中国省域交通碳排放空间关联格局的驱动特征

摘要
图/表
参考文献
相关文章 (3)

全文: PDF (1070 KB) HTML (0 KB)
输出: BibTeX | EndNote (RIS)

摘要利用我国2003~2020年省级交通碳排放数据，基于模体结构分析和指数随机图模型对中国交通碳排放空间关联网络的宏观格局、微观连通和驱动特征进行研究.结果表明，中国交通碳排放空间关联网络呈现周期波动特征，等级均衡化发展和核心虹吸的空间分布，演化出少核心—多半核心—边缘新格局，碳转移路径表现出明显的空间惰性与时间惯性以及区域集中性；经纪属性在推动跨区域交通协同减排的经济活动中发挥重要作用；内生网络的互惠性、连通性、集聚性对交通碳排放网络形成具有重要驱动作用，行为者属性中发出、接收、抑制、互惠的驱动关系明显，外部网络的影响具有明显的地理邻近效应，呈现出地理距离衰减规律特征.由此提出短期重大项目驱动，推动绿色交通转型联动；中期提升协同水平，加强跨区域交通碳减排协调机制；长期顶层零碳设计，工业化-产业化-市场化全面协同等政策建议.

	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
	杨青
	郭露
	刘星星
	赵坤强

关键词 ：交通碳排放, 模体结构分析, 指数随机图模型, 驱动要素

Abstract：Based on the provincial transportation carbon emission data from 2003 to 2020, the macro pattern, micro connectivity and driving characteristics of China's transportation carbon emission spatial correlation network were studied based on the modular structure analysis and the exponential random graph model. The spatial correlation network of China's transport carbon emissions presents periodic fluctuation characteristics, and the spatial distribution of hierarchical equilibrium development and core siphon has evolved into a new pattern with few core-mostly core-edge can be derived from results. Obvious spatial inertia, time inertia and regional concentration were shown in the carbon transfer path shows. In promoting the economic activities of trans-regional transportation collaborative emission reduction, brokerage attributes was instrumental. Reciprocity, connectivity and agglomeration of endogenous networks played an important driving role in the formation of transport carbon emission networks, and the driving relationship between emission, reception, inhibition and reciprocity among the attributes of actors was obvious. The influence of external networks had an obvious geographical proximity effect, showing a regular feature of geographical distance attenuation. Therefore, policy suggestions such as major projects in the short term, promoting the linkage of green transportation transformation, upgrading the level of coordination in the medium term, strengthening the coordination mechanism of cross-regional transportation carbon emission reduction, long-term top-level zero carbon design, and comprehensive coordination of industrialization- industrialization-marketization were proposed.

Key words： transportation carbon emission modal structure analysis exponential random graph model driving factor

收稿日期: 2023-07-02

PACS:

X321

基金资助:国家自然科学基金资助项目（72374164）

通讯作者: 刘星星,副教授,liuxingxing@whut.edu.cn E-mail: liuxingxing@whut.edu.cn

作者简介: 杨青(1962-),男,广东大埔人,教授,博士,主要研究方向为城市绿色发展与智能管理.发表论文100余篇.yangq@whut.edu.cn.

引用本文:

杨青, 郭露, 刘星星, 赵坤强. 中国省域交通碳排放空间关联格局的驱动特征[J]. 中国环境科学, 2024, 44(2): 1171-1184. YANG Qing, GUO Lu, LIU Xing-xing, ZHAO Kun-qiang. Driving characteristics of the spatial correlation pattern of carbon emissions from provincial transportation in China. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(2): 1171-1184.

链接本文:

