Calculation and Spatial Distribution Characteristics of Carbon Emissions from New Energy Vehicles on Expressways

doi:10.12141/j.issn.1000-565X.230293

Abstract

Abstract:

In order to overcome the difficulty in estimating the indirect carbon emissions from new energy vehicles due to the energy consumption in a large-scale expressway network, this paper proposes a new carbon emission calculation method of new energy vehicles based on the toll data in expressway ETC network. Firstly, the traffic flow data of new energy vehicles on expressways were cleansed and processed. Then, according to the distribution of toll gantries, road sections were defined and segmented. On this basis, the energy consumption of different types of new energy vehicles was analyzed. Furthermore, a carbon emission quantification model of new energy vehicles in the operational stage was established. Finally, by taking Guangdong Province as an example, the carbon emissions of new energy vehicles on expressways were calculated, with its spatial distribution characteristics being also analyzed. The results show that (1) Category 1 pure electric passenger cars are the main source of carbon emissions from new energy vehicles on expressways, accounting for 74.20% of the total carbon emissions of new energy vehicles, followed by Category 1 pure electric trucks, whose carbon emissions account for 14.18% of the total, and Category 1 plug-in hybrid passenger cars contribute 11.62% of the total; (2) the cities in the Pearl River Delta urban agglomeration are the main contributors to high carbon emissions from new energy vehicles, and the less developed regions in eastern, western and northern Guangdong account for less than 13% of the total carbon emissions from new energy vehicles; (3) the road segments with high carbon emissions from new energy vehicles mainly locate in the transportation hubs and inner-city expressway rings of developed cities, and Guangzhou metropolitan area as well as Shenzhen metropolitan area as the center has a radiating effect on the expressway network of surrounding cities; and (4) in less developed areas, due to the low density of expressway network and relatively lagging infrastructure, the penetration rate of new energy vehicles is low, the corresponding carbon emissions from new energy vehicles are generally low.

Key words: expressway, toll data, new energy vehicle, energy consumption, carbon emission

CLC Number:

U491

WEN Huiying, HE Ziqi, HU Yuqing, et al. Calculation and Spatial Distribution Characteristics of Carbon Emissions from New Energy Vehicles on Expressways[J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(8): 1-13.

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References 18

1	赵芳敏，蔡志洲，彭令发．公路碳评价指标体系初探［J］．交通节能与环保，2021，17（6）：1-4，9．
	ZHAO Fangmin， CAI Zhizhou， PENG Lingfa ．Study on highway carbon evaluation index system［J］．Transport Energy Conservation ＆ Environmental Protection，2021，17（6）：1-4，9．
2	HULSHOF D， MULDER M ．Willingness to pay for CO₂ emission reductions in passenger car transport［J］．Environmental and Resource Economics，2020，75（4）：899-929．
3	YANG T， XING C， LI X ．Evaluation and analysis of new-energy vehicle industry policies in the context of technical innovation in China［J］．Journal of Cleaner Production，2021，281：125126/1-18．
4	王芳，邱彬．2023年新能源汽车发展形势研判［J］．智能网联汽车，2023（2）：64-68．
	WANG Fang， QIU Bin ．Analysis of the development situation of new energy vehicles in 2023［J］．Intelligent Connected Vehicles，2023（2）：64-68．
5	SELENA M S， VISWANATH A S， BASIL S，et al ．Well-to-wheel analysis of greenhouse gas emissions and energy consumption for electric vehicles：a comparative study in Oceania［J］．Energy Policy，2021，158：112552/1-14．
6	严军华，王舒笑，袁浩然，等．电动汽车能耗与气体排放分析及环境影响评价［J］．华南理工大学学报（自然科学版），2018，46（6）：137-144．
	YAN Junhua， WANG Shuxiao， YUAN Haoran，et al ．Life cycle assessment of energy consumption with emi-ssion and environmental impact of electric vehicle［J］．Journal of South China University of Technology （Natural Science Edition），2018，46（6）：137-144．
7	YANG Z， WANG B， JIAO K ．Life cycle assessment of fuel cell，electric and internal combustion engine vehicles under different fuel scenarios and driving mileages in China［J］．Energy，2020，198：117365/1-9．
8	奚悦，马同涛，刘明光，等．面向汽车电动化替代的公路交通碳排放影响因素分析［J］．现代电力，2023，40（6）：985-994．
	XI Yue， MA Tongtao， LIU Mingguang，et al ．Analysis on influencing factors of road traffic carbon emission oriented to vehicle electrification substitution［J］．Modern Electric Power，2023，40（6）：985-994．
9	付佩，兰利波，陈颖，等．面向2035的节能与新能源汽车全生命周期碳排放预测评价［J］．环境科学，2023，44（4）：2365-2374．
	FU Pei， LAN Li-bo， CHEN Ying，et al ．Life cycle prediction assessment of energy saving and new energy vehicles for 2035［J］．Environmental Science，2023，44（4）：2365-2374．
10	YU A， WEI Y， CHEN W，et al ．Life cycle environmental impacts and carbon emissions：a case study of electric and gasoline vehicles in China［J］．Transportation Research Part D，2018，65：409-420．
11	詹炜鹏，王震坡，邓钧君，等．基于大数据的电动汽车行驶阶段碳减排影响因素分析［J］．汽车工程，2022，44（10）：1581-1590．
	ZHAN Weipeng， WANG Zhenpo， DENG Junjun，et al ．Analysis of influencing factors of carbon emission reduction in the driving stage of electric vehicles based on big data［J］．Automotive Engineering，2022，44（10）：1581-1590．
12	广东省统计局，国家统计局广东调查总队．2022年广东省国民经济和社会发展统计公报［N］．南方日报，2023-03-31（A6）．
13	HELMERS E， MARX P ．Electric cars：technical characteristics and environmental impacts［J］．Environmental Sciences Europe，2012，24（1）：1-15．
14	中国电力企业联合会．中国电力行业年度发展报告2021［EB/OL］．（2021-07-09）［2023-11-15］．．
15	YAN X， SUN S ．Impact of electric vehicle development on China’s energy consumption and greenhouse gas emissions［J］．Clean Technologies and Environmental Policy，2021，23：2909-2925．
16	综合能耗计算通则：［S］．
17	车用汽油：）［S］．
18	车用柴油：）［S］．

