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城市轨道交通碳排放效率与影响因素研究
引用本文:周琪,梁肖,黄俊生,王海鹏,毛保华.城市轨道交通碳排放效率与影响因素研究[J].交通运输系统工程与信息,2023,23(1):30-38.
作者姓名:周琪  梁肖  黄俊生  王海鹏  毛保华
作者单位:1. 北京交通大学,综合交通运输大数据应用技术交通运输行业重点实验室,北京 100044; 2. 交通运输部,公路科学研究院,北京 100088
基金项目:中央高校基本科研业务费专项资金 (2021YJS095);国家自然科学基金 (71971021)。
摘    要:从静态和动态两方面综合分析我国城市轨道交通的碳排放效率,以探索高效、绿色的城市轨道交通发展路径。首先,利用“自上而下”的方法测算城市轨道交通系统的碳排放量,构建涵盖车辆、人力、能源、环境、运输效益的要素体系;然后,运用考虑非期望产出的超效率SBM(Slack Based Model)模型测度我国23个省会城市轨道交通碳排放效率,利用方向性距离函数构建GML (Global Malmquist-Luenberger)指数分析碳排放效率的动态变化特性;最后,采用面板模型厘清碳排放效率的影响因素。模型计算结果表明:城市轨道交通碳排放效率总体呈现与网络规模正相关的差异化态势,不同类型城市轨道交通碳排放效率GML及其分解指数的变化特征有所差异; 规模效率、技术进步对城市轨道交通碳排放效率具有提升作用,规模效率、技术进步指数每上升 1%,碳排放效率GML指数分别提高1.906%、2.338%,火力发电比例对碳排放效率的提升有一定的抑制作用;随着城市轨道交通网络的发展,碳排放效率的提升仍需要技术进步的推动。最后,针对不同类型城市轨道交通提出了提升碳排放效率的策略要点。

关 键 词:城市交通  碳排放效率  超效率SBM  城市轨道交通  面板模型  GML指数  
收稿时间:2022-08-03

Carbon Emission Efficiency and Influencing Factors Analysis of Urban Rail Transits
ZHOU Qi,LIANG Xiao,HUANG Jun-sheng,WANG Hai-peng,MAO Bao-hua.Carbon Emission Efficiency and Influencing Factors Analysis of Urban Rail Transits[J].Transportation Systems Engineering and Information,2023,23(1):30-38.
Authors:ZHOU Qi  LIANG Xiao  HUANG Jun-sheng  WANG Hai-peng  MAO Bao-hua
Institution:1. Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport, Beijing Jiaotong University, Beijing 100044, China; 2. Research Institute of Highway, Ministry of Transport, Beijing 100088, China
Abstract:To explore an efficient and green development pathway of urban rail transit, the carbon emission efficiency of China's urban rail transits is comprehensively analyzed from both static and dynamic aspects. First, we use the "topdown" method to measure the carbon emissions and build a system covering vehicles, human resources, energy, environment, and transport benefits. Then, a super efficiency Slack Based Model (SBM) model considering unexpected output is used to measure the carbon emission efficiency of rail transits in 23 provincial capitals in China, and the Global Malmquist Lounberger (GML) index is constructed using the directional distance function to analyze the dynamic characteristics of carbon emission efficiency. Finally, the panel model is used to clarify the influencing factors of carbon emission efficiency. The results indicate that the carbon emission efficiency of urban rail transit shows a positive correlation with the network scale. The changing characteristics of carbon emission efficiency GML and its decomposition index are different with different types of urban rail transits. Scale efficiency, technological progress and passenger turnover can improve the carbon emission efficiency. An increase of 1% in the growth rate of scale efficiency and technological progress can result in the carbon emission efficiency GML index increased by 1.906% and 2.338%, respectively. The proportion of thermal power generation has a certain inhibitory effect on the improvement of carbon emission efficiency. With the development of the urban rail transits, the improvement of carbon emission efficiency still needs technological progress. Finally, the main directions to improve carbon emission efficiency are proposed for different types of urban rail transits.
Keywords:urban traffic  carbon emission efficiency  super efficiency SBM  urban rail transit  panel model  GML index  
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