http://www.zghjkx.com.cn/CN/ 或 http://www.zghjkx.com.cn/CN/Y2024/V44/I2/1171

[1]	金凤君,陈卓.新时代交通强国的地理内涵与目标[J]. 经济地理, 2023,43(2):1-9. Jin F J, Chen Z. Geographical connotation and target system of building national strength in transportation in the new era [J]. Economic Geography, 2023,43(2):1-9.
[2]	郑航,叶阿忠.城市群碳排放空间关联网络结构及其影响因素[J]. 中国环境科学, 2022,42(5):2413-2422. Zheng H, Ye A Z. Spatial correlation network structure and influencing factors of carbon emission in urban agglomeration [J]. China Environmental Science, 2022,42(5):2413-2422.
[3]	王靖添,闫琰,黄全胜,等.中国交通运输碳减排潜力分析[J]. 科技管理研究, 2021,41(2):200-210. Wang J T, Yan Y, Huang Q S, et al. Analysis of carbon emission reduction potential of China's transportation [J]. Science and Technology Management Research, 2021,41(2):200-210.
[4]	刘淳森,曲建升,葛钰洁,等.基于LSTM模型的中国交通运输业碳排放预测[J]. 中国环境科学, 2023,43(5):2574-2582. Liu C S, Qu J S, Ge Y J, et al. LSTM model-based prediction of carbon emissions from China's transportation sector [J]. China Environmental Science, 2023,43(5):2574-2582.
[5]	张正峰,张栋.基于社会网络分析的京津冀地区碳排放空间关联与碳平衡分区[J]. 中国环境科学, 2023,43(4):2057-2068. Zhang Z F, Zhang D. Spatial relatedness of CO2 emission and carbon balance zoning in beijing tianjin hebei counties [J]. Chinese Journal of Environmental Sciences, 2023,43(4):2057-2068.
[6]	袁长伟,张帅,焦萍,等.中国省域交通运输全要素碳排放效率时空变化及影响因素研究[J]. 资源科学, 2017,39(4):687-697. Yuan C W, Zhang S, Jiao P, et al. Temporal and spatial variation and influencing factors research on total factorefficiency for transportation carbon emissions in China [J]. Resources Science, 2017,39(4):687-697.
[7]	曾晓莹,邱荣祖,林丹婷,等.中国交通碳排放及影响因素时空异质性[J]. 中国环境科学, 2020,40(10):4304-4313. Zeng X Y, Qiu R Z, Lin D T, et al. Spatio-temporal heterogeneity of transportation carbon emissions and its influencing factors in China [J]. China Environmental Science, 2020,40(10):4304-4313.
[8]	闫敏,孙慧.经济内循环视角下中国省域间隐含碳转移网络结构特征研究[J]. 生态经济, 2022,38(2):13-21. Yan M, Sun H. Research on the network structural characteristics of China's provincial Implicit carbon ttransfer from the perspective of economic internal circulation [J]. Ecological Economy, 2022,38(2):13-21.
[9]	邵海琴,王兆峰.中国交通碳排放效率的空间关联网络结构及其影响因素[J]. 中国人口·资源与环境, 2021,31(4):32-41. Shao H Q, Wang Z F. Spatial network structure of transportation carbon emissions efficiency in China and its influencing factors [J]. China Population, Resources and Environment, 2021,31(4):32-41.
[10]	邵帅,徐俐俐,杨莉莉.千里"碳缘"一线牵:中国区域碳排放空间关联网络的结构特征与形成机制[J]. 系统工程理论与实践, 2023,43(4):958-983. Shao S, Xu L L, Yang L L. Structural characteristics and formation mechanism of carbon emission spatial association networks within China [J]. Systems Engineering-Theory & Practice, 2023,43(4):958-983.
[11]	Milo R, Itzkovitz S, Kashtan N, Levitt R, Alon U. Response to comment on "network mot ifs:simple building blocks of complex networks" and "super families of evolved and designed networks"[J]. Science, 2004,305(5687):1107.
[12]	Xiong J, Feng X D, Tang Z W. Understanding user-to-user interaction on government microblogs:An exponential random graph model with the homophily and emotional effect [J]. Information Processing & Management, 2020,57:102229.
[13]	黄群慧."新常态"、工业化后期与工业增长新动力[J]. 中国工业经济, 2014,(10):5-19. Huang Q H. "New normal", late industrialization and new driving force of industrial growth [J]. China Industrial Economics, 2014, (10):5-19.
[14]	吉雪强,刘慧敏,张跃松.中国省际土地利用碳排放空间关联网络结构演化及驱动因素[J]. 经济地理, 2023,43(2):190-200. Ji X Q, Liu H M, Zhang Y S. Spatiotemporal evolution and driving factors of spatial correlation network structure of China's land-use carbon emission [J]. Economic Geography, 2023,43(2):190-200.
[15]	Hunter D R, Handcock M S, Butts C T, Goodreau S M, Morris M. ERGM:A package to fit simulate and diagnose exponential-family models for networks [J]. Journal of Statistical Software, 2008,24(3):1-29.
[16]	Nowicki K, Snijders T. Estimation and prediction for stochastic blockstructure [J]. Journal of the American statistical Association, 2001,96(455):1077-1087.
[17]	戢晓峰,白淑敏,陈方.效率视角下省域交通碳排放配额分配研究[J]. 干旱区资源与环境, 2022,36(4):1-7. Ji X F, Bai S M, Chen F. Study on carbon emission quota allocation of provincial transportation from the perspective of efficiency [J]. Journal of Arid Land Resources and Environment, 2022,36(4):1-7.
