国家中心城市交通碳排放效率的空间网络结构及动因研究

杨青; 吴向荣; 刘洋; 郑衍迪

doi:10.12153/j.issn.1674-991X.20240052

国家中心城市交通碳排放效率的空间网络结构及动因研究

doi: 10.12153/j.issn.1674-991X.20240052

武汉理工大学安全科学与应急管理学院

基金项目: 国家自然科学基金面上项目（72374164）

详细信息

作者简介:
杨青（1962—），男，教授，博士，主要从事复杂系统智能管理研究，yangq@whut.edu.cn

通讯作者:
郑衍迪（1990—），男，博士研究生，主要从事区域经济、低碳交通研究，837325158@qq.com

中图分类号: X32；F50
计量
- 文章访问数: 116
- HTML全文浏览量: 72
- PDF下载量: 17
- 被引次数: 0
出版历程
- 收稿日期: 2024-01-23
- 录用日期: 2024-04-18
- 修回日期: 2024-04-06

Research on spatial network structure and influencing factors of transportation carbon emission efficiency in national central cities in China

School of Safety Science and Emergency Management, Wuhan University of Technology

摘要

摘要:
为科学把握城市交通碳排放效率的空间网络结构，实现交通运输业可持续发展，基于2011—2020年我国9个国家中心城市交通碳排放数据，构建考虑非期望产出的全局超效率SBM模型（GB-US-Super-SBM模型）并测算交通碳排放效率，利用修改的引力模型建立空间关联网络，在此基础上应用社会网络分析方法厘清交通碳排放效率空间网络结构及其动因。结果表明：1）研究期内，9个国家中心城市交通碳排放效率整体水平不高，城市间存在较大差距。2）国家中心城市交通碳排放效率的空间关联呈现网络结构形态，并逐渐形成了天津、西安、郑州等多个网络中心；空间网络关联性以2017年为节点呈现先增强后减弱的趋势；天津、西安、郑州等城市发挥着“桥梁”和“中介”作用，对空间网络的形成发挥了重要作用。3）经济发展水平差异、城镇化水平差异、节能技术水平差异和空间邻接关系等因素在交通碳排放效率的空间网络结构中发挥显著作用，其中空间邻接关系和经济发展水平差异的影响最显著。
- 国家中心城市 /
- 交通碳排放效率 /
- 碳达峰 /
- 碳中和 /
- 空间网络结构
Abstract:
In order to scientifically grasp the spatial network structure of carbon emission efficiency in urban transportation and achieve sustainable development in the transportation industry, based on the data of nine national central cities in China from 2011 to 2020, a global super efficiency SBM model (GB-US-Super-SBM model) considering unexpected output was constructed to assess the transportation carbon emission efficiency; the revised spatial gravity model was used to construct the spatial correlation network and, based on this, the social network analysis method was applied to reveal the spatial network structure of transportation carbon emission efficiency and the influencing factors. The results showed that: (1) During the entire study duration, the overall transportation carbon emission efficiency of nine national central cities was relatively low, and there were considerable gaps among the cities. (2) The spatial correlation of transportation carbon emission efficiency in national central cities presented a network structure, gradually forming multiple network centers such as Tianjin, Xi'an and Zhengzhou; the spatial network of transportation carbon emission efficiency shows a trend of first strengthening and then weakening with 2017 as the node; cities such as Tianjin, Xi'an and Zhengzhou served as "bridges" and "intermediaries", playing a crucial role in the shaping of the spatial network. (3) Variations in economic development, urbanization, energy-saving technology and spatial proximity significantly influenced the spatial configuration of carbon emission efficiency in transportation. Among these factors, spatial proximity and differences in the level of economic development exerted the most substantial impact.
- national central city /
- transportation carbon emission efficiency /
- carbon peak /
- carbon neutrality /
- spatial network structure

HTML全文

图 1 2011—2020年9个国家中心城市交通碳排放效率空间网络拓扑

Figure 1. Topologies of transportation carbon emission efficiency in 9 national central cities of China from 2011 to 2020

下载: 全尺寸图片幻灯片

图 2 交通碳排放效率网络关系数和网络密度

Figure 2. Network connectedness and density of transportation carbon emission efficiency

下载: 全尺寸图片幻灯片

图 3 交通碳排放效率网络等级度和网络效率

Figure 3. Network hierarchy and efficiency of transportation carbon emission efficiency

