Environmental and Resource Economics

, Volume 71, Issue 3, pp 777–800 | Cite as

Defining the Abatement Cost in Presence of Learning-by-Doing: Application to the Fuel Cell Electric Vehicle

  • Anna Creti
  • Alena Kotelnikova
  • Guy MeunierEmail author
  • Jean-Pierre Ponssard


We consider a partial equilibrium model to study the optimal phasing out of polluting goods by green goods. The unit production cost of the green goods involves convexity and learning-by-doing. The total cost for the social planner includes the private cost of production and the social cost of carbon, assumed to be exogenous and growing at the social discount rate. Under these assumptions the optimization problem can be decomposed in two questions: (i) when to launch a given schedule; (ii) at which rate the transition should be completed that is, the design of a transition schedule as such. The first question can be solved using a simple indicator interpreted as the MAC of the whole schedule, possibly non optimal. The case of hydrogen vehicle (Fuel Cell Electric Vehicles) offers an illustration of our results. Using data from the German market we show that the 2015–2050 trajectory foreseen by the industry would be consistent with a carbon price at 52€/t. The transition cost to achieve a 7.5 M car park in 2050 is estimated at 21.6 billion € that is, to JEl 4% discount rate, 115 € annually for each vehicle which would abate 2.18 tCO\(_2\) per year.


