城市客运交通方式碳排放强度比较

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Abstract To compare the carbon emissions levels across various urban passenger transport modes, we proposed a comprehensive evaluation index and developed a detailed calculation model for urban passenger transport carbon emission intensity. Through the application of Kruskal-Wallis test and Bonferroni correction, we discerned significant variances in carbon emission intensities among different urban passenger transport modes. Additionally, the k-means clustering method was further employed to examine the carbon emission intensity across urban rail transit (URT) of different operational mileages. The findings revealed: (1) Both RPCF (Rated person-kilometer carbon emission factor) and APCF (Actual person-kilometer carbon emission factor) indicators were effectively to assess the carbon emission intensity of urban passenger transport modes, yet a significant and non-negligible difference between these two indicators was observed. (2) When average levels of RPCF and APCF were examined, urban public transport was shown to have a clear low-carbon advantage over private transport. The average values of RPCF were determined in descending order for gasoline cars, electric cars, diesel buses, natural gas buses, URT and electric buses, recorded as 40.69, 21.26, 14.86, 11.63, 8.81 and 5.28gCO₂/(person·km) respectively; whereas APCF averages were identified in descending order for gasoline cars, electric cars, URT, diesel buses, natural gas buses and electric buses, noted as 113.02, 59.06, 43.14, 42.47, 33.24 and 15.07gCO₂/(person·km). Compared to RPCF, the low-carbon advantage of URT's APCF was diminished. (3) URT and diesel buses were not observed to have a low-carbon advantage over electric cars. Natural gas buses and electric buses exhibited a significant low-carbon advantage compared to URT, with electric buses being identified as the most carbon-efficient mode and gasoline cars as the least. (4) URT with higher operational mileage was shown to have a greater low-carbon advantage compared to that with lower mileage. URT with operational distances of 0~70, 70~200, 200~400, 400~600 and 600~800 km had their APCF measured as 86.03, 57.43, 51.71, 34.11 and 33.41gCO₂/(person·km).

Key words： urban passenger transport urban transport carbon intensity carbon emission factor Kruskal-Wallis test Bonferroni correction k-means clustering

Received: 15 October 2023

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	TIAN Pei-ning
	ZHANG Hao-xiang
	MAO Bao-hua
	ZHANG Shu-jing

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TIAN Pei-ning,ZHANG Hao-xiang,MAO Bao-hua等. Comparison of carbon emission intensities across different urban passenger transport modes[J]. CHINA ENVIRONMENTAL SCIENCECE, 2024, 44(5): 2823-2832.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2024/V44/I5/2823

