Measurement and allocation methods for carbon emissions from vehicles

Yan Shengyu; Hao Shijie; Wang Shangjun; Zhao Chenglong; Zhao Lingyu

doi:10.11714/acta.snus.ZR20260096

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Measurement and allocation methods for carbon emissions from vehicles

Research articles | 更新时间：2026-10-20

- Measurement and allocation methods for carbon emissions from vehicles
- Acta Scientiarum Naturalium Universitatis Sunyatseni Vol. 65, Issue 5, Pages: 129-136(2026)
- 作者机构：
  
  长安大学汽车学院，陕西西安 710018
- 作者简介：
- 基金信息：
- DOI：10.11714/acta.snus.ZR20260096
  CLC： U121
- Received：15 April 2026，
  
  Accepted：12 May 2026，
  
  Online First：26 June 2026，
  
  Published：25 September 2026
- 稿件说明：
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闫晟煜,郝时杰,王赏军等.城市公交车辆碳排放量测算与线网分配方法[J].中山大学学报(自然科学版)(中英文),2026,65(05):129-136.

Yan Shengyu,Hao Shijie,Wang Shangjun,et al.Measurement and allocation methods for carbon emissions from vehicles[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(05):129-136.
闫晟煜,郝时杰,王赏军等.城市公交车辆碳排放量测算与线网分配方法[J].中山大学学报(自然科学版)(中英文),2026,65(05):129-136. DOI： 10.11714/acta.snus.ZR20260096.

Yan Shengyu,Hao Shijie,Wang Shangjun,et al.Measurement and allocation methods for carbon emissions from vehicles[J].Acta Scientiarum Naturalium Universitatis Sunyatseni,2026,65(05):129-136. DOI： 10.11714/acta.snus.ZR20260096.

摘要

提出了一种基于“自上而下”法的车辆碳排放量测算与路网分配方法。利用公交企业车辆能耗统计数据与碳排放因子，研究了纯电动车（BEV）、插电式混合动力电动车（PHEV）和传统燃料车辆的碳排放量测算方法。依托高频GPS数据中经纬度和车辆速度参数，以15 km/h作为行驶状态判定阈值，基于驱动系数和附属设备时间惩罚系数，确定每个轨迹点的相对碳排放惩罚权重；利用空间拥堵惩罚系数修正路段属性，运用改进的网络核密度函数对公交线网的路段赋值，通过计算各路段的归一化权重，将车辆碳排放总量按照权重分配到公交线网上，确定各路段的碳排放强度。研究表明：经实例验证，本文提出的公交车辆碳排放量测算与线网分配方法可行；实例城市2024年公交车辆碳排放量为20.43万t，中型BEV、大型BEV的每100 km碳排放量分别仅为同等车长CNG燃料车型的59.10%、65.35%；当车速低于15 km/h时，相对碳排放惩罚权重呈非线性拖尾分布特征；高碳排放区域显著集中于车流量大、站点密、线网密度高、信号灯密集的拥堵路段。研究成果可支撑面向绿色低碳的公交线网优化与局部高碳排放路段治理。

Abstract

A top-down approach for measuring vehicle carbon emissions and allocating them to the road network was proposed. Utilizing the statistical energy consumption data of vehicles from public transit enterprises and carbon emission factors， calculation methods for battery electric vehicles （BEVs）， plug-in hybrid electric vehicles（PHEVs）， and traditional fuel vehicles were studied. Relying on the longitude， latitude， and vehicle speed parameters from high-frequency GPS data，

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120642265&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120642264&type=

12.86933327

2.79399991

was set as the threshold for determining the driving state. Based on the driving coefficient and the auxiliary equipment time penalty coefficient， the relative carbon emission penalty weight for each trajectory point was determined. Furthermore， the spatial congestion penalty coefficient was utilized to correct road segment attributes， and an improved network kernel density function was applied to assign values to the road segments of the transit network. By calculating the normalized weight of each road segment， the total carbon emissions of vehicles were allocated to the transit network according to the weights， thereby determining the carbon emission intensity of each road segment. The results indicate that the proposed measurement and network allocation method for public transit vehicle carbon emissions is feasible， as verified by a case study. The total carbon emissions of public transit vehicles in the example city in 2024 are

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120642290&type=

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=120642282&type=

11.93799973

2.70933342

tons. The carbon emissions per 100 km of medium-sized BEVs and large BEVs account for only 59.10% and 65.35% of those of compressed natural gas（CNG） vehicles of the same length， respectively. When the vehicle speed is lower than 15 km/h， the relative carbon emission penalty weight exhibits a non-linear heavy-tailed distribution characteristic. High carbon emission areas are significantly concentrated in congested road segments characterized by heavy traffic volume， dense transit stops， high network density， and frequent traffic lights. The research results will support the optimization of green and low-carbon public transit networks and the targeted governance of local high-carbon-emission road segments.

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references

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