Effectiveness of carbon pricing policies for promoting urban freight electrification: analysis of last mile delivery in Madrid

  • Jose L. Arroyo
  • Ángel Felipe
  • M. Teresa Ortuño
  • Gregorio TiradoEmail author


This research analyzes the effect of carbon pricing policies in transport electrification. It combines a heuristic algorithm to solve the Green Vehicle Routing Problem with Multiple Technologies and Partial Recharges with an economic Total Cost of Ownership model. The paper compares the performance of battery electric (BEV) and internal combustion vehicles (ICEV) for last mile delivery, using real data of Madrid (Spain). The results show that carbon pricing is scarcely effective when daily mileage is low (precisely when BEVs require incentives), and its effectivity increases as mileage increases (precisely when it is not so necessary to incentivize BEVs). Hence, carbon pricing is not an effective tool for promoting electric vehicles in the short term, and as a result, any political decision to fix CO2 prices must be adopted with a long-term view in mind. Specifically for the case of Spain, this research shows that current aids to BEVs are insufficient, with the exception of some regions like Madrid, which complement national subsidies with regional ones.


Carbon pricing Electric vehicle Transportation Optimization 



This work has been supported by the Government of Spain, Grants MTM2015-65803-R and MTM2015-67057-P, and the local Government of Madrid, Grant S2013/ICE-2845 (CASI-CAM-CM).


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Departamento de Métodos CuantitativosUniversidad Pontificia ComillasMadridSpain
  2. 2.Departamento de Estadística e Investigación OperativaUniversidad Complutense de MadridMadridSpain
  3. 3.Departamento de Economía Financiera y Actuarial y EstadísticaUniversidad Complutense de MadridPozuelo de AlarcónSpain
  4. 4.Instituto de Matemática InterdisciplinarUniversidad Complutense de MadridMadridSpain

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