中国城市碳排放强度时空演变与影响因素的时空异质性

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (1187 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要运用地图可视化、核密度估计、标准差椭圆等方法对中国2005~2020年城市碳排放强度的时空演变与路径迁移进行探究,并基于时空地理加权模型(GTWR),实证考察了城市碳排放强度影响因素的时空异质性.研究发现:(1)中国城市碳排放强度逐年降低,但仍远超同期发达国家水平,减排空间较大.碳排放强度呈“北高南低”的空间分布,南北分异趋势在研究后期愈发明显.(2)各区域内部城市碳排放强度差异逐渐收敛,分布越发均衡.东部城市碳排放强度的多级分化现象显著,西部高碳城市集聚分布.碳排放强度高值区域自山西省境内持续向西北方向转移,西北地区逐步成为中国碳排放的主要贡献区域.(3)总体上,高碳化能源消费对碳排放强度呈正向促进效应,产业升级、经济发展、人口集聚、科技研发与外资强度则主要表现为负向抑制.局部内,各因素作用效果存在较强的时空分异,不同区域与时期内,各影响因素的波动方向与作用强度不尽相同.

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	李云燕
	张硕

关键词 ：城市, 碳排放强度, 影响因素, 时空演变, 时空地理加权模型

Abstract：Investigating the Spatiotemporal Evolution and Pathway Migration of Urban Carbon Intensity in China from 2005 to 2020 using Map Visualization, Kernel Density Estimation, and Standard Deviation Ellipses. Empirical Examination of Spatiotemporal Heterogeneity of Factors Affecting Urban Carbon Intensity using a Geographically and Temporally Weighted Regression (GTWR) Model. The study finds that: (1) Carbon emission intensity in Chinese cities has decreased over the years but remains significantly higher than that of developed countries during the same period, with considerable room for emission reduction. Spatially, there is a "north-south" disparity in carbon emission intensity, which has become more prominent in the later period of the study. (2) The differences in carbon emission intensity of cities within each region gradually converged, and the distribution became more balanced. The multi-level differentiation of carbon emission intensity in the eastern region was remarkable, and there were still high carbon agglomerations in the west. The region with high carbon emission intensity gradually shifted to the northwest, and the northwest became the main contributor to China's carbon emissions. (3) In general, high-carbon energy consumption has a positive effect in promoting carbon intensity, while industrial upgrading, economic development, population agglomeration, technological research and development, and foreign investment intensity have mainly negative inhibitory effects. However, at the local level, the effects of these factors exhibit significant spatiotemporal heterogeneity. The direction and strength of the fluctuations of various influencing factors vary across different regions and periods.

Key words： city carbon emission intensity influencing factors spatial and temporal evolution GTWR model

收稿日期: 2022-10-08

PACS:

X22

基金资助:国家社会科学基金后期资助项目(21FJYB023);2022年北京工业大学服务首都重大战略决策咨询重点项目(011000514122545);北京市社会科学基金重点项目(19YJA002)

通讯作者: 李云燕,教授,yunyanli@126.com E-mail: yunyanli@126.com

作者简介: 李云燕(1963-),女,山东滨州人,教授,博士,主要从事环境经济、低碳政策研究.发表论文90余篇.yunyanli@126.com

引用本文:

李云燕, 张硕. 中国城市碳排放强度时空演变与影响因素的时空异质性[J]. 中国环境科学, 2023, 43(6): 3244-3254. LI Yun-yan, ZHANG Shuo. Spatio-temporal evolution of urban carbon emission intensity and spatiotemporal heterogeneity of influencing factors in China. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(6): 3244-3254.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2023/V43/I6/3244

