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 Published:2020-06-25 Online: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.

图/表 10

图1

表1

表2

表3

图2

图3

图4

表4

表5

表6

参考文献 53

[1]	Su Yongxian, Chen Xiuzhi, Ye Yuyao, et al. The characteristics and mechanisms of carbon emissions from energy consumption in China using DMSP/OLS night light imageries. Acta Geographica Sinica, 2013,68(11):1513-1526. doi: 10.11821/dlxb201311007
	[ 苏泳娴, 陈修治, 叶玉瑶, 等. 基于夜间灯光数据的中国能源消费碳排放特征及机理. 地理学报, 2013,68(11):1513-1526.] doi: 10.11821/dlxb201311007
[2]	Liu Q, Wang S, Zhang W, et al. Examining the effects of income inequality on CO₂ emissions: Evidence from non-spatial and spatial perspectives. Applied Energy, 2019,236:163-171. doi: 10.1016/j.apenergy.2018.11.082
[3]	Deng Jixiang, Liu Xiao, Wang Zheng. Characteristics analysis and factor decomposition based on the regional difference changes in China's CO₂ emission. Journal of Natural Resources, 2014,29(2):189-200.
	[ 邓吉祥, 刘晓, 王铮. 中国碳排放的区域差异及演变特征分析与因素分解. 自然资源学报, 2014,29(2):189-200.]
[4]	Song Jiekun, Liang Lulu, Niu Danping, et al. Evaluation, influencing factors and promotion countermeasures of cities' carbon emissions efficiencies in Shandong province. Journal of China University of Petroleum (Edition of Social Sciences), 2018,34(1):15-21.
	[ 宋杰鲲, 梁璐璐, 牛丹平, 等. 山东省地市碳排放效率测度、影响因素与提升对策. 中国石油大学学报(社会科学版), 2018,34(1):15-21.]
[5]	Ishii S, Tabushi S, Aramaki T, et al. Impact of future urban form on the potential to reduce greenhouse gas emissions from residential, commercial and public buildings in Utsunomiya, Japan. Energy Policy, 2010,38(9):4888-4896. doi: 10.1016/j.enpol.2009.08.022
[6]	Liu Zhu, Geng Yong, Xue Bing, et al. A calculation method of CO₂ emission from urban energy consumption. Resources Science, 2011,33(7):1325-1330.
	[ 刘竹, 耿涌, 薛冰, 等. 城市能源消费碳排放核算方法. 资源科学, 2011,33(7):1325-1330.]
[7]	Zhao Rongqin, Huang Xianjin, Peng Buzhuo. Research on carbon cycle and carbon balance of Nanjing urban system. Acta Geographica Sinica, 2012,67(6):758-770. doi: 10.11821/xb201206004
	[ 赵荣钦, 黄贤金, 彭补拙. 南京城市系统碳循环与碳平衡分析. 地理学报, 2012,67(6):758-770.] doi: 10.11821/xb201206004
[8]	Xu Shichun, Xi Rong, He Zhengxia. Influential factors and policy implications of carbon emissions for energy consumption in China. Resources Science, 2012,34(1):2-12.
	[ 许士春, 习蓉, 何正霞. 中国能源消耗碳排放的影响因素分析及政策启示. 资源科学, 2012,34(1):2-12.]
[9]	Xu Guoquan, Liu Zeyuan, Jiang Zhaohua. Decomposition model and empirical study of carbon emissions for China, 1995-2004. China Population, Resources and Eevironment, 2006,16(6):158-161.
	[ 徐国泉, 刘则渊, 姜照华. 中国碳排放的因素分解模型及实证分析: 1995—2004. 中国人口·资源与环境, 2006,16(6):158-161.]
[10]	Ping W, Wu W, Zhu B, et al. Examining the impact factors of energy-related CO₂ emissions using the STIRPAT model in Guangdong Province, China. Applied Energy, 2013,106(11):65-71. doi: 10.1016/j.apenergy.2013.01.036
[11]	Zhao Rongqin, Huang Xianjin, Zhong Taiyang. Research on carbon emission intensity and carbon footprint of different industrial spaces in China. Acta Geographica Sinica, 2010,65(9):1048-1057. doi: 10.11821/xb201009002
	[ 赵荣钦, 黄贤金, 钟太洋. 中国不同产业空间的碳排放强度与碳足迹分析. 地理学报, 2010,65(9):1048-1057.] doi: 10.11821/xb201009002
[12]	Li Jian, Zhou Hui. Correlation analysis of carbon emission intensity and industrial structure in China. China Population, Resources and Environment, 2012,22(1):7-14.
	[ 李健, 周慧. 中国碳排放强度与产业结构的关联分析. 中国人口·资源与环境, 2012,22(1):7-14.]
[13]	Wang Qunwei, Zhou Peng, Zhou Dequn. Research on dynamic carbon dioxide emissions performance, regional disparity and affecting factors in China. China Industrial Economics, 2010(1):45-54.
	[ 王群伟, 周鹏, 周德群. 我国二氧化碳排放绩效的动态变化、区域差异及影响因素. 中国工业经济, 2010(1):45-54.]
[14]	Mielnik O, Goldemberg J. Communication the evolution of the "carbonization index" in developing countries. Energy Policy, 1999,27(5):307-308. doi: 10.1016/S0301-4215(99)00018-X
[15]	Yamaji K, Matsuhashi R, Nagata Y, et al. A study on economic measures for CO₂ reduction in Japan. Energy Policy, 1993,21(2):123-132. doi: 10.1016/0301-4215(93)90134-2
[16]	Sun J W. The decrease of CO₂ emission intensity is decarbonization at national and global levels. Energy Policy, 2005,33(8):975-978. doi: 10.1016/j.enpol.2003.10.023
