The correlation of externalities in marginal cost pricing: lessons learned from a real-world case study

Abstract

Negative externalities cause inefficiencies in the allocation of capacities and resources in a transport system. Marginal social cost pricing allows to correct for these inefficiencies in a simulation environment and to derive real-world policy recommendations. In this context, it has been shown for analytical models considering more than one externality, that the correlation between the externalities needs to be taken into account. Typically, in order to avoid overpricing, this is performed by introducing correction factors which capture the correlation effect. However, the correlation structure between, say, emission and congestion externalities changes for every congested facility over time of day. This makes it close to impossible to calculate the factors analytically for large-scale systems. Hence, this paper presents a simulation-based approach to calculate and internalize the correct dynamic price levels for both externalities simultaneously. For a real-world case study, it is shown that the iterative calculation of prices based on cost estimates from the literature allows to identify the amplitude of the correlation between the two externalities

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

“…The internalization of externalities through optimal pricing can, e.g. in agentbased transport simulations, be used to identify the upper bound of possible efficiency gains in a transport system (see, e.g., Kaddoura et al, 2015;Agarwal and Kickhöfer, 2016). However, the calculation of dynamic, 8 'Handbook Emission Factors for Road Transport', Version 3.1, see www.hbefa.net 9 The marginal social costs are the sum of marginal private costs (MPC) and marginal external costs (MEC) (see, e.g., Walters, 1961;Turvey, 1963).…”

“…Figure 2 shows the territorial border of Munich city and Munich metropolitan area (MMA). The initial scenario was created by and further modified by Agarwal and Kickhöfer (2016). In the present study, the latter is used.…”

“…Interpretation of utility parameters Following the study by Agarwal and Kickhöfer (2016), the present study also uses two different PT modes and consequently two ASCs for each PT mode. All behavioral parameters and the approximate average Values of Travel Time Savings (VTTS ) 13 are listed in Table 3.…”

“…Effect of combined pricing schemes on emission costs A combined pricing scheme for emission and congestion is proposed by Agarwal and Kickhöfer (2015) and a positive correlation between these two externalities is found. Further, Agarwal and Kickhöfer (2016) identify the amplitude of the correlation between congestion and emission externalities. However, these combined pricing schemes are able to reduce the emissions costs only by 2.5%, and 7.2% respectively under different initial assumptions which is still far-away from the 20% reduction target.…”

“…Thus, in a first step, the present study applies an existing optimal pricing scheme for exhaust emissions to a real-world scenario of the Munich metropolitan area in Germany similar to work by ; Agarwal and Kickhöfer (2016); Kaddoura et al (2017). In a second step, the paper attempts to identify the necessary additional prices, as multiples of the original damage cost estimates, in order to reach the EU 2020 CO 2 reduction targets.…”