[1]	Pan X, Guo S, Xu H, et al. China's carbon intensity factor decomposition and carbon emission decoupling analysis[J]. Energy, 2022,239:122175.
[2]	Zhang F, Deng X, Phillips F, et al. Impacts of industrial structure and technical progress on carbon emission intensity:Evidence from 281cities in China[J]. Technological Forecasting and Social Change. 2020,154:119949.
[3]	Huang Y, Zhu H, Zhang Z. The heterogeneous effect of driving factors on carbon emission intensity in the Chinese transport sector:Evidence from dynamic panel quantile regression[J]. Science of the Total Environment, 2020,727:138578.
[4]	田华征,马丽.中国工业碳排放强度变化的结构因素解析[J]. 自然资源学报, 2020,35(3):639-653. Tian H Z, Ma L. Study on the change of China's industrial carbon emission intensity from the perspective of sector structure[J]. Journal of Natural Resources, 2020,35(3):639-653.
[5]	赵桂梅,耿涌,孙华平,等.中国省际碳排放强度的空间效应及其传导机制研究[J]. 中国人口·资源与环境, 2020,30(3):49-55. Zhao G M, Geng Y, Sun H P, et al. Spatial effects and transmission mechanism of inter-provincial carbon emission intensity in China[J]. China Population, Resources and Environment, 2020,30(3):49-55.
[6]	王少剑,谢紫寒,王泽宏.中国县域碳排放的时空演变及影响因素[J]. 地理学报, 2021,76(12):3103-3118. Wang S J, Xie Z H, Wang Z H. The spatiotemporal pattern evolution and influencing factors of CO2 emissions at the county level of China[J]. Acta Geographica Sinica, 2021,76(12):3103-3118.
[7]	Zhao Y, Chen R, Zang P, et al. Spatiotemporal patterns of global carbon intensities and their driving forces[J]. Science of the Total Environment, 2022,818:151690.
[8]	原嫄,孙欣彤.中国省际碳排放强度时空演变及驱动因素研究[J]. 环境科学与技术, 2022,45(S1):168-176. Yuan Y, Sun X T. Spatial-temporal evolution and driving factors of inter-provincial carbon emission intensity in China[J]. Environmental Science & Technology, 2022,45(S1):168-176.
[9]	张卓群,张涛,冯冬发.中国碳排放强度的区域差异､动态演进及收敛性研究[J]. 数量经济技术经济研究, 2022,39(4):67-87. Zhang Z Q, Zhang T, Feng D F. Study on regional differences, dynamic evolution and convergence of carbon emission intensity in China[J]. Journal of Quantitative & Technological Economics, 2022, 39(4):67-87.
[10]	Feng K, Hubacek K, Guan D. Lifestyles, technology and CO2 emissions in China:A regional comparative analysis[J]. Ecological Economics, 2009,69(1):145-154.
[11]	Liu S, Wang Y, Liang Y. Environmental consequence analysis of oil spills from onshore pipelines with parametric uncertainty[J]. Process Safety and Environmental Protection. 2020,141:123-134.
[12]	朱泳丽,丁利杰.长三角城市群碳排放强度的空间效应及影响因素——基于产业转移视角[J]. 资源科学, 2022,44(7):1373-1387. Zhu Y L, Ding L J. Spatial effects and influencing factors of carbon emission intensity in the Yangtze River Delta urban agglomeration:An industrial transfer-based perspective[J]. Resources Science, 2022, 44(7):1373-1387.
[13]	Zhang F, Deng X, Phillips F, et al. Impacts of industrial structure and technical progress on carbon emission intensity:Evidence from 281cities in China[J]. Technological Forecasting and Social Change, 2020,154:119949.
[14]	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[J]. 地理学报, 2019,74(6):1131-1148. Wang S J, Huang Y Y, Spatial spillover effect and driving forces of carbon emission intensity at city level in China[J]. Acta Geographica Sinica, 2019,74(6):1131-1148.
[15]	Song C, Zhao T, Wang J. Spatial-temporal analysis of China's regional carbon intensity based on ST-IDA from 2000 to 2015[J]. Journal of Cleaner Production, 2019,238:117874.
