Journal of Geographical Sciences ›› 2020, Vol. 30 ›› Issue (5): 757-774.doi: 10.1007/s11442-020-1754-3
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WANG Shaojian1, GAO Shuang1, HUANG Yongyuan2, SHI Chenyi1
Received:
2020-01-06
Accepted:
2020-03-10
Online:
2020-05-25
Published:
2020-07-25
About author:
Wang Shaojian (1986?), Associate Professor, specialized in urban geography and regional development. E-mail: 1987wangshaojian@163.com
Supported by:
WANG Shaojian, GAO Shuang, HUANG Yongyuan, SHI Chenyi. Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends[J].Journal of Geographical Sciences, 2020, 30(5): 757-774.
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Table 1
System of input-output indicators for carbon emission performance"
Indicator | Variable | Unit | Mean | Min | Max | S.D. |
---|---|---|---|---|---|---|
Input | Fixed-asset investment | 108 yuan | 42.65 | 12.95 | 836.24 | 66.34 |
Number of employees | 104 person | 220.36 | 0.32 | 1729.55 | 169.70 | |
Electricity consumption | 104 kwh | 680.87 | 0.25 | 8514.69 | 907.31 | |
Expected output | GDP | 108 yuan | 103.97 | 2.96 | 1483.55 | 125.46 |
Non-expected output | CO2 emissions | 104 t | 1665.89 | 0.62 | 20832.94 | 2219.91 |
Table 3
Spatial Markov transfer probability matrix (k = 4)"
Lag | t/t+1 | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|
1 | 1 | P11|1 | P12|1 | P13|1 | P14|1 |
2 | P21|1 | P22|1 | P23|1 | P24|1 | |
3 | P31|1 | P32|1 | P33|1 | P34|1 | |
4 | P41|1 | P42|1 | P43|1 | P44|1 | |
2 | 1 | P11|2 | P12|2 | P13|2 | P14|2 |
2 | P21|2 | P22|2 | P23|2 | P24|2 | |
3 | P31|2 | P32|2 | P33|2 | P34|2 | |
4 | P41|2 | P42|2 | P43|2 | P44|2 | |
3 | 1 | P11|3 | P12|3 | P13|3 | P14|3 |
2 | P21|3 | P22|3 | P23|3 | P24|3 | |
3 | P31|3 | P32|3 | P33|3 | P34|3 | |
4 | P41|3 | P42|3 | P43|3 | P44|3 | |
4 | 1 | P11|4 | P12|4 | P13|4 | P14|4 |
2 | P21|4 | P22|4 | P23|4 | P24|4 | |
3 | P31|4 | P32|4 | P33|4 | P34|4 | |
4 | P41|4 | P42|4 | P43|4 | P44|4 |
Table 5
Spatial Markov matrix of city-level carbon emission performance in China from 1992-2013"
Lag | t/t+1 | n | 1 | 2 | 3 | 4 |
---|---|---|---|---|---|---|
1 | 1 | 807 | 0.7720 | 0.1437 | 0.0595 | 0.0248 |
2 | 313 | 0.1565 | 0.6006 | 0.1821 | 0.0607 | |
3 | 206 | 0.0388 | 0.2330 | 0.5631 | 0.1650 | |
4 | 176 | 0.0341 | 0.0455 | 0.1364 | 0.7841 | |
2 | 1 | 470 | 0.7319 | 0.2000 | 0.0553 | 0.0128 |
2 | 436 | 0.1124 | 0.6651 | 0.1789 | 0.0436 | |
3 | 321 | 0.0218 | 0.2274 | 0.5919 | 0.1589 | |
4 | 256 | 0.0430 | 0.0313 | 0.1953 | 0.7305 | |
3 | 1 | 182 | 0.6923 | 0.2253 | 0.0495 | 0.0330 |
2 | 440 | 0.0750 | 0.6841 | 0.2045 | 0.0364 | |
3 | 475 | 0.0147 | 0.1537 | 0.6505 | 0.1811 | |
4 | 371 | 0.0054 | 0.0296 | 0.2075 | 0.7574 | |
4 | 1 | 55 | 0.6000 | 0.3273 | 0.0727 | 0.0000 |
2 | 268 | 0.0709 | 0.6828 | 0.2090 | 0.0373 | |
3 | 470 | 0.0085 | 0.1489 | 0.6872 | 0.1553 | |
4 | 697 | 0.0014 | 0.0158 | 0.1277 | 0.8551 |
Table 6
Predicted evolution in carbon emission performance in Chinese cities"
State type | 1 | 2 | 3 | 4 | ||
---|---|---|---|---|---|---|
Ignoring spatial lag | Initial state | 0.1484 | 0.3534 | 0.3004 | 0.1979 | |
Ultimate state | 0.1377 | 0.2512 | 0.2948 | 0.3162 | ||
Considering spatial lag | Ultimate state | 1 | 0.2521 | 0.2524 | 0.2242 | 0.2713 |
2 | 0.1908 | 0.3184 | 0.2708 | 0.2201 | ||
3 | 0.0851 | 0.2585 | 0.3471 | 0.3093 | ||
4 | 0.0477 | 0.2220 | 0.3249 | 0.4054 |
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