{"title":"Carbon emission of urban transport with different data sources","authors":"Ruo-yu Wu, Chun-fu Shao, Xin-yi Wang, Xu-yang Yin","doi":"10.18686/mt.v12i1.8856","DOIUrl":null,"url":null,"abstract":"<p class=\"a\"><span lang=\"EN-US\">The identification of critical sectors at the provincial level is important for achieving China’ s CO<sub>2</sub> mitigation target. To scientifically subdivide the target of emission peak and carbon neutrality in public transport, <a name=\"_Hlk135899611\"></a>this article employs a decision tree, taking into a combination of “top-down” and “bottom-up” approaches, to determine selection rules for carbon emission calculation under different data sources. The stepwise regression analysis determines length of vehicle is the key factor affecting bus 100 km energy consumption. The results reveal that, with 383.0 million tons of carbon dioxide being emit, the highest carbon emission from Inner Mongolia ground buses system happened in 2013. The results show that measures including replacing conventional vehicles with electric vehicles could effectively facilitate the road transport sector to gradually approach zero carbon emissions.</span></p>","PeriodicalId":46137,"journal":{"name":"Journal of Modern Transportation","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Modern Transportation","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.18686/mt.v12i1.8856","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0
Abstract
The identification of critical sectors at the provincial level is important for achieving China’ s CO2 mitigation target. To scientifically subdivide the target of emission peak and carbon neutrality in public transport, this article employs a decision tree, taking into a combination of “top-down” and “bottom-up” approaches, to determine selection rules for carbon emission calculation under different data sources. The stepwise regression analysis determines length of vehicle is the key factor affecting bus 100 km energy consumption. The results reveal that, with 383.0 million tons of carbon dioxide being emit, the highest carbon emission from Inner Mongolia ground buses system happened in 2013. The results show that measures including replacing conventional vehicles with electric vehicles could effectively facilitate the road transport sector to gradually approach zero carbon emissions.