Mind the price gap: How optimal emission pricing relates to the EU CO2 reduction targets

Abstract

From the transport economic literature, it is known that optimal pricing of (environmental) externalities improves the urban system. In contrast to theory-based optimal pricing strategies, real-world policy setting often follows so-called “backcasting” approaches where certain targets are set, and policy measures are implemented in order to reach those targets. An example for the latter approach is the EU goal to reduce global greenhouse gas emissions in the transport sector by 20% until 2020 with respect to 1990 levels. This article aims to (i) compare optimal pricing and backcasting approaches for a specific case study in a simulation environment by identifying the contribution of each approach in EU’s 2020 emission reduction target, and (ii) to determine the costs required to reach the desired targets. For this purpose, an optimal emission pricing strategy is applied to a real-world scenario of the Munich metropolitan area in Germany. The highly differentiated tolls relate to individual exhaust emissions, i.e. they are calculated using damage cost estimates from the literature and vary over time of day, with traffic situation, and with vehicle type. The results indicate that the desired reduction in CO2 emissions is not reached for optimal pricing approach, and that the initial damage costs estimates need to be multiplied by a factor of 5 in order to reach the target, yielding a price of 350 CO2. When aiming at a decrease of the overall emission costs by 20% (CO2 and local pollutants), the initial cost estimates need to be multiplied by a factor of 10. Furthermore, it is shown that the major contribution to the overall emission reduction stems from behavioral changes of commuters and reverse commuters rather than from urban travelers; under some circumstances, urban travelers even increase their CO2 emission levels. Hence, the study rises awareness that conflicting trends for different types of pollutants and different types of individuals are very likely: an increase in non-methane hydrocarbon levels for urban travelers and freight depicts that pricing emissions does not necessarily result in a reduction of all pollutants or of the emissions levels of all travelers. It is shown how agent-based simulations can be used to provide valuable insights and decision support in such possibly counter-intuitive situations.