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Initial incidence of carbon taxes and environmental liability. A vehicle ownership approach

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  • Tovar Reaños, Miguel A.

Abstract

A German panel data of vehicle and owner characteristics is used to analyse the incidence of additional carbon taxes. It is shown that an additional carbon tax on fuel used for private transportation is regressive when there is no allocation of tax revenue. When smoothing consumption across time in the face of additional carbon taxes, low income households can reduce the tax burden. When the cost of air pollution is included in the metric for the tax incidence, the tax burden decreases considerably. It is also found that in order to charge drivers for the attributed emissions, carbon taxes need to be set at least at €30 per tonne of CO2 emissions. Moreover, the estimated own price elasticities suggest that an additional carbon tax may fail to induce owners of vehicles with an intense usage to reduce energy consumption. Consequently, carbon taxes need to be designed jointly with other taxes to target heavy polluters.

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  • Tovar Reaños, Miguel A., 2020. "Initial incidence of carbon taxes and environmental liability. A vehicle ownership approach," Energy Policy, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:enepol:v:143:y:2020:i:c:s0301421520303165
    DOI: 10.1016/j.enpol.2020.111579
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    Cited by:

    1. Tovar Reanos, Miguel, 2020. "Car ownership and the distributional and environmental policies to reduce driving behavior," Papers WP673, Economic and Social Research Institute (ESRI).

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    More about this item

    Keywords

    Transport policies; Distributional effects; CO2 emissions;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • R48 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government Pricing and Policy
    • C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data; Spatio-temporal Models

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