belt in specific years"
Region | 2007 | 2010 | 2013 | 2016 | ||||
---|---|---|---|---|---|---|---|---|
CCR | SBM | CCR | SBM | CCR | SBM | CCR | SBM | |
Shanghai | 0.9135 | 0.6728 | 0.9844 | 0.8853 | 1 | 1 | 1 | 1 |
Jiangsu | 1 | 1 | 0.9822 | 0.7922 | 0.9986 | 0.9338 | 1 | 1 |
Zhejiang | 0.6957 | 0.4185 | 0.7997 | 0.5749 | 0.9321 | 0.7798 | 1 | 1 |
Anhui | 0.9006 | 0.5860 | 0.9113 | 0.6419 | 0.8974 | 0.6633 | 0.9527 | 0.7483 |
Jiangxi | 1 | 1 | 0.9653 | 0.7987 | 1 | 1 | 1 | 1 |
Hubei | 0.8922 | 0.5689 | 0.9017 | 0.5717 | 0.9576 | 0.7586 | 1 | 1 |
Hunan | 0.9487 | 0.7318 | 0.9482 | 0.7720 | 0.9401 | 0.7738 | 1 | 1 |
Chongqing | 0.9905 | 0.9267 | 0.9824 | 0.9087 | 0.9974 | 0.9319 | 1 | 1 |
Sichuan | 1 | 1 | 0.9701 | 0.8881 | 0.9784 | 0.9371 | 1 | 1 |
Yunnan | 0.7185 | 0.4993 | 0.6519 | 0.4753 | 0.7441 | 0.5431 | 0.7821 | 0.5461 |
Huizhou | 1 | 1 | 0.9819 | 0.8595 | 0.8672 | 0.7141 | 1 | 1 |
Table 5
Regression results of CLUE using the Tobit model"
Variables | Coefficient | Std. Err. | Z | Significance |
---|---|---|---|---|
PC | -0.0004409 | 0.0001838 | -2.4 | 0.016 |
PG | 3.14E-06 | 1.08E-06 | 2.9 | 0.004 |
PP | 0.0000704 | 0.0000279 | 2.52 | 0.012 |
MP | -0.0208301 | 0.0070656 | -2.95 | 0.003 |
AT | 0.0573215 | 0.0258476 | 2.22 | 0.027 |
PI | -1.16E-06 | 0.0000392 | -0.03 | 0.976 |
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