基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测

doi:10.11821/dlxb202006016

地理学报 ›› 2020, Vol. 75 ›› Issue (6): 1316-1330.doi: 10.11821/dlxb202006016

• 城乡研究与区域发展 • 上一篇

基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测

王少剑¹, 高爽¹, 黄永源², 史晨怡¹

1.中山大学地理科学与规划学院广东省城市化与地理环境空间模拟重点实验室,广州 510275;
2.北京大学城市与环境学院,北京 100871

收稿日期:2019-04-25 修回日期:2020-03-12 出版日期:2020-06-25 发布日期:2020-08-25
作者简介:王少剑(1986-), 男, 河南驻马店人, 博士, 副教授, 博士生导师, 中国地理学会会员(S110011019M), 研究方向为城市地理、城市与区域规划。E-mail: 1987wangshaojian@163.com
基金资助:
中央高校基本科研业务青年教师重点培育项目(19lgzd09);广东省特支计划;广州市珠江科技新星(201806010187)

Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model

WANG Shaojian¹, GAO Shuang¹, HUANG Yongyuan², SHI Chenyi¹

1. Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation, School of Geography and Planning, Sun Yat-sen University, Guangzhou 510275, China;
2. College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

Received:2019-04-25 Revised:2020-03-12 Online:2020-06-25 Published:2020-08-25
Supported by:
Fundamental Research Funds for the Central Universities(19lgzd09);Guangdong Special Support Program;Pearl River S&T Nova Program of Guangzhou(201806010187)

摘要/Abstract

摘要：

由CO₂排放所引起的气候变化是当今社会所关注的热点话题,提高碳排放绩效是碳减排的重要途径。目前关于碳排放绩效的研究多从国家尺度和行业尺度进行探讨,由于能源消耗统计数据有限,缺乏城市尺度的研究。基于遥感模拟反演的1992—2013年中国各城市碳排放数据,采用超效率SBM模型对城市碳排放绩效进行测定,构建马尔可夫和空间马尔可夫概率转移矩阵,首次从城市尺度探讨了中国碳排放绩效的时空动态演变特征,并预测其长期演变的趋势。研究表明,中国城市碳排放绩效均值呈现波动中稳定上升的趋势,但整体仍处于较低的水平,未来城市碳排放绩效仍具有较大的提升空间,节能减排潜力大;全国城市碳排放绩效空间格局呈现“南高北低”特征,城市间碳排放绩效水平的差异性显著;空间马尔科夫概率转移矩阵结果显示,中国城市碳排放绩效类型转移具有稳定性,且存在“俱乐部收敛”现象,地理背景在中国城市碳排放绩效类型转移过程中发挥重要作用;从长期演变的趋势预测来看,中国碳排放绩效未来演变较为乐观,碳排放绩效随时间的推移而逐步提升,碳排放绩效分布呈现向高值集中的趋势。因此未来中国应继续加大节能减排力度以提高城市碳排放绩效,实现国家节能减排目标;同时不同地理背景的邻域城市之间应建立完善的经济合作联动机制,以此提升城市碳排放绩效水平并追求经济增长与节能减排之间协调发展,从而实现低碳城市建设和可持续发展。

关键词: 城市碳排放绩效, 超效率SBM模型, 空间马尔可夫链, 时空演变, 趋势预测, 中国

Abstract:

Climate change caused by CO₂ emissions has become an environmental issue globally in recent years, and improving carbon emission performance is an important way to reduce carbon emissions. Although some scholars have discussed the carbon emission performance at the national scale and industry level, literature lacks studies at the city- level due to a limited availability of statistics on energy consumptions. In this study, based on China's city-level remote sensing carbon emissions from 1992 to 2013, we used the super-efficiency SBM model to measure the urban carbon emission performance, and the traditional Markov probability transfer matrix and spatial Markov probability transfer matrix are constructed to explore the spatio-temporal dynamic evolution characteristics of urban carbon emission performance in China for the first time and to predict its long-term evolution trend. The study shows that urban carbon emission performance in China presents a trend of steady increase in the fluctuation, but the overall level is still at a low level, so there is still a great improvement space in urban carbon emission performance, with huge potential for energy conservation and emission reduction. The spatial pattern of national urban carbon emission performance shows the characteristics of "high in the south and low in the north", and there is a significant difference in the level of carbon emission performance between cities. The spatial Markov probabilistic transfer matrix results show that the transfer of carbon emission performance type in Chinese cities is stable, thus it forms the "club convergence" phenomenon, and the geographical background plays an important role in the process of the transfer. From the perspective of long-term trend prediction, the future evolution of urban carbon emission performance in China is relatively optimistic. The carbon emission performance will gradually improve over time, and the distribution of carbon emission performance presents a trend of high concentration. Therefore, in the future, China should continue to strengthen research and development to improve the performance level of urban carbon emissions and achieve the national target of energy conservation and emission reduction. At the same time, neighboring cities with different geographical backgrounds should establish a sound linkage mechanism of economic cooperation to pursue coordinated development between economic growth, energy conservation and emission reduction, so as to realize low-carbon city construction and sustainable development.

Key words: urban carbon emission performance, super-efficiency SBM model, spatial Markov chain, spatio-temporal evolution, trend prediction, China

王少剑, 高爽, 黄永源, 史晨怡. 基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测[J]. 地理学报, 2020, 75(6): 1316-1330.

WANG Shaojian, GAO Shuang, HUANG Yongyuan, SHI Chenyi. Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model[J]. Acta Geographica Sinica, 2020, 75(6): 1316-1330.

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指标	变量	单位	平均值	中间值	标准差	最小值	最大值
投入指标	固定资产投资	10⁸元	42.65	22.50	66.34	12.95	836.24
	从业人员	10⁴人	220.36	183.02	169.70	0.32	1729.55
	电力消费	10⁴kwh	680.87	366.25	907.31	0.25	8514.69
期望产出	GDP	10⁸元	103.97	71.34	125.46	2.96	1483.55
非期望产出	CO₂排放	10⁴ t	1665.89	896.10	2219.91	0.62	20832.94

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

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

邻域类型	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

基于超效率SBM模型的中国城市碳排放绩效时空演变格局及预测

Spatio-temporal evolution and trend prediction of urban carbon emission performance in China based on super-efficiency SBM model

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参考文献 53

相关文章 15

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本文评价

状态类型			1	2	3	4
不考虑空间滞后	初始状态		0.1484	0.3534	0.3004	0.1979
不考虑空间滞后	终极状态		0.1377	0.2512	0.2948	0.3162
考虑空间滞后	终极状态	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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