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
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- 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

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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

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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

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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。

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