下载: 全尺寸图片幻灯片

表 1 变量定义

Table 1. Variable definitions

变量	定义
交通碳排放效率空间相关性（GL）	1.2节构建的交通碳排放效率空间网络
经济发展水平差异（ED）	城市之间的人均GDP差异
城镇化水平差异（UL）	城市之间的城镇人口比例差异
交通运输强度差异（TI）	交通运输综合换算周转量与各城市地区生产总值的比值差异
产业结构差异（TS）	城市之间的第三产业总值占地区生产总值比例差异
节能技术水平差异（ET）	交通能源强度倒数差异，即交通运输综合换算周转量与能源消耗量的比值
城市之间的相邻关系（C）	采用0-1法则，两城市邻接记为1，否则为0

下载: 导出CSV

表 2 2011—2020年中国9个中心城市交通碳排放效率

Table 2. Transportation carbon emission efficiency of 9 central cities of China from 2011 to 2020

年份	北京	天津	上海	广州	重庆	成都	武汉	郑州	西安	均值
2011	1.026	0.427	1.028	1.037	0.354	0.419	0.529	1.060	1.198	0.786
2012	0.964	0.491	0.895	0.933	0.355	0.402	0.534	1.036	1.032	0.738
2013	1.002	0.569	0.915	0.896	0.389	0.430	0.540	0.922	0.893	0.729
2014	1.005	0.731	1.027	0.880	0.356	0.414	0.517	1.048	0.822	0.756
2015	0.886	0.837	0.966	0.881	0.349	0.412	0.502	0.887	0.781	0.722
2016	0.819	0.902	0.968	0.778	0.315	0.400	0.484	0.619	1.035	0.702
2017	1.005	1.004	0.966	0.796	0.295	0.397	0.475	0.563	0.798	0.700
2018	1.013	1.014	1.004	0.868	0.281	0.378	0.452	0.517	0.683	0.690
2019	1.002	1.019	1.017	0.944	0.275	0.366	0.468	0.549	0.678	0.702
2020	0.482	1.009	1.009	1.018	0.279	0.415	0.461	0.600	0.735	0.667
均值	0.920	0.800	0.980	0.903	0.325	0.403	0.496	0.780	0.865

下载: 导出CSV

表 3 中国9个中心城市交通碳排放效率网络中心性分析

Table 3. Centrality analysis of spatial correlation network of transportation carbon emission efficiency in 9 national central cities of China

城市	2015年			2017年			2020年
城市	C_AB	C_AP	C_AD	C_AB	C_AP	C_AD	C_AB	C_AP	C_AD
北京	25.000	50.000	0.714	25.000	53.333	0.714	12.500	44.444	0.000
天津	62.500	72.727	18.155	75.000	80.000	22.143	62.500	72.727	36.607
上海	50.000	66.667	9.345	37.500	61.538	4.286	25.000	50.000	1.786
广州	37.500	53.333	2.679	37.500	61.538	0.714	25.000	50.000	0.893
重庆	50.000	66.667	9.345	50.000	66.667	1.429	25.000	50.000	0.893
成都	37.500	57.143	3.393	62.500	72.727	10.833	50.000	66.667	17.857
武汉	50.000	61.538	7.440	37.500	57.143	3.095	37.500	61.538	1.786
郑州	62.500	72.727	18.155	62.500	72.727	13.810	50.000	61.538	11.607
西安	50.000	66.667	9.345	62.500	72.727	10.833	62.500	72.727	25.000

下载: 导出CSV

表 4 我国9个国家中心城市交通碳排放效率空间网络动因分析

Table 4. Analysis of spatial correlation network drivers of transportation carbon emission efficiency in 9 national central cities of China

自变量	QAP相关分析		QAP回归分析
自变量	相关系数	P	回归系数	P
经济发展水平	−0.338	0.020**	−0.277	0.013**
城镇化水平	−0.168	0.099*	−0.161	0.073*
交通运输强度	−0.187	0.092*	0.090	0.190
产业结构	−0.163	0.110	0.225	0.040**
节能技术水平	−0.177	0.096*	−0.188	0.034**
空间邻接关系	0.638	0.000***	0.658	0.000***
注：表示P<0.1，表示P<0.05，**表示P<0.01；R²为0.520，回归分析调整后R²为0.484，显著性水平为0.000。

下载: 导出CSV

参考文献(47)

