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

Journals >Journal of Geographical Sciences >Volume 30 >Issue 5 >Page 757 > Article

Journal of Geographical Sciences
Vol. 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

Author Affiliations

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

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DOI: 10.1007/s11442-020-1754-3 Cite this Article

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 Copy Citation Text

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

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

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

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

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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 1.

System of input-output indicators for carbon emission performance

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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 2.

Markov transfer probability matrix (k = 4)

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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 3.

Spatial Markov transfer probability matrix (k = 4)

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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 4.

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

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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 5.

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

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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