[17]	Stretesky P B, Lynch M J. A cross-national study of the association between per capita carbon dioxide emissions and exports to the United States. Social Science Research, 2009,38(1):239-250. doi: 10.1016/j.ssresearch.2008.08.004
[18]	Zhang Z, Qu J, Zeng J. A quantitative comparison and analysis on the assessment indicators of greenhouse gases emission. Journal of Geographical Sciences, 2008,18(4):387-399. doi: 10.1007/s11442-008-0387-8
[19]	Ang B W. Is the energy intensity a less useful indicator than the carbon factor in the study of climate change? Energy Policy, 1999,27(15):943-946. doi: 10.1016/S0301-4215(99)00084-1
[20]	Zha Jianping, Tang Fangfang. Research on static level, dynamic change and affecting factors of industrial carbon emissions performance: An empirical analysis based on Chinese provincial panel data. Joumal of Shanxi Finance and Economics University, 2012(3):71-80.
	[ 查建平, 唐方方. 中国工业碳排放绩效: 静态水平及动态变化: 基于中国省级面板数据的实证分析. 山西财经大学学报, 2012(3):71-80.]
[21]	Wang Shaojian, Liu Yanyan, Fang Chuanglin. Review of energy-related CO₂ emission in response to climate change. Progress in Geography, 2015,34(2):151-164. doi: 10.11820/dlkxjz.2015.02.004
	[ 王少剑, 刘艳艳, 方创琳. 能源消费CO₂排放研究综述. 地理科学进展, 2015,34(2):151-164.] doi: 10.11820/dlkxjz.2015.02.004
[22]	Ramanathan R. Combining indicators of energy consumption and CO₂ emissions: A cross-country comparison. International Journal of Global Energy Issues, 2002,17(3):214-227. doi: 10.1504/IJGEI.2002.000941
[23]	Zhou P, Ang B W, Han J Y. Total factor carbon emission performance: A Malmquist index analysis. Energy Economics, 2010,32(1):194-201. doi: 10.1016/j.eneco.2009.10.003
[24]	Wang B, Lu Y, Yang Y. Measuring and decomposing energy productivity of China's industries under carbon emission constraints. Journal of Financial Research, 2013(10):128-141.
[25]	Meng M, Fu Y, Wang T, et al. Analysis of low-carbon economy efficiency of Chinese industrial sectors based on a RAM model with undesirable outputs. Sustainability, 2017,9(3):451. doi: 10.3390/su9030451
[26]	Liu W B, Meng W, Li X X, et al. DEA models with undesirable inputs and outputs. Annals of Operations Research, 2010,173(1):177-194. doi: 10.1007/s10479-009-0587-3
[27]	Wang Qunwei, Zhou Dequn, Zhou Peng. Regional carbon dioxide emission performance and its reduction potential based on environmental production technology: The case of main industrial provinces in China. Studies in Science of Science, 2011,29(6):868-875.
	[ 王群伟, 周德群, 周鹏. 区域二氧化碳排放绩效及减排潜力研究: 以我国主要工业省区为例. 科学学研究, 2011,29(6):868-875.]
[28]	Du K, Huang L, Yu K. Sources of the potential CO₂ emission reduction in China: A nonparametric metafrontier approach. Applied Energy, 2014,115(4):491-501. doi: 10.1016/j.apenergy.2013.10.046
[29]	Wang Zhaofeng, Du Yaoyao. Performance of carbon emission in Hunan province based on three-stage SBM model. Journal of Central South University of Forestry & Technology (Social Sciences), 2019,13(1):23-30.
	[ 王兆峰, 杜瑶瑶. 基于三阶段SBM模型的湖南省碳排放绩效评价. 中南林业科技大学学报(社会科学版), 2019,13(1):23-30.]
[30]	Choi Y, Ning Z, Zhou P. Efficiency and abatement costs of energy-related CO₂ emissions in China: A slacks-based efficiency measure. Applied Energy, 2012,98:198-208. doi: 10.1016/j.apenergy.2012.03.024
[31]	Li Tao. Measuring the win-win performance of China's carbon reduction and economic growth with energy constraint: A rang-adjusted measure of non-radial DEA. China Economic Quarterly, 2013,12(2):667-692.
	[ 李涛. 资源约束下中国碳减排与经济增长的双赢效率研究: 基于非径向DEA方法RAM模型的测度. 经济学(季刊), 2013,12(2):667-692.]
[32]	Shen C, Feng R, Yu B, et al. Industrial CO₂ emissions efficiency and its determinants in China: Analyzing differences across regions and industry sectors. Polish Journal of Environmental Studies, 2018,27(3):1239-1254. doi: 10.15244/pjoes/76794
[33]	IEA. World Energy Outlook 2012. Paris: International Energy Agency (IEA), 2012.
[34]	Wang S, Liu X. China's city-level energy-related CO₂ emissions: Spatiotemporal patterns and driving forces. Applied Energy, 2017,200:204-214. doi: 10.1016/j.apenergy.2017.05.085
[35]	Wang Shaojian, Su Yongxian, Zhao Yabo. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions. Acta Geographica Sinica, 2018,73(3):414-428. doi: 10.11821/dlxb201803003