字段名称	字段含义
Pass Time	交易发生时间
Pass ID	计费交易编号
Gantry Hex	门架编号
Vehicle Type	车辆类型
Vehicle Plate	计费车辆车牌号码及颜色

新能源汽车类型	车型	日均车流量/辆	车流量占比/%
客车	1类	752 234	70.86
	2类	212	0.02
	3类	106	0.01
	4类	106	0.01
货车	1类	305 628	28.79
	2类	1 380	0.13
	3类	106	0.01
	4类	637	0.06
	5类	0	0.00

1类客车品牌编号	车流量/辆	车流量占比/%	平均100 km电耗/（kW·h）	100 km电耗均值/（kW·h）
1	25 814	24.47	16.00	15.76
2	15 331	14.53	16.50
3	13 221	12.53	15.80
4	10 598	10.05	15.10
5	7 528	7.14	15.40
1类货车品牌编号	车流量/辆	车流量占比/%	平均100 km电耗/（kW·h）	100 km电耗均值/（kW·h）
1	709	14.52	23.90	24.64
2	651	13.33	26.00
3	531	10.87	22.10
4	281	5.75	24.50
5	247	5.06	26.70

1类客车品牌编号	车流量/辆	车流量占比/%	平均100 km电耗/（kW·h）	平均100 km油耗/L	100 km电耗均值/（kW·h）	100 km油耗均值/L
1	13 591	40.98	15.80	5.5	16.47	5.30
2	3 097	9.34	16.78	5.1
3	2 829	8.53	18.20	5.8
4	2 605	7.86	15.10	4.8

[1]	HE Qingling, PEI Yulong, DONG Chuntong, et al. Classification and Identification of Risky Driving Behavior Based on Hybrid Strategy Improved ASO-LSSVM [J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(9): 131-141.
[2]	SU Yuejiang, WEN Huiying, YUAN Minxian, WU Dexin, ZHOU Lulu, QI Weiwei. Travel Carbon Emission Prediction Model Based on Resident Attribute Data [J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(8): 23-33.
[3]	WANG Jiangfeng, LI Xiudong, SONG Zhifan, et al. Differentiated Highway Toll Pricing Model Considering Carbon Emission Reduction Benefits [J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(8): 14-22.
[4]	GAO Longkai, ZHAO Xiaohua, OU Jushang, et al. Optimization Design of the Stereoscopic Compound Expressway Sign System Based on Unity3D [J]. Journal of South China University of Technology(Natural Science Edition), 2024, 52(5): 20-30.
[5]	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.
[6]	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.
[7]	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.
[8]	JIANG Yu, WANG Yasha, XUE Qingwen, et al. Research on Space-Time Taxiing Optimization of Aircraft Based on Carbon Emission [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 152-159.
[9]	HU Xinghua, CHEN Xinghui, WANG Ran, et al. Optimization Model of Bus Priority Control Considering Carbon Emissions with Stochastic Characteristics [J]. Journal of South China University of Technology(Natural Science Edition), 2023, 51(10): 160-170.
[10]	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.
[11]	ZHOU Xuan, WANG Xinyao, YAN Junwei, et al. Anomaly Detection of Complex Building Energy Consumption System Based on Machine Learning [J]. Journal of South China University of Technology(Natural Science Edition), 2022, 50(7): 144-154.
[12]	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.
[13]	JI Tianyao WANG Tingshao. Building Energy Consumption Prediction Based on Word Embedding and Convolutional Neural Network [J]. Journal of South China University of Technology(Natural Science Edition), 2021, 49(6): 40-48.
[14]	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.
[15]	ZHOU Xuan LEI Shangpeng YAN Junwei. Multi-Step Forecasting for Lighting and Equipment Energy Consumption in Office Building Based on Deep Learning [J]. Journal of South China University of Technology (Natural Science Edition), 2020, 48(10): 19-29.