中国碳排放空间格局的时空演化——基于动态演化及空间集聚的视域

doi:10.12068/j.issn.1005-3026.2016.11.031

东北大学学报:自然科学版 ›› 2016, Vol. 37 ›› Issue (11): 1668-1672.DOI: 10.12068/j.issn.1005-3026.2016.11.031

• 管理科学 • 上一篇

中国碳排放空间格局的时空演化——基于动态演化及空间集聚的视域

佟昕^1,2，李学森³，佟琳⁴，陈凯¹

(1. 东北大学工商管理学院，辽宁沈阳110169; 2. 中央财经大学经济学院，北京100081; 3. 沈阳职业技术学院，辽宁沈阳110045; 4 . 大连科技学院，辽宁大连116052)

收稿日期:2015-09-09 修回日期:2015-09-09 出版日期:2016-11-15 发布日期:2016-11-18
通讯作者: 佟昕
作者简介:佟昕(1975-)，女，辽宁沈阳人，东北大学秦皇岛分校讲师，中央财经大学博士后研究人员；陈凯(1961-)，男，山西浑源人，东北大学教授，博士生导师.
基金资助:
河北省社会科学基金资助项目(HB16YJ002).

Spatial-Time Evolution of Carbon Emission Patterns in China: Based on Dynamic Evolution and Spatial Agglomeration

TONG Xin^1,2， LI Xue-sen³， TONG Lin⁴， CHEN Kai¹

1. School of Business Administration， Northeastern University， Shenyang 110169， China; 2. School of Economics， Central University of Finance and Economics， Beijing 100081， China; 3. Shenyang Polytechnic College， Shenyang 110045， China; 4. Dalian Institute of Science and Technology， Dalian 116052， China.

Received:2015-09-09 Revised:2015-09-09 Online:2016-11-15 Published:2016-11-18
Contact: TONG Xin
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对中国碳排放的空间相关性和差异性，通过全域空间自相关检验分析中国区域碳排放的空间依赖性和时空跃迁路径，局域空间自相关检验分析中国区域碳排放的空间集聚性，依据时空跃迁测度法对中国区域碳排放散点图的时空演化展开分析.结果显示:中国碳排放省域间存在自相关性，并且表现为一种集聚格局；碳排放的相关性呈逐渐增强的趋势；中国碳排放的空间相关性和局域区域结构具有一定的锁定效应和路径依赖.因此，应该客观理性地看待中国碳排放问题，发展低碳经济和治理碳排放的政策应该主要从空间视域动态地综合考虑.

Abstract: Aiming at the spatial correlation and difference of Chinese carbon emission， the spatial dependence and temporal transition path of Chinese regional carbon emission were analyzed with global spatial autocorrelation testing the spatial agglomeration was explored with local spatial autocorrelation testing， and the evolution of Chinese regional carbon emission scatter plot was studied based on Rey’s measurement of spatial and temporal transition . The results showed that there exists an inter-province correlation in Chinese carbon emission with a clustering pattern， which tends to increase gradually， and the spatial correlation of Chinese carbon emissions and the local regional structure are quite stable and path dependent. Therefore， the issue of Chinese carbon emission should be viewed objectively and rationally， the development of low-carbon economy and the policies of managing carbon emission in China should be considered both globally and dynamically from the spatial perspective.

Key words: carbon emission, spatial correlation, spatial difference, space-time dynamics, space-time transition, spatial agglomeration

中图分类号:

F205
X24

佟昕，李学森，佟琳，陈凯. 中国碳排放空间格局的时空演化——基于动态演化及空间集聚的视域[J]. 东北大学学报:自然科学版, 2016, 37(11): 1668-1672.

TONG Xin， LI Xue-sen， TONG Lin， CHEN Kai. Spatial-Time Evolution of Carbon Emission Patterns in China: Based on Dynamic Evolution and Spatial Agglomeration[J]. Journal of Northeastern University Natural Science, 2016, 37(11): 1668-1672.

