یارانه بهینه در وسایل نقلیه الکتریکی در مناطق شهری آلمان: تجزیه و تحلیل مکانی تعادل عمومی

E-mobility and diffusion of electric vehicles have become a major policy issue in many countries. For example, the German federal government pursues the strategy of achieving one million electric vehicles by 2020. In this paper we examine whether it is optimal to subsidize the use of electric vehicles by granting electric power subsidies and how large the corresponding optimal rate is. We, first, analytically derive the optimal power tax in a spatial model of a city with two zones where commuting, carbon emissions, endogenous labor supply, fuel and power taxes are considered. It is shown that in a spatial urban environment, the optimal tax rate depends in particular on transport related externalities, tax interaction effects and redistribution effects working via the urban land market. Second, we extend the model to a full spatial general equilibrium model and employ simulations to calculate sign and size of the optimal tax/subsidy rate. This model is calibrated to a typical German metropolitan area. The results show that electric vehicles should not be subsidized but taxed. The results are robust with respect to changes in the willingness to adopt electric vehicles, the costs of driving electric vehicles, and even if emissions of electric vehicles are zero.

E-mobility and the impacts of the diffusion of electric vehicles (EVs) have become a topic of high interest for policymakers and scientists in many countries. Lots of governments aim at raising the share of EVs1 – e.g. either as hybrid electric vehicles, plug-in hybrid electric vehicles or full electric vehicles – in the automobile fleet to lower greenhouse gas emissions of road transport and, thus, to mitigate traffic's contribution to climate change.2 For example, Germany's federal government pursues the strategy of achieving one million electric vehicles by 2020 (see Bundesregierung, 2009). 3 However, switching to EVs on account of economic incentives which lower the high costs of these cars raises questions concerning the net social benefits of these decisions and the optimal level of politically set incentives such as subsidies granted for buying or driving EVs. We explore these issues in the following by applying a spatial urban model approach not yet considered in the research on EVs. Of course, there is a large body of literature on this and other EV related issues. Generally, researchers are by far less optimistic than governments concerning the benefits or net benefits of EVs. It is even disputed whether EVs can lower CO2 emissions in passenger transport (beneficial effects of EVs are found by e.g. Karplus et al., 2010, Kazimi, 1997a, Kazimi, 1997b, Nanaki and Koroneos, 2013 and Thiel et al., 2010; but negative effects are found by Doucette and McCulloch, 2011, Massiani and Weinmann, 2012 and Öko-Institut, 2011).4 There are even some studies calculating social net benefits/costs of EVs (Baum et al., 2011, Carlsson and Johannson-Stenman, 2003, Christensen et al., 2012, Funk and Rabl, 1999, Lave and MacLean, 2002, Massiani and Radeke, 2013 and Prud'homme and Koning, 2012). Most of them find negative social benefits of EVs. Further, demand for EVs is currently very low despite evidence in favor of a high willingness-to-pay for EVs (e.g. Axsen and Kurani, 2012 and Graham-Rowe et al., 2012).5 Studies on pure private costs and benefits of EVs including life cycle cost analyses mostly find negative private net benefits which might explain low demand (e.g. Axsen and Kurani, 2009, Carley et al., 2013, Delucchi, 2005, Delucchi and Lipman, 2001, Kurani et al., 1996 and Werber et al., 2009). To foster demand it might, therefore, be appealing to grant subsidies to R&D, or the purchase and use of EVs.6 However, research concerning efficient policies supporting the diffusion of EVs and the analyses of related impacts is surprisingly rare. Moreover, existing studies evaluating potential policies lack general equilibrium considerations which allow accounting for several feedback effects. This is our point of departure. We explore whether the use of EVs shall be subsidized by granting electric power subsidies and how large the corresponding subsidy rate shall be.7 In contrast to the literature, we take a more general view and consider a broad range of developments in technology, emission levels, EV prices and responsiveness of demand for EVs. We employ a fully specified spatial general equilibrium approach in a second best urban environment that allows us to consider social benefits and costs, to calculate changes in emission costs and to derive the optimal subsidy rate. Therefore, our findings are very robust with respect to many issues examined in the literature.8 The focus is on cities because we expect that the use of EVs will be particularly high in cities. They offer sufficiently short cruising ranges and enough density required for battery loading systems. However, in cities congestion is usually higher and travel related taxes/subsidies affect transport decisions. This might also influence spatial location decisions and, thus, decisions on, e.g., distances traveled.9 The general equilibrium approach is appropriate because the welfare outcome of subsidies on the use of EVs depends intuitively on a number of countervailing effects. For example, even if a higher share of EVs in the car fleet actually lowers carbon emissions there might negative side effects of this policy as well as interactions with other policy instruments. These side effects depend on the level of subsidies required to achieve a certain level of diffusion of EVs. For example, if a subsidy is not high enough to fully compensate for the higher vehicle costs of EVs but people switch to EVs because they have a higher willingness-to-pay for EVs, then travel costs increase. This in turn may lower congestion, labor supply and shopping activities in the city. As a consequence, emissions are reduced further but employment declines too. In contrast, if subsidies overcompensate the higher costs of EVs, traffic increases and so do emissions. In addition, financing this subsidy is likely to cause distortions. In a second-best world tax interaction effects and interaction effects among externalities matter too (see Parry and Small, 2005). There might also be spatial relocation as well as changes in the modal split. Whether this strengthens or weakens net benefits is also a priori undetermined. The overall outcome depends on the relative strength of these and other interdependent effects. As a consequence, the overall effect of subsidies to EVs can only be assessed if feedback effects working through different markets are considered. We proceed as follows: First, we analytically derive the optimal power tax in a spatial model of a city with two zones where commuting, carbon emissions, endogenous labor supply, fuel and power taxes are considered, and where we distinguish between conventional fuel-powered cars and electric vehicles. Second, we extend the model to a spatial computable general equilibrium model (CGE) in the tradition of Anas and Co-authors (see Anas and Rhee, 2006 and Anas and Xu, 1999; see also Tscharaktschiew and Hirte, 2010a, Tscharaktschiew and Hirte, 2010b and Tscharaktschiew and Hirte, 2012) and employ simulations to calculate sign and size of the optimal subsidy or tax rate. This simulation model is calibrated to a typical German metropolitan area. The spatial CGE approach encompasses endogenous individual decisions of urban households (e.g. spatially differentiated consumption requiring shopping trips, housing, labor–leisure choice where labor supply decisions are associated with commuting trips, location decisions concerning the place of residence and employment, travel mode choice), and accounts for market distortions caused by taxes and subsidies levied by a local/federal government as well as distortions stemming from externalities caused by urban transport activities (e.g. congestion and carbon dioxide emissions). All these decisions and related effects caused by these decisions provoke feedback effects working via urban land, labor and good markets. Public policies which aim to increase the diffusion of EVs and so the share of e-mobility can then have a wide range of differentiated effects eventually affecting welfare of the economic actors. In the analytical part it is shown that, in a spatial urban environment, the optimal power tax rate depends in particular on transport related externalities, tax interaction effects and redistribution effects working via the urban land market. Because of the presence of these differentiated, occasionally countervailing, effects, the sign of the optimal tax rate is ambiguous. In the baseline simulations of the numerical part we find that the social costs of subsidizing the use of EVs exceed the social benefits, thus EVs shall not be subsidized but taxed. This refers to all tax rate levels below the current power tax rate in Germany. In the next stage, we examine whether this result also holds if assumptions are changed as much as possible in favor of EVs. We raise the willingness to adopt EVs so that a subsidy more effectively pushes diffusion of EVs implying that a smaller subsidy is sufficient for achieving the government target regarding the diffusion level of EVs. Again, the findings stay the same. Next, we assume that technological progress and scale economies reduce the average costs of EVs by thirty percent. This also does not change the findings. Eventually, we assume that neither the use of EVs nor the upstream production of power implies any carbon emissions. This might mimic a scenario where power generation exclusively comes from renewable resources. Even this does not change our findings. Hence, our analyses suggest that as long as demand for EVs only boosts if they are subsidized they are not an efficient device to achieve climate change goals as well as to improve urban welfare.

