综合智慧能源 ›› 2023, Vol. 45 ›› Issue (8): 26-35.doi: 10.3969/j.issn.2097-0706.2023.08.004

• 双碳体系 • 上一篇    下一篇

不同能源策略下北京市私有机动车辆CO2排放系统仿真模拟

何沭纬1,2(), 韩颖慧1,3,*(), 徐文斌4(), 张元勋2,*(), 单玉龙3(), 余运波3()   

  1. 1.中国科学院大学 资源与环境学院,北京 100049
    2.中国科学院大学 燕山地球关键带与地表通量观测研究站,北京 101408
    3.中国科学院生态环境研究中心,北京 100085
    4.北京环境特性研究所,北京 100854
  • 收稿日期:2023-06-02 修回日期:2023-06-14 出版日期:2023-08-25 发布日期:2023-08-22
  • 通讯作者: *韩颖慧(1978),女,副教授,硕士生导师,博士,从事温室气体核算、监测与减排控制、电化学储能、综合能源管理等方面的研究,hanyinghui@ucas.ac.cn;张元勋(1971),男,教授,博士生导师,博士,从事大气化学、环境监测仪器与分析方法开发、环境健康、生态环境损害鉴定评估等方面的研究,yxzhang@ucas.edu.cn
  • 作者简介:何沭纬(1997),男,在读博士研究生,从事常规污染物与温室气体协同减排及核算、区块链技术在温室气体交易的应用、系统动力学模型与最优化模型耦合决策等方面的研究, heshuwei22@mails.ucas.ac.cn
    徐文斌(1986),男,副研究员,博士,从事温室气体检测及危险化学气体泄漏监测等方面的研究,xuwb@aircas.ac.cn
    单玉龙(1990),男,副研究员,硕士生导师,博士,从事移动源污染排放控制基础与应用方面的研究,ylshan@rcees.ac.cn
    余运波(1971),男,研究员,博士生导师,博士,从事机动车碳污一体化减排控制方面的研究,ybyu@rcees.ac.cn
  • 基金资助:
    国家重点研发计划项目(2022YFE0209500)

Simulation for CO2 emissions from private vehicles in Beijing under different energy strategies

HE Shuwei1,2(), HAN Yinghui1,3,*(), XU Wenbin4(), ZHANG Yuanxun2,*(), SHAN Yulong3(), YU Yunbo3()   

  1. 1. College of Resources and Environment Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
    2. Yanshan Earth Critical Zone and Surface Flux Observation and Research Station, University of Chinese Academy of Sciences, Beijing 101408, China
    3. Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
    4. Science and Technology on Optical Radiation Laboratory, Beijing Institute of Environmental Characteristics, Beijing 100854, China
  • Received:2023-06-02 Revised:2023-06-14 Online:2023-08-25 Published:2023-08-22
  • Supported by:
    National Key Research and Development Program of China(2022YFE0209500)

摘要:

在全球气候变暖的背景下,低碳排放已经成为公众关注的焦点。为减少北京市私有机动车辆的碳排放,需要研究不同能源策略下北京市私人所有车辆碳排放趋势。对北京市私有车辆的碳排放特性进行了深入的研究,通过收集北京市私有车辆数量、车辆能源消耗、能源排放因子以及电力生产排放等数据,使用系统动力学方法建立了北京市私人乘用车、私人货运车辆以及其他用途车辆碳排放模型,重点研究了2030—2060年的碳排放情况。模型计算了基准情景、燃料替代情景、清洁能源情景和燃料替代与清洁能源双重实施情景下2010—2060年间北京市私有机动车辆的碳排放量。研究结果显示:4种情景下都能实现碳达峰,但在现有规划下,基准情景很难在2030年实现碳达峰,而其他情景则能加速碳达峰的到来;到2060年时,与基准情景相比,清洁能源情景减少了5.7%的碳排放量,燃料替代情景减少了20.0%的碳排放量,燃料替代与清洁能源双重实施情景减少了34.3%的碳排放量;单独从能源消费端实施燃料替代行动比单独从能源生产端实施清洁能源行动更有效,但双重实施情景的碳减排效果超过了两者单独实施情景之和,产生了协同效应。敏感性分析显示,能源消费端燃料替代、能源生产端清洁能源发电以及柴油机和汽油机的燃烧效率是影响碳排放的主要因素。研究结果可为决策者提供有启发的信息,推动北京市未来的能源和交通领域实现低碳发展,为北京市能源和交通领域的碳排放研究提供新的视角。

关键词: 碳排放:私有机动车辆, 系统动力学, 能源政策, 情景模拟, 碳达峰, 清洁能源

Abstract:

In the context of global warming and climate change, reduction of carbon emissions has become a major concern. To solve this problem,the carbon emissions from private vehicles in Beijing with different energy policies are analysed. The number of private vehicles,energy consumptions of vehicles, emission coefficient of vehicles and carbon emissions from power generation are intensively studied. The carbon emission models of private vehicles, freight cars and vehicles for various purposes in Beijing are established by system dynamics, and their carbon emissions from 2030 and 2060 are studied. The models calculate the carbon emissions from private vehicles in Beijing between 2010 and 2060 under four scenarios: the basic scenario, electric vehicle (EV) substitution, clean energy, and combined utilization of EV substitution and clean energy. Although the carbon peaking can be achieved under all the four scenarios, but the goal can hardly be achieved by 2030 under the basic scenario. The other three scenario can accelerate the pursuit of the goal. Compared with the carbon emissions under the basic scenario, the carbon emissions from vehicles under clean energy scenario can be reduced by 5.7%, that under EV substitution scenario can be reduced by 20.0%, and that with combined utilization of EV substitution and clean energy can be reduced by 34.3%. It can be seen that the EV substitution strategy implemented at user end is prior to the clean energy strategy implemented at source end. However, the combination of clean energy strategy and EV substitution strategy demonstrates a synergistic effect in carbon emission reduction, surpassing the sum of the reduction made by the two strategies. The sensitivity analysis reveals that EV substitution at consumption end, clean energy generation at source side, and the combustion efficiency of diesel and gasoline engines are the primary factors influencing carbon emissions. This study provides insightful information for decision-makers, promoting low-carbon development of energy and transportation sectors in Beijing, and offering a new perspective for researches on carbon emissions from these two sectors.

Key words: carbon emissions, private vehicle, system dynamics, energy policy, scenarios simulation, carbon peak, clean energy

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