Study on Measuring Method of Vehicle Carbon Emission in Expressway Network

doi:10.12141/j.issn.1000-565X.220019

Abstract

Abstract:

Under the background of dual-carbon policy, it is imperative to maintain ecological balance and reduce carbon emissions. Nowadays, it is very important to accurately measure the carbon emission index of vehicles on a wide range of road networks. Therefore, this study proposed a measuring method of vehicle carbon emission in expressway network based on multi-source data fusion. Firstly, a basic data cleaning method for carbon emission statistics was proposed to clean the basic data required for subsequent carbon emission calculation. Secondly, the highway carbon emission calculation model was established, and then the related calculation process was designed. Finally, taking the whole highway network of Guangdong province as an example, this paper calculated the vehicle carbon emission from September 2020 to June 2021, and compared the calculation results with China’s carbon accounting database.Through this method, the proposed method was proved to be scientific and reliable. The research shows that the average carbon emission of mini buses in Guangdong province is small, but the total carbon emission accounts for the largest proportion of all types of vehicles, up to 52.1%; the total carbon emission of gasoline vehicles accounts for 49.8%, which is higher than that of diesel vehicles (45.4%) and of other energy vehicles (4.8%). Vigorously promoting new energy vehicles can effectively reduce the carbon emission of expressways. In addition, the study finds that there are significant differences in the travel patterns of different vehicles under COVID-19, but the overall impact on the transportation economy is limited.

Key words: carbon emission measurement, big data, expressway, new energy vehicle, dual-carbon policy

CLC Number:

U491

LIN Xukun, ZHANG Yang, LUO Zhiqing, et al. Study on Measuring Method of Vehicle Carbon Emission in Expressway Network[J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(9): 22-28.

Figures/Tables 9

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References 17

1	郭胜．城市交通系统碳排放评估体系与评价方法研究［D］．合肥：合肥工业大学，2014.
2	张秀媛，杨新苗，闫琰．城市交通能耗和碳排放统计测算方法研究［J］．中国软科学，2014（6）：142-150.
	ZHANG Xiuyuan， YANG Xinmiao， YAN Yan ．Research on energy consumption and carbon emission of urban transportation ［J］．China Soft Science，2014（6）：142-150.
3	徐志，邹哲，曹伯虎．城市客运交通碳排放水平估算及低碳途径—以天津市为例［J］．北京工业大学学报，2013，39（7）：1007-1013，1020.
	XU Zhi， ZOU Zhe， CAO Bohu ．Carbon emission level estimation and low-carbon approach of urban passenger transport：a case study of tianjin ［J］．Journal of Beijing University of Technology，2013，39（7）：1007-1013，1020.
4	Intergovernmental Panel on Climate Change ．2006 IPCC guidelines for national greenhouse gas Inventories ［M］．Japan：Institute for Global Environmental Strategies （IGES），2006.
5	景国胜，张海霞，廖文苑．城市交通碳排放测算研究——以广州市为例［C］∥创新驱动与智慧发展——2018年中国城市交通规划年会论文集．青岛：［s.n.］，2018：3453-3463.
6	郭耀，何保红．生命周期视角下的建成环境对居民出行碳排放影响研究—以昆明市为例［J］．物流科技，2021，44（4）：78-81，87．
	GUO Yao， HE Baohong. Impacts of built environment on residents' travel carbon emissions from the perspective of life cycle：a case study of kunming city ［J］．Logistics Science and Technology，2021，44（4）：78-81，87.
7	CAI Hao， XIE Shaodong ．Estimation of vehicular emission inventories in China from 1980 to 2005［J］．Atmospheric Environment，2007，39（41）：8963-8979.
8	景侨楠，侯慧敏，白宏涛，等．自上而下的城市能源消耗碳排放估算方法［J］．中国环境科学，2019，39（1）：420-427．
	JING Qiaonan， HOU Huimin， BAI Hongtao，et al ．A top-down estimation method of urban energy consumption carbon emissions［J］．Chinese Environmental Science，2019，39（1）：420-427.
9	CHANG Xiaomeng， CHEN Biyu， LI Qingquan，et al ．Estimating real-time traffic carbon dioxide emissions based on intelligent transportation system technologies［J］．IEEE Transactions on Intelligent Transportation Systems，2013，14（1）：469-479.
10	姬文哲．天津市交通碳排放计算与减排对策研究［D］．天津：天津大学，2014.
11	ANIL N G M R D ．TEMMS：an integrated package for modelling and mapping urban traffic emissions and air quality［J］．Environmental Modelling & Software，2002，17（2）：177-188.
12	LE M， YAN C H ．Study on transportation energy carbon emission based on system dynamics［C］∥2021 9th International Conference on Traffic and Logistic Engineering（ICTLE）.［S.l.］：［s.n.］，2021：102-107.
13	张清，陶小马，杨鹏．特大型城市客运交通碳排放与减排对策研究［J］．中国人口·资源与环境，2012，22（1）：35-42.
	ZHANG Qing， TAO Xiaoma， YANG Peng，et al ．Research on carbon emission and emission reduction strategy of passenger transport in mega-city ［J］．China Population，Resources and Environment，2012，22（1）：35-42.
14	方晓丽，骆勇．城市交通碳排放计算方法比较研究［J］．交通节能与环保，2017，13（4）：81-83.
	FANG Xiaoli， LUO Yong ．Comparative study on calculation methods of urban transportation carbon emission ［J］．Transportation Energy Conservation and Environmental Protection，2017，13（4）：81-83.
15	国家统计局．公路水路交通运输企业一套表统计调查制度［EB/OL］［2020-12-06］．．cn/tjfw/bmdcxmsp/bmzd/202012/t20201216_1809589．
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16	中华人民共和国国家发展改革委员会．《陆上交通运输企业温室气体排放核算方法与报告指南（试行）》［EB/OL］［2015-07-06］．.
17	广东省交通运输厅．32年，从0到1万公里！广东高速公路总里程连续7年全国第一［EB/OL］［2021-04-21］．.