[1]	刘志林, 秦波. 城市形态与低碳城市: 研究进展与规划策略[J]. 国际城市规划,2013,28(2):4-11. LIU Z L, QIN B. Urban form and low-carbon cities: research progress and planning strategies[J]. Urban Planning International,2013,28(2):4-11.
[2]	住房和城乡建设部城乡规划司, 中国城市规划设计研究院. 全国城镇体系规划 2006—2020年[M]. 北京: 商务印书馆, 2010.
[3]	前瞻产业研究院. 2021年全球及主要国家碳排放市场现状及分析[EB/OL]. (2021-07-13)[2024-01-23]. https://www.qianzhan.com/analyst/detail/220/210713-4d33ef2d.html.
[4]	中央纪委国家监委网站. “双碳”变革: 访中国工程院院士、清华大学碳中和研究院院长贺克斌[EB/OL]. (2022-01-26)[2024-01-23]. https://www.ccdi.gov.cn/yaowenn/202201/t20220126_167199.html.
[5]	李晓易, 谭晓雨, 吴睿, 等. 交通运输领域碳达峰、碳中和路径研究[J]. 中国工程科学,2021,23(6):15-21. doi: 10.15302/J-SSCAE-2021.06.008 LI X Y, TAN X Y, WU R, et al. Paths for carbon peak and carbon neutrality in transport sector in China[J]. Strategic Study of CAE,2021,23(6):15-21. doi: 10.15302/J-SSCAE-2021.06.008
[6]	杨青, 郭露, 刘星星, 等. 基于模体结构与指数随机图的中国省域交通碳排放空间关联格局的驱动要素研究[J/OL]. 中国环境科学: 1-16. [2023-12-04]. https://doi.org/10.19674/j.cnki.issn1000-6923.20231127.026.
[7]	中国城市公共交通协会. 首届中国出行碳大会成功举办: 技术变革加速实现零碳交通愿景[EB/OL]. (2023-05-12)[2024-01-23]. http://cupta.org.cn/news.php?lm=14&ye=6.
[8]	卢建锋, 傅惠, 王小霞. 区域交通运输业碳排放效率影响因素研究[J]. 交通运输系统工程与信息,2016,16(2):25-30. doi: 10.3969/j.issn.1009-6744.2016.02.006 LU J F, FU H, WANG X X. Research on the impact of regional transportation emissions efficiency factors[J]. Journal of Transportation Systems Engineering and Information Technology,2016,16(2):25-30 doi: 10.3969/j.issn.1009-6744.2016.02.006
[9]	国务院. 中共中央国务院印发《交通强国建设纲要》[A/OL]. (2019-09-19)[2024-01-23]. http://www.gov.cn/gongbao/content/2019/content_5437132.htm.
[10]	REN J W, GAO B, ZHANG J W, et al. Measuring the energy and carbon emission efficiency of regional transportation systems in China: chance-constrained DEA models[J]. Mathematical Problems in Engineering,2020,2020:9740704.
[11]	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.
[12]	林秀群, 李嘉新, 李阳, 等. 长江经济带物流业碳排放效率的测度及时空演化特征研究[J]. 生态经济,2022,38(12):31-38. LIN X Q, LI J X, LI Y, et al. Measurement and temporal and spatial evolution of carbon emission efficiency of logistics industry in the Yangtze River Economic Belt[J]. Ecological Economy,2022,38(12):31-38.
[13]	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 factor efficiency for transportation carbon emissions in China[J]. Resources Science,2017,39(4):687-697.
[14]	董梦如, 韩增林, 郭建科. 中国海洋交通运输业碳排放效率测度及影响因素分析[J]. 海洋通报,2020,39(2):169-177. DONG M R, HAN Z L, GUO J K. Measurement of carbon emission efficiency and its influencing factors in China's marine transportation industry[J]. Marine Science Bulletin,2020,39(2):169-177.
[15]	王兆峰, 黄冬春. 长江经济带与黄河流域交通碳排放效率的比较及其影响因素[J/OL]. 经济地理, 2023: 1-15. [2023-12-04]. https://kns.cnki.net/kcms/detail/43.1126.K.20230831.1703.002.html.
[16]	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. LYU Y Q, FAN T Z, ZHANG J N. Spatiotemporal characteristics and influencing factors of China's transport sector carbon emissions efficiency[J]. Ecological Economy,2023,39(3):13-22.
[17]	任梦洋, 黄羿, 付善明, 等. 国家中心城市交通运输业碳排放效率研究[J]. 生态科学,2022,41(1):169-178. REN M Y, HUANG Y, FU S M, et al. The study on carbon emission efficiency of transportation industry in national central city[J]. Ecological Science,2022,41(1):169-178.
