交通运输系统工程与信息 ›› 2020, Vol. 20 ›› Issue (2): 8-12.

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缓解城市交通拥堵的CO2减排效益评估方法研究

李振宇*,廖凯,崔占伟,刘洋   

  1. 交通运输部科学研究院,城市公共交通智能化交通运输行业重点实验室,北京 100029
  • 收稿日期:2019-11-07 修回日期:2020-01-09 出版日期:2020-04-25 发布日期:2020-04-30
  • 作者简介:李振宇(1975-),男,山西长治人,研究员.
  • 基金资助:

    全球环境基金/Global Environment Facility(TF 014206-CN);中央科研院所基本科研业务费专项资金/ Fundamental Research Funds for the Central Scientific Institutions of China(20194809).

An Evaluation Method of CO2 Emissions Reduction in Urban Traffic Congestion Mitigation

LI Zhen-yu, LIAO Kai, CUI Zhan-wei, LIU Yang   

  1. Key Laboratory of Intelligent Urban Public Transportation in Transportation Industry, China Academy of Transportation Sciences, Beijing 100029, China
  • Received:2019-11-07 Revised:2020-01-09 Online:2020-04-25 Published:2020-04-30

摘要:

城市交通拥堵是导致城市交通碳排放快速增长的重要原因. 分析影响城市交通碳排放的关键因素,收集现有碳排放因子,开展车辆排放测试,建立不同道路类型和服务水平的城市交通碳排放因子数据库. 对ASIF 方法进行改进,基于城市交通拥堵的分类方法,提出中观尺度的城市交通缓堵减排效益评估方法和模型. 以成都市为例,开展城市调研、数据采集与分析、模型运算,评估成都全年城市交通的碳排放总量,其中,交通拥堵的碳排放占48.2%. 设置4种正向减排发展情景和3种反向恶化发展情景,分析不同情景下的碳排放效果.

关键词: 城市交通, 碳排放影响, 评估模型, 交通拥堵, 排放因子

Abstract:

Urban traffic congestion has been one of the important reasons for the rapid growth of carbon emissions of urban transport. Firstly, this paper analyzes the key factors affecting the carbon emissions of urban transport. Secondly, by collecting the existing carbon emission factors and conducting vehicle emission tests, a database of urban transport carbon emission factors for different road types and service levels has been established. Based on the classification method of urban traffic congestion, the ASIF method is improved. And a meso-scale assessment method and model are proposed for the benefit evaluation on carbon emission reduction from traffic congestion mitigation. Lastly, taking Chengdu as an example, the city survey, data collection and analysis, and the calculation are carried out using the model. The total carbon emissions of urban transport in Chengdu are evaluated throughout the year, of which 48.2% were carbon emissions from traffic congestion. By setting four positive development scenarios and three negative development scenarios, and the carbon emission effects under different scenarios are analyzed.

Key words: urban traffic, impact on carbon emission, evaluation model, traffic congestion, emission factors

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