Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/74169
Authors: 
Kypreos, Socrates
Year of Publication: 
2005
Series/Report no.: 
Nota di Lavoro, Fondazione Eni Enrico Mattei 123.2005
Abstract: 
Two stylized backstop systems with endogenous technological learning formulations (ETL) are introduced in MERGE: one for the electric and the other for the non-electric markets. Then the model is applied to analyze the impacts of ETL on carbon-mitigation policy, contrasting the resulting impacts with the situation without learning. As the model considers endogenous technological change in the energy sector only some exogenous key parameters defining the production function are varied together with the assumed learning rates to check the robustness of our results. Based on model estimations and the sensitivity analyses we conclude that increased commitments for the development of new technologies to advance along their learning curves has a potential for substantial reductions in the cost of climate mitigation helping to reach safe concentrations of carbon in the atmosphere.
Subjects: 
Climate change stabilization policies
Non-linear optimization
Induced technological change
Energy and macroeconomy
JEL: 
C61
O30
Q42
Q43
Document Type: 
Working Paper

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