分类	车辆规格
大型客车	车长大于等于6 m或者乘坐人数大于等于20人的客车，包括公交车
中型客车	车长小于6 m且乘坐人数为10-19人的客车
微型小型客车	车长小于6 m且乘坐人数小于等于9人的客车（含轿车）
重型载货汽车	总质量大于等于12 t的货车
中型载货汽车	车长大于等于6 m或者总质量大于等于4.5 t且小于12 t的货车
微型轻型载货汽车	车长小于6 m且总质量小于4.5 t的货车
重型专项作业车	总质量大于等于12 t的专项作业车
中型专项作业车	车长大于等于6 m或者总质量大于等于4.5 t且小于12 t的专项作业车
微型轻型专项作业车	车长小于6 m且总质量小于4.5 t的专项作业车

燃料种类	换算单位	标准煤/kg
汽油	1 L	1.07
柴油	1 L	1.25
电	1 kW·h	0.12
液化石油气	1 t	1.24
压缩天然气	1 m³	1.33
液化天然气	1 kg	1.76
乙醇汽油	1 L	1.07
燃料油	1 kg	1.43

车型	车辆数/辆	理论能耗修订系数
大型客车	1 135	1.24
中型客车	36	1.29
微型、小型客车	116	1.34
重型货车	3 534	0.99
中型货车	233	1.27
轻型货车	655	1.87

时间	总车次/万辆	能耗/万吨标煤	碳排放量/Mt
2020-09	19 331.8	117.2	2.520
2020-10	21 674.1	140.1	2.879
2020-11	19 572.3	120.3	2.577
2020-12	20 395.9	131.7	2.636
2021-01	19 507.1	118.6	2.567
2021-02	12 273.3	88.0	1.884
2021-03	20 118.3	127.4	2.704
2021-04	19 414.6	115.0	2.599
2021-05	19 532.4	119.4	2.560
2021-06	16 441.5	104.3	2.189

[1]	LIN Peiqun, ZHANG Yang, LUO Zhiqing, et al. Precise Calculation Method of Traffic Carbon Emission in Expressway Segment Integrating Multi-Source Data [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(7): 100-108.
[2]	WANG Linhong, LI Hongtao, LI Ruonan. Design of Speed Limit at Expressway in Rainy Day Considering Drivers’ Visual Search Ability [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(6): 20-29.
[3]	WU Jiaorong, HUANG Zhengwen, DENG Yongqi . Development Law of Traffic Network Density in the Spatial Structure of Metropolitan Area Hierarchy [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(2): 111-121.
[4]	WEN Huiying, LI Qiuling, ZHAO Sheng. Research on Spatiotemporal Characteristic and Risk of Lane-Changing Behaviors of Large Vehicles in Expressway Merging Area [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(5): 11-21.
[5]	XIE Jiehui, NIU Fujun, PENG Zhiyu, et al. Deformation Law and Settlement Prediction Application of Soft Soil Subgrade in Coastal Expressway [J]. Journal of South China University of Technology (Natural Science Edition), 2021, 49(4): 97-107.
[6]	YAO Shushen WENG Xiaoxiong LI Feiyu. Analysis of Commuting Mode Based on Behavior Dynamics of Time Characteristics [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(9): 53-60.
[7]	ZHENG Meiguang YANG Jiao CHANG Chenglong HU Zhigang. Unstable Data Partition Recognition Algorithm for Unstructured Cloud Data Management System [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(8): 105-112.
[8]	MA Zhiliang TENG Mingkun REN Yuan. Building Energy Consumption Information Model for Big Data Analysis [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(12): 72-77,91.
[9]	FU Xinsha DU Jintao HE Shijian. Effects of Guide Distance and Initial Speed on Driving Behavior at Expressway Exit [J]. Journal of South China University of Technology (Natural Science Edition), 2019, 47(11): 1-9.
[10]	CHEN Yongheng XIONG Shuai BAI Qiaowen LIU Xinshan TAO Chuqing . Analysis on Merging Position Distribution Characteristics of On-ramp Vehicles at Urban Expressway [J]. Journal of South China University of Technology (Natural Science Edition), 2018, 46(11): 125-131.
[11]	MA Ru-jin XU Shi-qiao WANG Da-lei CHEN Ai-rong. Big Data-Based Fatigue Life Analysis of Steel Box Girder in Large-Span Suspension Bridge [J]. Journal of South China University of Technology (Natural Science Edition), 2017, 45(6): 66-73.
[12]	LUO Yong-gang CHEN Xing-shu YANG Lu. MapReduce Job Performance Tuning by Optimizing Memory Configurations [J]. Journal of South China University of Technology (Natural Science Edition), 2017, 45(1): 102-111.
[13]	HE Yan-xiang LIU Jian-bo SUN Song-tao. Neural Network-Based Public Opinion Prediction Method for Microblog [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(9): 47-52.
[14]	YANG Qing-fang WEI Xue-wu LIN Ci-yun LI Zhi-lin LIU Xiang-yu. Reliability Prediction of Travel Time Based on Spatio-Temporal Bayesian Model [J]. Journal of South China University of Technology (Natural Science Edition), 2016, 44(4): 115-122.
[15]	. Estimationof Expressway Section Travel Time Based on Correction Algorithm [J]. Journal of South China University of Technology (Natural Science Edition), 2015, 43(4): 20-27.