Dynamic abatement costs Learning by doing Fuel cell electric vehicles 

JEL Classification

Q55 Q42 C61 


  1. Acemoglu D, Aghion P, Bursztyn L, Hemous D (2012) The environment and directed technical change. Am Econ Rev 102(1):131–66CrossRefGoogle Scholar
  2. Allen MR, Frame DJ, Huntingford C, Jones CD, Lowe JA, Meinshausen M, Meinshausen N (2009) Warming caused by cumulative carbon emissions towards the trillionth tonne. Nature 458(7242):1163–1166CrossRefGoogle Scholar
  3. Amigues J-P, Ayong Le Kama A, Moreaux M (2015a) Equilibrium transitions from non-renewable energy to renewable energy under capacity constraints. J Econ Dyn Control 55:89–112CrossRefGoogle Scholar
  4. Amigues J-P, Lafforgue G, Moreaux M (2015b) Optimal timing of carbon sequestration policies. Econ Bull 35(4):2242–2251Google Scholar
  5. Archsmith J, Kendall A, Rapson D (2015) From cradle to junkyard: assessing the life cycle greenhouse gas benefits of electric vehicles. Res Transp Econ 52:72–90CrossRefGoogle Scholar
  6. Beeker E (2014) Y a-t-il une place pour l’hydrogène dans latransition ènergètique? Note d’analyse, France Stratègie,Commissariat gènèral à la Stratègie et à la Prospective .
  7. Bramoullé Y, Olson LJ (2005) Allocation of pollution abatement under learning by doing. J Public Econ 89(9):1935–1960CrossRefGoogle Scholar
  8. Brunet J, Ponssard J.-P (2016) Policies and deployment for fuel cell electric vehicles an assessment of the normandy project, Int J Hydrog Energy (forthcoming)Google Scholar
  9. Creti A, Kotelnikova A, Meunier G, Ponssard J-P (2015) A cost benefit analysis of fuel cell electric vehicles. Report 1:1–41Google Scholar
  10. Golosov M, Hassler J, Krusell P, Tsyvinski A (2014) Optimal taxes on fossil fuel in general equilibrium. Econometrica 82(1):41–88CrossRefGoogle Scholar
  11. Goulder LH, Mathai K (2000) Optimal CO\(_2\) abatement in the presence of induced technological change. J Environ Econ Manag 39(1):1–38CrossRefGoogle Scholar
  12. Grimaud A, Rouge L (2008) Environment, directed technical change and economic policy. Environ Resour Econ 41(4):439–463CrossRefGoogle Scholar
  13. Grimaud A, Rouge L (2014) Carbon sequestration, economic policies and growth. Resour Energy Econ 36(2):307–331CrossRefGoogle Scholar
  14. Harrison P (2014) Fueling Europe’s Futur: how auto innovation leads to eu jobs, technical report, Cambridge econometrics.
  15. IEA (2000) Experience curves for energy technology policy. International Energy Agency, Paris, FranceGoogle Scholar
  16. IPCC (2013) Climate Change 2013: the physical science basis. contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change, Cambridge University Press, Cambridge, United Kingdom and New York, USA, chapter Summary for PolicymakersGoogle Scholar
  17. Loulou R (2008) ETSAP-TIAM: the TIMES integrated assessment model. Part II: mathematical formulation. CMS 5(1–2):41–66Google Scholar
  18. Loulou R, Labriet M (2008) ETSAP-TIAM: the TIMES integrated assessment model Part I: model structure. CMS 5(1–2):7–40CrossRefGoogle Scholar
  19. Marcantonini C, Ellerman AD (2014) The implicit carbon price of renewable energy incentives in Germany. Robert Schuman Centre for Advanced Studies Research Paper No, RSCAS, p 28Google Scholar
  20. Matthews HD, Caldeira K (2008) Stabilizing climate requires near-zero emissions. Geophys Res Lett 35:L04705. doi: 10.1029/2007GL032388
  21. McDonald A, Schrattenholzer L (2001) Learning rates for energy technologies. Energy Policy 29(4):255–261CrossRefGoogle Scholar
  22. McKinsey & Company (2010) Portfolio of power-trains for Europe: a fact-based analysis.
  23. Nordhaus WD (2011) Estimates of the social cost of carbon: background and results from the RICE-2011 model. Technical report, National Bureau of Economic ResearchGoogle Scholar
  24. Oshiro K, Masui T (2014) Diffusion of low emission vehicles and their impact on \(\text{CO}_{2}\) emission reduction in Japan. Energy Policy (in press)Google Scholar
  25. Quinet A (2009) La valeur tutélaire du carbone. Rapport du Centre d’Analyse Stratégique, La documentation FrançaiseGoogle Scholar
  26. Quinet E (2013) L’évaluation socioéconomique des investissements publics, gènèral à la stratègie et à la prospectiveGoogle Scholar
  27. Rezai A, van der Ploeg F (2015) Robustness of a simple rule for the social cost of carbon. Econ Lett 132:48–55. CrossRefGoogle Scholar
  28. Rosen S (1972) Learning by experience as joint production, Q J Econ, pp 366–382CrossRefGoogle Scholar
  29. Rösler H, van der Zwaan B, Keppo I, Bruggink J (2014) Electricity versus hydrogen for passenger cars under stringent climate change control. Sustain Energy Technolog Assess 5:106–118CrossRefGoogle Scholar
  30. Schennach SM (2000) The economics of pollution permit banking in the context of Title IV of the 1990 Clean Air Act Amendments. J Environ Econ Manag 40(3):189–210CrossRefGoogle Scholar
  31. Stern NH (2007) The economics of climate change: the Stern review. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  32. Thompson P (2010) Learning by doing. Handb Econ Innov 1:429–476CrossRefGoogle Scholar
  33. Tol RS (2014) Climate economics: economic analysis of climate, climate change and climate policy. Edward Elgar Publishing, CheltenhamGoogle Scholar
  34. Van den Bijgaart I, Gerlagh R, Liski M (2016) A simple formula for the social cost of carbon. J Environ Econ Manag 77:75–94CrossRefGoogle Scholar
  35. Vogt-Schilb A, Meunier G, Hallegatte S (2012) How inertia and limited potentials affect the timing of sectoral abatements in optimal climate policy. World Bank Policy Research Working Paper (6154)Google Scholar
  36. Zachmann G, Holtermann M, Radeke J, Tam M, Huberty M, Naumenko D, Faye AN (2012) The great transformation: decarbonising Europe’s energy and transport systems. Bruegel Blueprint 16Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2017

Authors and Affiliations

  • Anna Creti
    • 1
  • Alena Kotelnikova
    • 1
  • Guy Meunier
    • 1
    • 2
    Email author
  • Jean-Pierre Ponssard
    • 1
    • 3
  1. 1.CRESTEcole Polytechnique, Université Paris-SaclaySaclayFrance
  2. 2.INRA-UR1303 ALISSIvry-Sur-SeineFrance
  3. 3.CREST, CNRS, Ecole PolytechniqueUniversité Paris-SaclaySaclayFrance

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