	[ 王少剑, 苏泳娴, 赵亚博. 中国城市能源消费碳排放的区域差异、空间溢出效应及影响因素. 地理学报, 2018,73(3):414-428.] doi: 10.11821/dlxb201803003
[36]	Tone K. A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 2001,130(3):498-509. doi: 10.1016/S0377-2217(99)00407-5
[37]	Wang Zhaofeng, Du Yaoyao. Spatial-temporal differences and influencing factors of carbon emission efficiency in Hunan province based on SBM-DEA model. Scientia Geographica Sinica, 2019,39(5):797-806.
	[ 王兆峰, 杜瑶瑶. 基于SBM-DEA模型湖南省碳排放效率时空差异及影响因素分析. 地理科学, 2019,39(5):797-806.]
[38]	Zhou C, Shi C, Wang S, et al. Estimation of eco-efficiency and its influencing factors in Guangdong province based on Super-SBM and panel regression models. Ecological Indicators, 2018,86:67-80. doi: 10.1016/j.ecolind.2017.12.011
[39]	Wang J, Wang S, Li S, et al. Evaluating the energy-environment efficiency and its determinants in Guangdong using a slack-based measure with environmental undesirable outputs and panel data model. Science of the Total Environment, 2019,663:878-888. doi: 10.1016/j.scitotenv.2019.01.413
[40]	Liu Xin, Li Shumin. An empirical analysis on the provincial energy efficiency in China based on SBM model of undesirable output. Mathematics in Practice and Theory, 2015,45(2):35-43.
	[ 刘心, 李淑敏. 基于非期望产出SBM模型的中国各省份能源效率的实证分析. 数学的实践与认识, 2015,45(2):35-43.]
[41]	Zhang J, Zeng W, Wang J, et al. Regional low-carbon economy efficiency in China: Analysis based on the Super-SBM model with CO₂ emissions. Journal of Cleaner Production, 2017,163:202-211. doi: 10.1016/j.jclepro.2015.06.111
[42]	Hong L, Fang K, Wei Y, et al. Regional environmental efficiency evaluation in China: Analysis based on the Super-SBM model with undesirable outputs. Mathematical & Computer Modelling, 2013,58(5/6):1018-1031.
[43]	Tone K. A slacks-based measure of super-efficiency in data envelopment analysis. European Journal of Operational Research, 2002,143(1):32-41. doi: 10.1016/S0377-2217(01)00324-1
[44]	Le Gallo J. Space-time analysis of GDP disparities among European regions: A Markov chains approach. International Regional Science Review, 2004,27(2):138-163. doi: 10.1177/0160017603262402
[45]	Wei Y H D, Liao F H. Dynamics, space, and regional inequality in provincial China: A case study of Guangdong province. Applied Geography, 2012,35(1/2):71-83. doi: 10.1016/j.apgeog.2012.05.003
[46]	Rey S J. Spatial empirics for economic growth and convergence. Geographical Analysis, 2010,33(3):195-214. doi: 10.1111/gean.2001.33.issue-3
[47]	Zeng Gang, Shang Yongmin, Si Yuefang. The convergent evolution of China's regional economic development models. Geographical Research, 2015,34(11):2005-2020.
	[ 曾刚, 尚勇敏, 司月芳. 中国区域经济发展模式的趋同演化: 以中国16种典型模式为例. 地理研究, 2015,34(11):2005-2020.]
[48]	Zhang Xueliang. Has transport infrastructure promoted regional economic growth? With an analysis of the spatial effects of transport infrasturcture. Social Sciences in China, 2012(3):60-77.
	[ 张学良. 中国交通基础设施促进了区域经济增长吗: 兼论交通基础设施的空间溢出效应. 中国社会科学, 2012(3):60-77.]
[49]	World Bank. World Development Indicators 2010. Beijing: Chinese Financial & Economic Publishing House, 2010.
	[ 世界银行. 2010年世界发展指标. 北京: 中国财政经济出版社, 2010.]
[50]	Wang Shaojian, Huang Yongyuan. Spatial spillover effect and driving forces of carbon emission intensity at city level in China. Acta Geographica Sinica, 2019,74(6):1131-1148. doi: 10.11821/dlxb201906005
	[ 王少剑, 黄永源. 中国城市碳排放强度的空间溢出效应及驱动因素探究. 地理学报, 2019,74(6):1131-1148.] doi: 10.11821/dlxb201906005
[51]	Wei Xiahai, Yu Lingzheng. Space dependence, carbon emissions and economic growth: Reinterpret China's EKC hypothesis. Probe, 2011(1):100-105.
	[ 魏下海, 余玲铮. 空间依赖、碳排放与经济增长: 重新解读中国的EKC假说. 探索, 2011(1):100-105.]
[52]	Wang S, Liu X, Zhou C, et al. Examining the impacts of socioeconomic factors, urban form, and transportation networks on CO₂ emissions in China's megacities. Applied Energy, 2017,185:189-200. doi: 10.1016/j.apenergy.2016.10.052
[53]	Wang S, Li Q, Fang C, et al. The relationship between economic growth, energy consumption, and CO₂ emissions: Empirical evidence from China. Science of The Total Environment, 2016,542(Part A):360-371. doi: 10.1016/j.scitotenv.2015.10.027