[18]	蒋自然, 金环环, 王成金, 等. 长江经济带交通碳排放测度及其效率格局(1985—2016年)[J]. 环境科学,2020,41(6):2972-2980. JIANG Z R, JIN H H, WANG C J, et al. Measurement of traffic carbon emissions and pattern of efficiency in the Yangtze River Economic Belt (1985-2016)[J]. Environmental Science,2020,41(6):2972-2980.
[19]	PENG Z, WU Q, WANG D, et al. Temporal-spatial pattern and influencing factors of China's province-level transport sector carbon emissions efficiency[J]. Polish Journal of Environmental Studies,2020,29(1):233-247.
[20]	MENG C H, DU X Y, ZHU M C, et al. The static and dynamic carbon emission efficiency of transport industry in China[J]. Energy,2023,274:127297. doi: 10.1016/j.energy.2023.127297
[21]	郑琰, 蒋雪梅, 肖玉杰. 交通运输业碳排放效率时空演变及趋势预测[J/OL]. 环境科学: 1-13. [2023-11-17]. https://doi.org/10.13227/j.hjkx.202305261.
[22]	杨扬, 郭挂梅. 基于超效率SBM模型的航空企业碳排放效率研究[J]. 环境工程技术学报,2023,13(5):1779-1786. doi: 10.12153/j.issn.1674-991X.20230095 YANG Y, GUO G M. Research on carbon emission efficiency of aviation enterprises based on super efficiency SBM model[J]. Journal of Environmental Engineering Technology,2023,13(5):1779-1786. doi: 10.12153/j.issn.1674-991X.20230095
[23]	TONE K. Dealing with undesirable outputs in DEA: a slack-based measure (SBM) approach[J]. National Graduate Institute for Policy Studies,2003,7(1):44-45.
[24]	PASTOR J T, LOVELL C K. A global Malmquist productivity index[J]. Economics Letters,2005,88(2):266-271. doi: 10.1016/j.econlet.2005.02.013
[25]	HUANG J H, YANG X G, CHENG G, et al. A comprehensive eco-efficiency model and dynamics of regional eco-efficiency in China[J]. Journal of Cleaner Production,2014,67:228-238. doi: 10.1016/j.jclepro.2013.12.003
[26]	刘华军, 郭立祥, 乔列成, 等. 中国物流业效率的时空格局及动态演进[J]. 数量经济技术经济研究,2021,38(5):57-74. LIU H J, GUO L X, QIAO L C, et al. Spatial-temporal pattern and dynamic evolution of logistics efficiency in China[J]. The Journal of Quantitative & Technical Economics,2021,38(5):57-74.
[27]	SCOTT J. Social network analysis[M]. London: Sage Publication, 2013.
[28]	WASSERMAN S, FAUST K. Social network analysis: methods and applications[M]. Cambridge: Cambridge University Press, 1994.
[29]	刘华军, 刘传明, 孙亚男. 中国能源消费的空间关联网络结构特征及其效应研究[J]. 中国工业经济,2015(5):83-95. LIU H J, LIU C M, SUN Y N. Spatial correlation network structure of energy consumption and its effect in China[J]. China Industrial Economics,2015(5):83-95.
[30]	LIU G Y, YANG Z F, FATH B D, et al. Time and space model of urban pollution migration: economy-energy-environment nexus network[J]. Applied Energy,2017,186:96-114. doi: 10.1016/j.apenergy.2016.06.132
[31]	尚杰, 吉雪强, 石锐, 等. 中国农业碳排放效率空间关联网络结构及驱动因素研究[J]. 中国生态农业学报(中英文),2022,30(4):543-557. doi: 10.12357/cjea.20210607 SHANG J, JI X Q, SHI R, et al. Structure and driving factors of spatial correlation network of agricultural carbon emission efficiency in China[J]. Chinese Journal of Eco-Agriculture,2022,30(4):543-557. doi: 10.12357/cjea.20210607
[32]	王凯, 张淑文, 甘畅, 等. 中国旅游业碳排放效率的空间网络结构及其效应研究[J]. 地理科学,2020,40(3):344-353. WANG K, ZHANG S W, GAN C, et al. Spatial network structure of carbon emission efficiency of tourism industry and its effects in China[J]. Scientia Geographica Sinica,2020,40(3):344-353.
[33]	SONG H H, GU L Y, LI Y F, et al. Research on carbon emission efficiency space relations and network structure of the Yellow River Basin city cluster[J]. International Journal of Environmental Research and Public Health,2022,19(19):12235. doi: 10.3390/ijerph191912235