[18]	王世进,蒯乐伊.中国交通运输业碳排放驱动因素与达峰路径[J]. 资源科学, 2022,44(12):2415-2427. Wang S J, Kuai L Y. Driving factors and peaking path of CO2 emissions for China's transportation sector [J]. Resources Science, 2022,44(12):2415-2427.
[19]	张诗青,王建伟,郑文龙.中国交通运输碳排放及影响因素时空差异分析[J]. 环境科学学报, 2017,37(12):4787-4797. Zhang S Q, Wang J W, Zhang W L. Spatio-temporal difference of transportation carbon emission and its influencing factors in China [J]. Acta Scientiae Circumstantiae, 2017,37(12):4787-4797.
[20]	袁长伟,赵潇,孙璐.中国交通运输碳排放效率测度及收敛性研究[J]. 环境科学与技术, 2019,42(12):222-229. Yuan C W, Zhao X, Sun L. Research on measurement and convergence of transport carbon emission efficiency in China [J]. Environmental Science & Technology, 2019,42(12):222-229.
[21]	范育洁,曲建升,张洪芬,等.西北五省区交通碳排放现状及影响因素研究[J]. 生态经济, 2019,35(9):32-37,67. Fan Y J, Qu J S, Zhang H F et al. Study on the current situation and influence factors of transportation carbon emissions in five northwest provinces [J]. Ecological Economy, 2019,35(9):32-37,67.
[22]	吕雁琴,范天正,张晋宁.中国交通运输碳排放效率的时空异质性及影响因素研究[J]. 生态经济, 2023,39(3):13-22. Lv Y Q, Fan T Z, Zhang J N. Spatiotemporal characteristics and influencing factors of China's transport sector garbon emissions efficiency [J]. Ecological Economy, 2023,39(3):13-22.
[23]	王晓平,冯庆,宋金昭.成渝城市群碳排放空间关联结构演化及影响因素[J]. 中国环境科学, 2020,40(9):4123-4134. Wang X P, Feng Q, Song J Z. The spatial association structure evolution of carbon emissions in chengdu-chongqing urban agglomeration and its influence mechanism [J]. China Environmental Science, 2020,40(9):4123-4134.
[24]	张同斌,孙静."国际贸易-碳排放"网络的结构特征与传导路径研究[J]. 财经研究, 2019,45(3):114-126. Zhang T B, Sun J. Study on the structural characteristics and transmission paths of the "International trade-carbon emission" network [J]. Journal of Finance and Economics, 2019,45(3):114-126.
[25]	侯传璐,覃成林.中国省际贸易网络的特征及影响因素——基于铁路货运流量数据及指数随机图模型的分析[J]. 财贸经济, 2019,40(3):116-129. Hou C L, Qin C L. Characteristics and influencing factors of China's inter-provincial trade network:An analysis based on railway freight flow data and exponential random graph model [J]. Finance & Trade Economics, 2019,40(3):116-129.
[26]	马越越.低碳视角下中国区域物流产业全要素生产率的空间溢出效应研究[J]. 宏观经济研究, 2016,217(12):90-101,144. Ma Y Y. Study on the spatial spillover effect of total factor productivity of China's regional logistics industry from the perspective of low carbon [J]. Macroeconomic Research, 2016,217(12):90-101, 144.
[27]	孙瑾,杨英俊.中国与"一带一路"主要国家贸易成本的测度与影响因素研究[J]. 国际贸易问题, 2016,401(5):94-103. Sun J, Yang Y J. Study on measurement and influencing factors of trade cost between China and major countries of the belt and road initiative [J]. International Trade Issues, 2016,401(5):94-103.
[28]	江小涓,孟丽君.内循环为主、外循环赋能与更高水平双循环——国际经验与中国实践[J]. 管理世界, 2021,37(1):1-19. Jiang X J, Meng L J. Internal circulation is dominant, external circulation enables and higher level double circulation:international experience and Chinese practice [J]. Management World, 2021,37(1):1-19.
[29]	汤维祺,吴力波,钱浩祺.从"污染天堂"到绿色增长——区域间高耗能产业转移的调控机制研究[J]. 经济研究, 2016,51(6):58-70. Tang W Q, Wu L B, Qian H Q. From "pollution paradise" to green growth:A study on the regulation mechanism of the transfer of energy-consuming industries between regions [J]. Economic Research, 2016,51(6):58-70.
[30]	张明,李曼.经济增长和环境规制对雾霾的区际影响差异[J]. 中国人口·资源与环境, 2017,27(9):23-34. Zhang M, Li M. Study on the regional difference in the relationship among haze pollution, economic growth and environmental regulation from the perspective of spatial gravitational effect [J]. China Population, Resources and Environment, 2017,27(9):23-34.
[31]	Hao H, Liu Z W, Zhao F Q. An overview of energy efficiency standards in China's transport sector. Renew [J]. Sustain. Energy Rev., 67, 2017:246-256.
[32]	侯志强.交通基础设施对区域旅游经济增长效应的实证分析——基于中国省域面板数据的空间计量模型[J]. 宏观经济研究, 2018, 235(6):118-132. Hou Z Q. Empirical analysis of the effect of transportation infrastructure on regional tourism economic growth:a spatial econometric model based on China's provincial panel data [J]. Research on Macroeconomics, 2018,235(6):118-132.
[33]	盛科荣,张红霞,赵超越.中国城市网络关联格局的影响因素分析——基于电子信息企业网络的视角[J]. 地理研究, 2019,38(5):1030-1044. Sheng K R, Zhang H X, Zhao C Y. Analysis of influencing factors of urban network correlation pattern in China:Based on the perspective of electronic information enterprise network [J]. Geographical Research, 2019,38(5):1030-1044.