Learn-by-Doing and Carbon Dioxide Abatement

Manne, A.S. & Barreto, L. (2004). Learn-by-Doing and Carbon Dioxide Abatement. IIASA Research Report (Reprint). IIASA, Laxenburg, Austria: RP-04-013. Reprinted from Energy Economics, 26(4):621-633 [2004].

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Abstract

There are inherent difficulties in solving learn-by-doing (LBD) models. Basic to such models is the idea that the accumulation of experience leads to a lowering of costs.

This paper is intended to explore some of the algorithmic issues in LBD modeling for carbon dioxide abatement. When using a standard algorithm for nonlinear programming, there is no guarantee that a local LBD optimum will also be a global optimum. Fortunately, despite the absence of guarantees, there is a good chance that one of the standard algorithms will produce a global optimum for models of this type - particularly if there is an artful selection of the starting point or of the terminal conditions. Moreover, there is a new procedure named BARON. In the case of small models, a global optimum can be recognized and guaranteed through BARON.

Eventually, it should be possible for BARON or a similar approach to be extended to large-scale LBD models for climate change. Meanwhile, in order to check for local optima, the most practical course may be to employ several different starting points and terminal conditions.

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