Learn-by-doing and carbon dioxide abatement

Manne AS & Barreto L (2004). Learn-by-doing and carbon dioxide abatement. Energy Economics 26 (4): 621-633. DOI:10.1016/j.eneco.2004.04.023.

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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.

Item Type: Article
Uncontrolled Keywords: Learn-by-doing model; carbon dioxide abatement; BARON
Research Programs: Environmentally Compatible Energy Strategies (ECS)
Bibliographic Reference: Energy Economics; 26(4):621-633 [2004]
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:16
Last Modified: 12 Sep 2016 13:16
URI: http://pure.iiasa.ac.at/7168

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