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

RichHTML

PDF (PC)

赞

可视化

摘要/Abstract

引用本文

使用本文

图/表 10

参考文献 53

相关文章 15

Metrics

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

[1]	魏石梅, 潘竟虎. 中国地级及以上城市网络结构韧性测度[J]. 地理学报, 2021, 76(6): 1394-1407.
[2]	杨忍, 潘瑜鑫. 中国县域乡村脆弱性空间特征与形成机制及对策[J]. 地理学报, 2021, 76(6): 1438-1454.
[3]	殷江滨, 李尚谦, 姜磊, 程哲, 黄晓燕, 路改改. 中国连片特困地区非农就业增长的时空特征与驱动因素[J]. 地理学报, 2021, 76(6): 1471-1488.
[4]	胡畔, 陈波, 史培军. 中国暴雨洪涝灾情时空格局及影响因素[J]. 地理学报, 2021, 76(5): 1148-1162.
[5]	黄晓东, 马海涛, 苗长虹. 基于创新企业的中国城市网络联系特征[J]. 地理学报, 2021, 76(4): 835-852.
[6]	王录仓, 刘海洋, 刘清. 基于腾讯迁徙大数据的中国城市网络研究[J]. 地理学报, 2021, 76(4): 853-869.
[7]	夏兴生, 潘耀忠, 朱秀芳, 张锦水. 中国综合农业分区下的Ångström-Prescott公式系数逐月校正与优选[J]. 地理学报, 2021, 76(4): 888-902.
[8]	王淑佳, 孙九霞. 中国传统村落可持续发展评价体系构建与实证[J]. 地理学报, 2021, 76(4): 921-938.
[9]	范泽孟. 中国生态过渡带分布的空间识别及情景模拟[J]. 地理学报, 2021, 76(3): 626-644.
[10]	徐羽, 李秀彬, 辛良杰. 中国耕地规模化流转租金的分异特征及其影响因素[J]. 地理学报, 2021, 76(3): 753-763.
[11]	古恒宇, 沈体雁. 中国高学历人才的空间演化特征及驱动因素[J]. 地理学报, 2021, 76(2): 326-340.
[12]	朱晟君, 金文纨. 地方出口产品结构及制度环境与企业出口相关多样化[J]. 地理学报, 2021, 76(2): 398-414.
[13]	李钢, 薛淑艳, 马雪瑶, 周俊俊, 徐婷婷, 王皎贝. 中国失踪人口的时空格局演变与形成机制[J]. 地理学报, 2021, 76(2): 310-325.
[14]	葛全胜, 朱会义. 两千年来中国自然与人文地理环境变迁及启示[J]. 地理学报, 2021, 76(1): 3-14.
[15]	张兴航, 张百平, 王晶, 余付勤, 赵超, 姚永慧. 中国南北过渡带东段样带植被序列与气候分界问题[J]. 地理学报, 2021, 76(1): 30-43.