[34]	张翼. 基于空间关联网络结构的中国省域协同碳减排研究[J]. 统计与信息论坛,2017,32(2):63-69. doi: 10.3969/j.issn.1007-3116.2017.02.010 ZHANG Y. Study on Chinese provincial collaborative carbon reduction based on spatial correlation network structure[J]. Statistics & Information Forum,2017,32(2):63-69. doi: 10.3969/j.issn.1007-3116.2017.02.010
[35]	邵璇璇, 姚永玲. 长江中游城市群的空间网络特征及其影响机制[J]. 城市问题,2019(10):15-26. SHAO X X, YAO Y L. Characteristics of spatial networks and the influencing mechanism of the Middle Reaches of Yangtze River[J]. Urban Problems,2019(10):15-26.
[36]	李婧, 管莉花. 区域创新效率的空间集聚及其地区差异: 来自中国的实证[J]. 管理评论,2014,26(8):127-134. LI J, GUAN L H. The spatial agglomeration of regional innovation efficiency and its disparities: evidence from China[J]. Management Review,2014,26(8):127-134.
[37]	BAI C Q, ZHOU L, XIA M L, et al. Analysis of the spatial association network structure of China's transportation carbon emissions and its driving factors[J]. Journal of Environmental Management,2020,253:109765. doi: 10.1016/j.jenvman.2019.109765
[38]	MA F, WANG Y X, YUEN K F, et al. The evolution of the spatial association effect of carbon emissions in transportation: a social network perspective[J]. International Journal of Environmental Research and Public Health,2019,16(12):2154. doi: 10.3390/ijerph16122154
[39]	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.
[40]	陈思茹, 张帅, 袁长伟. 中国交通运输经济发展与碳排放效率评价[J]. 中国公路学报,2019,32(1):154-161. doi: 10.3969/j.issn.1001-7372.2019.01.017 CHEN S R, ZHANG S, YUAN C W. China's transportation economy development and carbon environmental efficiency evaluation[J]. China Journal of Highway and Transport,2019,32(1):154-161. doi: 10.3969/j.issn.1001-7372.2019.01.017
[41]	单豪杰. 中国资本存量K的再估算: 1952—2006年[J]. 数量经济技术经济研究,2008,25(10):17-31. SHAN H J. Reestimating the capital stock of China: 1952-2006[J]. The Journal of Quantitative & Technical Economics,2008,25(10):17-31.
[42]	蔡博峰, 冯相昭, 陈徐梅. 交通二氧化碳排放和低碳发展[M]. 北京: 化学工业出版社, 2012.
[43]	吴雯, 李玮. 中部六省交通运输业碳排放影响因素分析[J]. 管理现代化,2019,39(1):62-65. WU W, LI W. Analysis on influencing factors of carbon emissions in transportation industry of six provinces in central China[J]. Modernization of Management,2019,39(1):62-65.
[44]	闫树熙, 陈璐. 交通碳排放影响因素分析: 以西安市为例[J]. 统计与决策,2020,36(4):62-66 YAN S X, CHEN L. Analysis of influencing factors oftransportation carbon emissions:Taking Xi'an as an example[J]. Statistics and Decision,2020,36(4):62-66
[45]	张国兴, 苏钊贤. 黄河流域交通运输碳排放的影响因素分解与情景预测[J]. 管理评论,2020,32(12):283-294. ZHANG G X, SU Z X. Analysis of influencing factors and scenario prediction of transportation carbon emissions in the Yellow River Basin[J]. Management Review,2020,32(12):283-294.
[46]	黄杰. 中国能源环境效率的空间关联网络结构及其影响因素[J]. 资源科学,2018,40(4):759-772. HUANG J. The spatial network structure of energy-environmental efficiency and its determinants in China[J]. Resources Science,2018,40(4):759-772.
[47]	吉雪强, 张跃松. 长江经济带种植业碳排放效率空间关联网络结构及动因[J]. 自然资源学报,2023,38(3):675-693. doi: 10.31497/zrzyxb.20230308 JI X Q, ZHANG Y S. Spatial correlation network structure and motivation of carbon emission efficiency in planting industry in the Yangtze River Economic Belt[J]. Journal of Natural Resources,2023,38(3):675-693. □ doi: 10.31497/zrzyxb.20230308