[16]	Cao Y, Zhao Y, Wang H, et al. Driving forces of national and regional carbon intensity changes in China:Temporal and spatial multiplicative structural decomposition analysis[J]. Journal of Cleaner Production, 2019,213:1380-1410.
[17]	宋鹏,张慧敏,毛显强.面向碳达峰目标的重庆市碳减排路径[J]. 中国环境科学, 2022,42(3):1446-1455. Song P, Zhang H M, Mao X Q. Research on Chongqing's carbon emission reduction path towards the goal of carbon peak[J]. China Environmental Science, 2022,42(3):1446-1455.
[18]	Yan B, Wang F, Dong M, et al. How do financial spatial structure and economic agglomeration affect carbon emission intensity? Theory extension and evidence from China[J]. Economic Modelling, 2022, 108:105745.
[19]	Yu Q, Li M, Li Q, et al. Economic agglomeration and emissions reduction:Does high agglomeration in China's urban clusters lead to higher carbon intensity?[J]. Urban Climate, 2022,43:101174.
[20]	肖宏伟,易丹辉.基于时空地理加权回归模型的中国碳排放驱动因素实证研究[J]. 统计与信息论坛, 2014,29(2):83-89. Xiao H W, Yi D H. Empirical study of carbon emissions drivers based on geographically time weighted regression model[J]. Journal of Statistics and Information, 2014,29(2):83-89.
[21]	王凯,唐小惠,甘畅,等.中国服务业碳排放强度时空格局及影响因素[J]. 中国人口·资源与环境, 2021,31(8):23-31. Wang K, Tang X H, Gan C, et al. Temporal spatial evolution and influencing factors of carbon emission intensity of China,s service industry[J]. China Population, Resources and Environment, 2021,31 (8):23-31.
[22]	Zhang F, Deng X, Phillips F, et al. Impacts of industrial structure and technical progress on carbon emission intensity:Evidence from 281cities in China[J]. Technological Forecasting and Social Change, 2020,154:119949.
[23]	虞义华,郑新业,张莉.经济发展水平､产业结构与碳排放强度——中国省级面板数据分析[J]. 经济理论与经济管理, 2011,(3):72-81. Yu Y H, Zheng X Y, Zhang L. Carbon dioxide emission and economic development:A panel data analysis[J]. Economic Theory and Business Management, 2011,(3):72-81.
[24]	余志伟,樊亚平,罗浩.中国产业结构高级化对碳排放强度的影响研究[J]. 华东经济管理, 2022,36(1):78-87. Yu Z W, Fan Y P, Luo H. Research on the influence of industrial structure upgrading on carbon emission intensity in China[J]. East China Economic Management, 2022,36(1):78-87.
[25]	朱于珂,高红贵,徐运保.双向FDI协调发展如何降低区域CO2排放强度?——基于企业绿色技术创新的中介效应与政府质量的调节作用[J]. 软科学, 2022,36(2):86-94. Zhu Y K, Gao H G, Xu Y B. How does coordinated development of two-way FDI reduce regional CO2 emission intensity?——Based on the mediating effect of enterprise green technology innovation and the moderating effect of government quality[J]. Soft Science, 2022,36(2):86-94.
[26]	沈杨,汪聪聪,高超,等.基于城市化的浙江省湾区经济带碳排放时空分布特征及影响因素分析[J]. 自然资源学报, 2020,35(2):329-342. Shen Y, Wang C C, Gao C, et al. Spatio-temporal distribution and its influencing factors of carbon emissions in economic zone of Zhejiang Bay Area based on urbanization[J]. Journal of Natural Resources, 2020,35(2):329-342.
[27]	吴建新,郭智勇.基于连续性动态分布方法的中国碳排放收敛分析[J]. 统计研究, 2016,33(1):54-60. Wu J X, Guo Z Y. Research on the convergence of carbon dioxide emissions in China:A continuous dynamic distribution approach[J]. Statistical Research, 2016,33(1):54-60.
[28]	任晓松,刘宇佳,赵国浩.经济集聚对碳排放强度的影响及传导机制[J]. 中国人口·资源与环境, 2020,30(4):95-106. Ren X S, Liu Y J, Zhao G H. The impact and transmission mechanism of economic agglomeration on carbon intensity[J]. China Population,Resources and Environment, 2020,30(4):95-106.
[29]	中国电力出版社.2021中国电力年鉴[M]. 北京:中国电力出版社, 2021. China Electric Power Press. 2021 China Electric Power Yearbook[M]. Beijing:China Electric Power Press, 2021.