Increasing emissions from road transport is a growing concern for planners and policy makers. Concurrently, congestion is another major issue which affects user behavior, escalates emissions and other externalities, and thus, reduces system welfare. Recent contributions in the literature investigated the interrelationship between congestion and emission levels, and find them to be positively correlated. However, most studies focus on pricing strategies to mitigate one isolated externality, and examine impacts on the other externality. This paper continues this line of research by investigating the effect of congestion pricing on emission levels, and the effect of emission pricing on congestion levels for a large-scale case study of Sioux Falls (South Dakota, US). Going beyond existing studies, the paper then proposes a joint optimization approach of internalizing both externalities simultaneously, and analyzes the effect of different available choice dimensions for users (route, mode, and departure time choice) on the results. The findings for separate pricing of the externalities are in line with the literature, and indicate a positive correlation between congestion and emissions. Furthermore, it is found that simultaneous pricing of congestion and emissions yields a higher increase in system welfare than separate pricing of only one externality. Mode choice turns out to be the determining factor of this welfare change, and therefore needs to be included in the transport model. Finally, the case study shows that simply combining the toll levels obtained from the separate pricing strategies will most likely result in tolls above the economic optimum, and thus, reduce overall welfare.


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    Titel :

    A combined marginal social cost approach for automobile emissions and congestion


    Beteiligte:
    Agarwal, Amit (Autor:in) / Kickhöfer, Benjamin (Autor:in) / Technische Universität Berlin (Gastgebende Institution)

    Erscheinungsdatum :

    2014



    Medientyp :

    Sonstige


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629




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