Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Shaojian WANG; Shuang GAO; Yongyuan HUANG; Chenyi SHI

doi:10.1007/s11442-020-1754-3

Journal of Geographical Sciences, Volume. 30, Issue 5, 757(2020)

Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends

Shaojian WANG^1、*, Shuang GAO¹, Yongyuan HUANG², and Chenyi SHI¹

¹Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China

²College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

show less

Figures & Tables(10)

Fig. 1. Effects of different factors on urban carbon emission performance from an input-output perspective

Download full size

View in Article

Fig. 2. Evolution in urban carbon emission performance from 1992-2013

Download full size

View in Article

Fig. 3. Box plot of urban carbon emission performance in Chinese cities from 1992 to 2013

Download full size

View in Article

Fig. 4. Spatial distributions of urban carbon emission performance in Chinese cities from 1992-2013

Download full size

View in Article

Table 1.
System of input-output indicators for carbon emission performance

View table

View in Article

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 2.
Markov transfer probability matrix (k = 4)
View table
View in Article
Table 2.
Markov transfer probability matrix (k = 4)
t/t+1 1 2 3 4
1 P₁₁ P₁₂ P₁₃ P₁₄
2 P₂₁ P₂₂ P₂₃ P₂₄
3 P₃₁ P₃₂ P₃₃ P₃₄
4 P₄₁ P₄₂ P₄₃ P₄₄

Table 3.
Spatial Markov transfer probability matrix (k = 4)

View table

View in Article

Table 3.
Spatial Markov transfer probability matrix (k = 4)

Lag	t/t+1	1	2	3	4
1	1	P_11\|1	P_12\|1	P_13\|1	P_14\|1
	2	P_21\|1	P_22\|1	P_23\|1	P_24\|1
	3	P_31\|1	P_32\|1	P_33\|1	P_34\|1
	4	P_41\|1	P_42\|1	P_43\|1	P_44\|1
2	1	P_11\|2	P_12\|2	P_13\|2	P_14\|2
	2	P_21\|2	P_22\|2	P_23\|2	P_24\|2
	3	P_31\|2	P_32\|2	P_33\|2	P_34\|2
	4	P_41\|2	P_42\|2	P_43\|2	P_44\|2
3	1	P_11\|3	P_12\|3	P_13\|3	P_14\|3
	2	P_21\|3	P_22\|3	P_23\|3	P_24\|3
	3	P_31\|3	P_32\|3	P_33\|3	P_34\|3
	4	P_41\|3	P_42\|3	P_43\|3	P_44\|3
4	1	P_11\|4	P_12\|4	P_13\|4	P_14\|4
	2	P_21\|4	P_22\|4	P_23\|4	P_24\|4
	3	P_31\|4	P_32\|4	P_33\|4	P_34\|4
	4	P_41\|4	P_42\|4	P_43\|4	P_44\|4

Table 4.
Markov matrix of city-level carbon emission performance types from 1992-2013
View table
View in Article
Table 4.
Markov matrix of city-level carbon emission performance types from 1992-2013
t/t+1 n 1 2 3 4
1 1514 0.7437 0.1777 0.0575 0.0211
2 1457 0.1030 0.6603 0.1929 0.0439
3 1472 0.0177 0.1793 0.6372 0.1658
4 1500 0.0133 0.0253 0.1600 0.8013

Table 5.
Spatial Markov matrix of city-level carbon emission performance in China from 1992-2013

View table

View in Article

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

View table

View in Article

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
Ignoring spatial lag	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

Tools

Get Citation

Copy Citation Text

Shaojian WANG, Shuang GAO, Yongyuan HUANG, Chenyi SHI. Spatiotemporal evolution of urban carbon emission performance in China and prediction of future trends[J]. Journal of Geographical Sciences, 2020, 30(5): 757

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites

Paper Information

Category: Research Articles

Received: Jan. 6, 2020

Accepted: Mar. 10, 2020

Published Online: Sep. 30, 2020

The Author Email:

DOI:10.1007/s11442-020-1754-3

Topics

Resource Economy

Research Articles

Table 1. System of input-output indicators for carbon emission performance

Table 1. System of input-output indicators for carbon emission performance

Table 2. Markov transfer probability matrix (k = 4)

Table 2. Markov transfer probability matrix (k = 4)

Table 3. Spatial Markov transfer probability matrix (k = 4)

Table 3. Spatial Markov transfer probability matrix (k = 4)

Table 4. Markov matrix of city-level carbon emission performance types from 1992-2013

Table 4. Markov matrix of city-level carbon emission performance types from 1992-2013

Table 5. Spatial Markov matrix of city-level carbon emission performance in China from 1992-2013

Table 5. Spatial Markov matrix of city-level carbon emission performance in China from 1992-2013

Table 6. Predicted evolution in carbon emission performance in Chinese cities

Table 6. Predicted evolution in carbon emission performance in Chinese cities

Table 1.
System of input-output indicators for carbon emission performance

Table 1.
System of input-output indicators for carbon emission performance

Table 2.
Markov transfer probability matrix (k = 4)

Table 2.
Markov transfer probability matrix (k = 4)

Table 3.
Spatial Markov transfer probability matrix (k = 4)

Table 3.
Spatial Markov transfer probability matrix (k = 4)

Table 4.
Markov matrix of city-level carbon emission performance types from 1992-2013

Table 4.
Markov matrix of city-level carbon emission performance types from 1992-2013

Table 5.
Spatial Markov matrix of city-level carbon emission performance in China from 1992-2013

Table 5.
Spatial Markov matrix of city-level carbon emission performance in China from 1992-2013

Table 6.
Predicted evolution in carbon emission performance in Chinese cities

Table 6.
Predicted evolution in carbon emission performance in Chinese cities