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Investigation on Traffic Carbon Emission Factor Based on Sensitivity and Uncertainty Analysis

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  • Jianan Chen

    (School of Energy and Power Engineering, Shandong University, Jinan 250061, China)

  • Hao Yu

    (China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Haocheng Xu

    (China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Qiang Lv

    (China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Zongqiang Zhu

    (China Automotive Engineering Research Institute Co., Ltd., Chongqing 401122, China)

  • Hao Chen

    (School of Energy and Power Engineering, Shandong University, Jinan 250061, China)

  • Feiyang Zhao

    (School of Energy and Power Engineering, Shandong University, Jinan 250061, China)

  • Wenbin Yu

    (School of Energy and Power Engineering, Shandong University, Jinan 250061, China)

Abstract

The premise for formulating effective emission control strategies is to accurately and reasonably evaluate the actual emission level of vehicles. Firstly, the active subspace method is applied to set up a low-dimensional model of the relationship between CO 2 emission and multivariate vehicle driving data, in which the vehicle specific power ( VSP ) is identified as the most significant factor on the CO 2 emission factor, followed by speed. Additionally, acceleration and exhaust temperature had the least impact. It is inferred that the changes in data sampling transform the establishment of subspace matrices, affecting the calculation of eigenvector components and the fitting of the final quadratic response surface, so that the emission sensitivity and final fitting accuracy are impressionable by the data distribution form. For the VSP , the best fitting result can be obtained when the VSP conforms to a uniform distribution. Moreover, the Bayesian linear regression method accounts for fitting parameters between the VSP and CO 2 emission factor with uncertainties derived from heteroscedastic measurement errors, and the values and distributions of the intercept and slope α and β are obtained. In general, the high-resolution inventory of the carbon emission factor of the tested vehicle is set up via systematically analyzing it, which brings a bright view of data processing in further counting the carbon footprint.

Suggested Citation

  • Jianan Chen & Hao Yu & Haocheng Xu & Qiang Lv & Zongqiang Zhu & Hao Chen & Feiyang Zhao & Wenbin Yu, 2024. "Investigation on Traffic Carbon Emission Factor Based on Sensitivity and Uncertainty Analysis," Energies, MDPI, vol. 17(7), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:7:p:1774-:d:1371772
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    References listed on IDEAS

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    1. Martin, Niall P.D. & Bishop, Justin D.K. & Choudhary, Ruchi & Boies, Adam M., 2015. "Can UK passenger vehicles be designed to meet 2020 emissions targets? A novel methodology to forecast fuel consumption with uncertainty analysis," Applied Energy, Elsevier, vol. 157(C), pages 929-939.
    2. Constantine, Paul G. & Diaz, Paul, 2017. "Global sensitivity metrics from active subspaces," Reliability Engineering and System Safety, Elsevier, vol. 162(C), pages 1-13.
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