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

doi:10.1007/s11442-020-1754-3

Abstract

Abstract:

Climate change resulting from CO₂ emissions has become an important global environmental issue in recent years. Improving carbon emission performance is one way to reduce carbon emissions. Although carbon emission performance has been discussed at the national and industrial levels, city-level studies are lacking due to the limited availability of statistics on energy consumption. In this study, based on city-level remote sensing data on carbon emissions in China from 1992-2013, we used the slacks-based measure of super-efficiency to evaluate urban carbon emission performance. The traditional Markov probability transfer matrix and spatial Markov probability transfer matrix were constructed to explore the spatiotemporal evolution of urban carbon emission performance in China for the first time and predict long-term trends in carbon emission performance. The results show that urban carbon emission performance in China steadily increased during the study period with some fluctuations. However, the overall level of carbon emission performance remains low, indicating great potential for improvements in energy conservation and emission reduction. The spatial pattern of urban carbon emission performance in China can be described as “high in the south and low in the north,” and significant differences in carbon emission performance were found between cities. The spatial Markov probabilistic transfer matrix results indicate that the transfer of carbon emission performance in Chinese cities is stable, resulting in a “club convergence” phenomenon. Furthermore, neighborhood backgrounds play an important role in the transfer between carbon emission performance types. Based on the prediction of long-term trends in carbon emission performance, carbon emission performance is expected to improve gradually over time. Therefore, China should continue to strengthen research and development aimed at improving urban carbon emission performance and achieving the national energy conservation and emission reduction goals. Meanwhile, neighboring cities with different neighborhood backgrounds should pursue cooperative economic strategies that balance economic growth, energy conservation, and emission reductions to realize low-carbon construction and sustainable development.

Key words: urban carbon emission performance, super-efficiency SBM model, spatial Markov chain, spatiotemporal patterns, trend prediction, China

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.

Figures/Tables 10

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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

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	P ₄₃	P₄₄

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

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

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