Learn-by-doing and Carbon Dioxide Abatement [Revised March 2002]

Manne AS & Barreto L (2001). Learn-by-doing and Carbon Dioxide Abatement [Revised March 2002]. IIASA Interim Report. IIASA, Laxenburg, Austria: IR-01-057

[img]
Preview
Text
IR-01-057.pdf

Download (104kB) | Preview

Abstract

There are inherent difficulties in solving LBD(learn-by-doing) 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. 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 top large-scale LBD models for climate change. Meanwhile, in order to check for local optima, the most practical course is to apply several different nonlinear programming algorithms - and several different starting solutions with each of them.

Item Type: Monograph (IIASA Interim Report)
Research Programs: Environmentally Compatible Energy Strategies (ECS)
Depositing User: IIASA Import
Date Deposited: 15 Jan 2016 02:13
Last Modified: 25 Oct 2016 07:24
URI: http://pure.iiasa.ac.at/6467

Actions (login required)

View Item View Item

International Institute for Applied Systems Analysis (IIASA)
Schlossplatz 1, A-2361 Laxenburg, Austria
Phone: (+43 2236) 807 0 Fax:(+43 2236) 71 313