[1] Petrović N, Bojović N, Petrović J. Appraisal of urbanization and traffic on environmental quality[J]. Journal of CO₂ Utilization, 2016,16:428-430.
[2] Liu X, Wang M, Qiang W, et al. Urban form, shrinking cities, and residential carbon emissions:Evidence from Chinese city-regions[J]. Applied Energy, 2020,261:114409-114409.
[3] 顾亮.浅析城市轨道交通客流因素对碳排放强度的影响[J].城市轨道交通, 2023,6:31-34. Gu L. An analysis of the impact of passenger flow factors on carbon emission intensity in urban rail transport[J]. China Metros, 2023, 6:31-34.
[4] Fan J, Wang J, Li F, et al. Energy demand and greenhouse gas emissions of urban passenger transport in the Internet era:A case study of Beijing[J]. Journal of Cleaner Production, 2017,165:177-189.
[5] Li Y, Lv C, Yang N, et al. A study of high temporal-spatial resolution greenhouse gas emissions inventory for on-road vehicles based on traffic speed-flow model:A case of Beijing[J]. Journal of Cleaner Production, 2020,277.
[6] Li Y X, Liu H, Li Y C, et al. Predicting of motor vehicle carbon emissions and spatio-temporal characteristic analysis in the Beijing-Tianjin-Hebei region[J]. Environmental science and pollution research international, 2023,30(18):52717-52731.
[7] Gao J, Chen H, Li Y, et al. Fuel consumption and exhaust emissions of diesel vehicles in worldwide harmonized light vehicles test cycles and their sensitivities to eco-driving factors[J]. Energy Conversion and Management, 2019,196:605-613.
[8] 徐龙,王力,刘莹,等.基于多源数据的公交车能耗碳排放测算模型[J].交通运输系统工程与信息, 2020,20(3):174-181. Xu L, Wang L, Liu Y, et al. Calculation model of bus energy consumption and CO₂ emission based on multi-source data[J]. Journal of Transportation Systems Engineering and Information Technology, 2020,20(3):174-181.
[9] 田佩宁,陈硕,毛保华,等.新能源汽车对中国私家车碳排放的影响[J].长安大学学报(自然科学版), 2023,43(5):88-98. Tian P N, Chen S, Mao B H, et al. lmpact of new energy vehicles on carbon emissions of private cars in China[J]. Journal of Chang'an University (Natural Science Edition), 2023,43(5):88-98.
[10] 黄志辉,纪亮,尹洁,等.中国道路交通二氧化碳排放达峰路径研究[J].环境科学研究, 2022,35(2):385-393. Huang Z H, Ji L, Yin J, et al. Peak pathway of China's road traffic carbon emissions[J]. Research of Environmental Sciences, 2022, 35(2):385-393.
[11] 吕晨,张哲,陈徐梅,等.中国分省道路交通二氧化碳排放因子[J].中国环境科学, 2021,41(7):3122-3130. Lv C, Zhang Z, Chen X M, et al. Study on CO₂ emission factors of road transport in Chinese provinces[J]. China Environmental Science, 2021,41(7):3122-3130.
[12] 史丹,叶云岭.城市交通碳排放趋势与减排对策研究——以上海市为例[J].现代管理科学, 2022,4:3-14. Shi D, Ye Y L. Study on the trend of urban transport carbon emissions and reduction countermeasures-A case study of Shanghai Municipality[J]. Modern Management Science, 2022,(4):3-14.
[13] Wang J J, Li Y, Zhang Y. Research on Carbon Emissions of Road Traffic in Chengdu City Based on a LEAP Model[J]. Sustainability, 2022,14(9):6525-6525.
[14] Eggleston S, Buendia L, Miwa K, et al. 2006 IPCC guidelines for national greenhouse gas inventories[R]. Japan:IGES, 2006.
[15] 生态环境部.关于做好2023-2025年发电行业企业温室气体排放报告管理有关工作的通知[EB/OL].(2023-02-17)[2023-08-16]. https://www.mee.gov.cn/xxgk2018/xxgk/xxgk06/202302/t20230207_1015569.html. Ministry of Ecology and Environment. Notice on doing the work related to the management of greenhouse gas emission reporting for enterprises in the power generation sector in 2023-2025[EB/OL].(2023-02-17)[2023-08-16]. https://www.mee.gov.cn/xxgk2018/xxgk/xxgk06/202302/t20230207_1015569.html.
[16] 田佩宁,毛保华,童瑞咏,等.我国交通运输行业及不同运输方式的碳排放水平和强度分析[J].气候变化研究进展, 2023,19(3):347-356. Tian P N, Mao B H, Tong R Y, et al. Analysis of carbon emission level and intensity of China's transportationindustry and different transportation modes[J]. Climate Change Research, 2023,19(3):347-356.
[17] Hansen A P, Beatty W M. Cast erosion from the cleaning of debris after the use of a cast trimmer[J]. The Journal of Prosthetic Dentistry, 2017,117(2):271-276.
[18] Glickman E M, Rao R S, Schultz R M. False discovery rate control is a recommended alternative to Bonferroni-type adjustments in health studies[J]. Journal of Clinical Epidemiology, 2014,67(8):850-857.
[19] Wang J, Wei G, Wei Y. Models for Green Supplier Selection with Some 2-Tuple Linguistic Neutrosophic Number Bonferroni Mean Operators[J]. Symmetry, 2018,10(5):131-131.
[20] LI J, YANG B. Analysis of greenhouse gas emissions from electric vehicle considering electric energy structure, climate and power economy of ev:a China case[J]. Atmospheric Pollution Research, 2020,11(6):1-11.
[21] 小熊油耗官方网站.小熊车系油耗排行榜[EB/OL]. 2023[2023-08-16]. https://www.xiaoxiongyouhao.com/page_rank_chexi.php. Official website of Xiaoxiongyouhao. Fuel consumption ranking of Xiaoxiongyouhao[EB/OL]. 2023[2023-08-16]. https://www.xiaoxiongyouhao.com/page_rank_chexi.php.
[22] 湖南省人民政府.2020年长沙全部使用新能源公交车[EB/OL]. 2017[2023-08-16]. http://www.hunan.gov.cn/hnyw/szdt/201705/t20170523_4810282.html. People's Government of Hunan Province. All new energy buses to be used in Changsha by 2020[EB/OL]. 2017[2023-08-16]. http://www.hunan.gov.cn/hnyw/szdt/201705/t20170523_4810282.html (in Chinese)
[23] 国家能源局.经济又环保江西省多地运营天然气公交车[EB/OL].(2012-10-19)[2023-08-16]. http://www.nea.gov.cn/2012-10/19/c_131915953.htm. National Energy Administration. Economical and environmentally friendly natural gas buses operating in many parts of Jiangxi Provinc[EB/OL].(2012-10-19)[2023-08-16]. http://www.nea.gov.cn/2012-10/19/c_131915953.htm (in Chinese)
[24] 陈丹,于慧,汤程,等.城市公共客运交通碳排放及其大气环境影响的实证研究[J].交通运输系统工程与信息, 2023,23(4):1-10. Chen D, Yu H, Tang C, et al. Empirical Study on carbon dioxide emissions and atmospheric environment impact of urban public passenger transportation[J]. Journal of Transportation Systems Engineering and Information Technology, 2023,23(4):1-10.
[25] 滕欣余,张钰.2019年新能源公交客车运行大数据研究报告[R/OL].社会科学文献出版社, 2019:109-141[2023-08-16]. Teng X Y, Zhang Y. 2019 New energy bus operation big data research report[R/OL]. Social Sciences Academic Press (CHINA), 2019:109-141[2023-08-16].https://www.doc88.com/p-9993922908077.html.
[26] 中国城市轨道交通协会.城市轨道交通运营企业运营数据报告(2019年年度)[R/OL].(2020-05)[2023-08-16]. https://www.camet.org.cn/. China Urban Rail Transit Association. Operation data report of urban rail transit operators (Annual 2019)[R/OL].(2020-05)[2023-08-16]. https://www.camet.org.cn/.