参考文献

[1]Tobler W.A computer movie simulating urban growth in the detroit region［J］.Economic Geography，1970，46(2):234-240.
[2]Lesage J P.The theory and practice of spatial econometrics ［M］.Toledo:University of Toledo， 1999:33-83.
[3]邓长德，刘帅.基于空间计量经济学的碳排放与经济增长分析［J］.中国人口·资源与环境，2011，21(5):80-86.(Deng Chang-de，Liu Shuai.Empirical research of carbon emission and economic growth in China based on the spatial econometric analysis［J］.China Population Resources and Environment，2011，21(5):80-86.)
[4]胡宗义，刘亦文.能源消耗、污染排放与区域经济增长关系的空间计量分析［J］.数理统计与管理，2015，34(1):1-9.(Hu Zong-yi，Liu Yi-wen.An spatial econometric research on the relationship among energy consumption［J］.Pollution Discharge and Regional Economic Growth Journal of Applied Statistics and Management，2015，34(1):1-9.)
[5]佟昕，陈凯，李刚.国际贸易与碳排放的空间维度溢出—基于中国30个省域数据的空间计量验证［J］.经济管理，2014(11):14-24.(Tong Xin，Chen Kai，Li Gang.A verification of international trade and carbon emissions apatial overflow based on 30 provinces data and spatial econometrics model［J］.Economic Management，2014(11):14-24.)
[6]Cliff A，Ord J K.Testing for spatial autocorrelation among regression residuals［J］.Geographical Analysis，1972，4(3):4267-4284.
[7]Anselin L.Spatial econometrics［M］.Bruton Center:School of Social Science，University of Texas at Dallas，1999:55-164.
[8]Anselin L.Local indicators of spatial association-LISA［J］.Geographical Analysis，1995，27(2):93-115.
[9]IPCC.2006 IPCC Guidelines for national greenhouse gas inventories［M］.Nara:IGES，2006.
[10]蒋金荷.中国碳排放量测算及影响因素分析［J］.资源科学，2011，33(4):597-604.(Jiang Jin-he.An evaluation and decomposition analysis of carbon emissions in China［J］.Resources Science，2011，33(4):597-604.)
[11]Rey S J.Spatial empirics for economic growth and convergence［J］.Geographical Analysis，2011，33(3):195-214.
[12]Rey S J，Murray A T，Anselin L.Visualizing regional income distribution dynamics［J］.Letters in Spatial and Resource Sciences，2011，4(1):81-90.(上接第1667页)
[9]董晓舟，陈转青.网站依恋对网络顾客忠诚影响的实证研究—通向网络顾客忠诚的第三条路径［J］.营销科学学报，2014，10(4):68-84.(Dong，Xiao-zhou，Chen Zhuan-qing.Empirical research on the effect of attachment to e-loyalty:the third path towards e-loyalty［J］.Journal of Marketing Science， 2014，10(4):68-84.)
[10]范秀成，郑秋莹，姚唐，等.顾客满意带来什么忠诚?［J］.管理世界，2009，2(1):89-91.(Fan Xiu-cheng，Zheng Qiu-ying，Yao Tang，et al.What loyalty is brought by customer satisfaction?［J］.Management World， 2009，2(1):89-91.)
[11]Harris L C，Goode M M H.The four levels of loyalty and the pivotal role of trust:a study of online service dynamics ［J］. Journal of Retailing，2004，80(2):139-158.
[12]董志勇.行为经济学［M］.北京:北京大学出版社，2005.(Dong Zhi-yong.Behavioural economy［M］.Beijing:Peking University Press，2007.)
[13]郑兵.B2C网络商店物流服务质量及其与顾客忠诚的关系研究［D］.大连:大连理工大学，2008.(Zheng Bing.A research on the relationship between logistic service quality and customer loyalty under the situation of B2C online shops［D］.Dalian:Dalian University of Technology，2008.)

中国碳排放空间格局的时空演化——基于动态演化及空间集聚的视域

Spatial-Time Evolution of Carbon Emission Patterns in China: Based on Dynamic Evolution and Spatial Agglomeration

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 6

编辑推荐

Metrics

本文评价

[1]	侯卉，殷赟，卜曼曼，何杰. 辽宁省碳排放预测及最优情境选择[J]. 东北大学学报:自然科学版, 2019, 40(8): 1211-1216.
[2]	于冠一，修春亮，赵昌松. 辽宁省环境规制约束下的雾霾脱钩效应分析[J]. 东北大学学报:自然科学版, 2019, 40(3): 435-440.
[3]	杨沫，陈凯. 基于STIRPAT及解耦模型的河北省碳排放影响因素分析[J]. 东北大学学报:自然科学版, 2017, 38(2): 300-304.
[4]	佟昕，陈凯，李刚. 中国碳排放影响因素分析和趋势预测——基于STIRPAT 和GM(1，1)模型的实证研究[J]. 东北大学学报:自然科学版, 2015, 36(2): 297-300.
[5]	杨培颖，唐加福，于洋. 低碳型机场接送服务的改进二维扫描算法[J]. 东北大学学报(自然科学版), 2013, 34(4): 478-481.
[6]	张玉明;李凯;. 基于空间知识溢出的经济增长模型[J]. 东北大学学报(自然科学版), 2008, 29(2): 297-300.