In this paper we have analytically derived the components of the optimal power tax on the use of electric vehicles. Externality costs, tax interaction effects, changes in travel costs (or differences in travel costs between conventional fuel-powered cars and EVs) and redistribution effects determine the optimal subsidy rate. The simulations show that particularly the (negative) tax interaction effect more than offset the reduction in carbon emission costs. Even in cases where congestion costs decline the overall effect of the subsidy is negative due to the strong tax interaction effect. Accordingly, we deduce that using electric power for car traveling shall be taxed, not subsidized. Different sensitivity analyses show that our main finding is surprisingly robust with respect to strong changes in the willingness to adopt EVs, changes in costs of using EVs and even if we assume that carbon emissions of using EVs and of upstream power production are zero. The social costs of EVs exceed the benefits from the reduction in CO2 emissions in all these cases. Besides, one should additionally keep in mind that our simulations are based on two further assumptions that basically favor the use of EVs: subsidies are only financed by lump-sum taxes and additional costs of using EVs such as costs of a load infrastructure are not considered. Hence, the overall negative outcome of subsidizing EVs by granting power subsidies is likely to be even understated. In contrast, if fuel taxes (and other taxes related to fuel consumption) on combustion engine vehicles are considerably smaller our verdict might be too strong. In such a case the strong negative tax interaction effect (the main source of the welfare loss) mainly caused by lower fuel tax revenue will be much smaller.60 In such a case, power subsidies could make a contribution to an improvement in welfare. All in all, however, our finding reveals that subsidizing EVs to achieve a reduction of passenger travel related carbon emissions is inefficient and welfare diminishing. Therefore policy shall be much less optimistic concerning the use of EVs for mitigating climate change (see also Massiani and Weinmann (2012) for a corresponding discussion). There are other much more efficient instruments available: e.g. congestion tolls and emission taxes, raising energy taxes to finance public transport,61 etc. (see Tscharaktschiew and Hirte, 2010a and Tscharaktschiew and Hirte, 2012). Concerning congestion pricing, we even looked into a combination of a Pigouvian congestion toll and a power tax subsidy to EVs just to see whether diffusion improves efficiency in the case that one of the externalities is internalized in an appropriate way. Then, subsidies to EVs should only be an instrument to internalize the emission externality. But even in that case the finding stays the same. In the presence of a Pigouvian congestion toll the power tax subsidy lowers aggregate welfare, too. Positive effects of EV diffusion are less extensive because congestion charges also internalize some of the externality costs associated with driving conventional fuel cars. However, we cannot exclude that other positive effects of EVs we have not considered here shift the results in favor of EVs. For example, rising oil prices which make conventional fuel more expensive could foster the demand for EVs (Diamond, 2009) by reducing the current cost disadvantage of EVs (see Dijk et al., 2013). There might be an increase in land supply due to closing of some filling stations. However, the additional available land area is likely to be rather small because only a few filling stations must be closed (the share of EVs is small even if the current policy target will be reached) and some of them might be converted to recharging stations needed to provide a sufficiently dense network for people who cannot charge at home or who are traveling longer distances. Moreover, EVs produce less noise and less local pollution. While the net effect of less noise is ambiguous because it raises safety issues, the second aspect of reduced air pollutants (see Sovacool, 2010) might be the most important benefit from e-mobility.62