We develop a model with two types of electricity producers, fossil fuel utilities generating emissions, and suppliers of electricity from of renewable energy sources (RES-E). We account for the vertical structure of the renewable energy sector by considering upstream RES-E equipment producers engaged in learning-by-doing and selling their equipment to downstream operators. We show that under pure private learning a first-best allocation can be induced by charging emission taxes on the Pigouvian level only, while in the case of learning spillovers additional subsidies on both output and market entry are necessary. In case that (optimal) emission taxes are ruled out, then in case of purely private learning, second-best optimal feed-in-tariffs should only account for the marginal environmental damage and the marginal cost of production of the conventional utilities. In the case of learning spillovers, feed-in-tariffs can also be used to spur learning.
Get full access to this article
View all access options for this article.
References
1.
ArrowK. (1962): “The Economic Implication of Learning-by-Doing,” Review of Economic Studies29, 155–173.
2.
BardhanP.K. (1971): “On Optimum Subsidy to a Learning Industry: An Aspect of the Theory of Infant-Industry Protection,” International Economic Review12, 54–70.
Directive 2001/77/EC of the Parliament and the Council of 27 September 2001 on the promotion of electricity produced from renewable energy sources in the internal electricity market, Official Journal of the European Communities L 283/33
10.
FudenbergD. and TiroleJ. (1983): “Learning-by-Doing and Performance Market,” The Bell Journal of Economics14, 522–530.
11.
GersbachH. and RequateT. (2004): “Emission Taxes and the Design of Refunding Schemes,” Journal of Public Economics88, 713–725.
12.
GhemawatP. and SpenceM. (1985): “Learning Curve Spillovers and Performance Market,” Quarterly Journal of Economics100, 839–852.
13.
HansenJ.D.JensenC., and MadsenE.S. (2003): “The Establishment of the Danish Windmill Industry - Was It Worthwhile?” Review of World Economics139, 324–347.
14.
International Energy Agency (2000): “Experience curves for energy technology policy,”Paris.
IsoardS., and SoriaA. (2001): “Technical Change Dynamics: Evidence from the Emerging Renewable Energy Technologies,” Energy Economics23, 619–636.
17.
JungingerM.FaaijA., and TurkenburgW. (2005) “Global experience curves for wind farms,” Energy Policy33, 133–150.
18.
LeahyD. and NearyP.J. (1999): “Learning-by-Doing, Precommitment and Infant-Industry Promotion,” Review of Economic Studies66, 447–474.
19.
MiraveteE.J. (2003): “Time-Consistent Protection with Learning-by-Doing,” European Economic Review47, 761–790.
20.
MorthorstP. (1999): “Capacity development and profitability of wind turbines,” Energy Policy27, 779–787.
21.
NeijL.AndersenP.D.DurstewitzM.HelbyP.Hoppe-KilperM., and MorthorstP.E. (2003): “Experience Curves: A Tool for Energy Policy Assessment,” IMES/EESS Report No. 40 Department of Technology and Society, Environmental and Energy Systems Studies, Lund University, Sweden.
22.
NeijL. (1997): “Use of experience curves to analyze the prospects for diffusion and adoption of renewable energy technology,” Energy Policy23, 1099–1107.
23.
NeijL. (1999): “Cost dynamics of wind power.” Energy24, 375–389.
24.
NordhausW. and BoyerJ. (2000): Warming the World - Economic Models of Global Warming. MIT Press, USA.
25.
PetrakisE.RasmusenE., and RoyS. (1997): “The learning curve in a competitive industry,” RAND Journal of Economics28, 248–268.
26.
RauchJ.E. (1992): “A Note on the Optimum Subsidy to a Learning Industry,” Journal of Development Economics38, 233–243.
27.
SimsR.E.H., SchockR.N., AdegbululgbeA., FenhannJ., KonstantinaviciuteI., MoomawW., NimirH.B., SchlamadingerB., Torres-MartínezJ., TurnerC., UchiyamaY., VuoriS.J.V., WamukonyaN., ZhangX. (2007): Energy supply. In Climate Change2007: Mitigation. Contribution of Working Group III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.
28.
SpenceM.A. (1981): “The Learning Curve and Competition,” The Bell Journal of Economics12, 49–70.
29.
SpulberD.F. (1985): “Effluent Regulation and Long-Run Optimality.” Journal of Environmental Economics and Management12, 103–116.
30.
TolR. (2005): “The marginal damage costs of carbon dioxide emissions: an assessment of the uncertainties,” Energy Policy33, 2064–2074.
