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Research on the Calculation of Urban Transport Carbon Dioxide Emissions: A Case Study of Guangzhou City

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Data Mining and Big Data (DMBD 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10943))

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Abstract

Taking Guangzhou as an example, the calculation formula of low carbon transport has been improved by calibrating the energy consumption coefficient in this paper. On this basis, the annual transport carbon emissions of Guangzhou from 2011 to 2015 based on the statistical data has been calculated. At the same time, the transport carbon emissions in 2015 based on the data of Guangzhou Traffic Planning Model has been also calculated in this paper. By comparing these two kinds of calculation method, calculation method based on the data of Guangzhou Traffic Planning Model can measure the carbon emission of cross boundary transport and the carbon emission of small passenger cars more accurately. This method is also easier to measure the carbon reduction caused by the transfer of individual transportation to intensive transportation. Finally, it is concluded that the calculation method based on the Guangzhou Traffic Planning Model data source is more suitable for the study of carbon reduction in micro level transport.

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Acknowledgments

This work was supported by the Science and Technology Program of Guangzhou, China (nos. 201607010327).

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Authors and Affiliations

  1. GuangZhou Transport Planning Research Institute, Guangzhou, 510030, China

    Hai-Xia Zhang

  2. School of Engineering, Sun Yat-Sen University, Guangzhou, 510275, China

    Wen-Yuan Liao

Authors
  1. Hai-Xia Zhang
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  2. Wen-Yuan Liao
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Corresponding author

Correspondence to Hai-Xia Zhang .

Editor information

Editors and Affiliations

  1. Peking University, Beijing, China

    Ying Tan

  2. Southern University of Science and Technology, Shenzhen, China

    Yuhui Shi

  3. Tongji University, Shanghai, China

    Qirong Tang

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Zhang, HX., Liao, WY. (2018). Research on the Calculation of Urban Transport Carbon Dioxide Emissions: A Case Study of Guangzhou City. In: Tan, Y., Shi, Y., Tang, Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science(), vol 10943. Springer, Cham. https://doi.org/10.1007/978-3-319-93803-5_67

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  • DOI: https://doi.org/10.1007/978-3-319-93803-5_67

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-93802-8

  • Online ISBN: 978-3-319-93803-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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