Uncovering the CO 2 emissions of vehicles: A well-to-wheel approach
Carbon dioxide (CO ) from road traffic is a non-negligible part of global greenhouse gas (GHG) emissions, and it is a challenge for the world today to accurately estimate road traffic CO emissions and formulate effective emission reduction policies. Current emission inventories for vehicles have eit...
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Veröffentlicht in: | Fundamental research (Beijing) 2024-09, Vol.4 (5), p.1025 |
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Hauptverfasser: | , , , , , |
Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | Carbon dioxide (CO
) from road traffic is a non-negligible part of global greenhouse gas (GHG) emissions, and it is a challenge for the world today to accurately estimate road traffic CO
emissions and formulate effective emission reduction policies. Current emission inventories for vehicles have either low-resolution, or limited coverage, and they have not adequately focused on the CO
emission produced by new energy vehicles (NEV) considering fuel life cycle. To fill the research gap, this paper proposed a framework of a high-resolution well-to-wheel (WTW) CO
emission estimation for a full sample of vehicles and revealed the unique CO
emission characteristics of different categories of vehicles combined with vehicle behavior. Based on this, the spatiotemporal characteristics and influencing factors of CO
emissions were analyzed with the geographical and temporal weighted regression (GTWR) model. Finally, the CO
emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies. The results show that the distribution of vehicle CO
emissions shows obvious heterogeneity in time, space, and vehicle category. By simply adjusting the existing NEV promotion policy, the emission reduction effect can be improved by 6.5%-13.5% under the same NEV penetration. If combined with changes in power generation structure, it can further release the emission reduction potential of NEVs, which can reduce the current CO
emissions by 78.1% in the optimal scenario. |
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ISSN: | 2667-3258 |
DOI: | 10.1016/j.fmre.2023.06.009 |