Learning by doing, learning spillovers and the diffusion of fuel cell vehicles

Authored by Malte Schwoon

Date Published: 2008-10

DOI: 10.1016/j.simpat.2008.08.001

Sponsors: No sponsors listed

Platforms: No platforms listed

Model Documentation: Other Narrative Flow charts Mathematical description

Model Code URLs: Model code not found

Abstract

Fuel cell vehicles (FCVs) running oil hydrogen do not Cause local air pollution. Depending on the energy sources used to produce the hydrogen they may also reduce greenhouse gases in the long term. Besides problems related to the necessary investments into hydrogen infrastructure, there is a general notion that Current fuel cell costs are too high to be competitive with conventional engines. But given historical evidence from many Other technologies it is highly likely that learning by doing (LBD) would lead to substantial cost reductions. In this study, we implement potential cost reductions from LBD into an existing agent-based model that captures the main dynamics of the introduction of the new technology together with hydrogen infrastructure build-up. Assumptions about the learning rate turn out to have a critical impact on the projected diffusion of the FCVs. Moreover, LBD could imply a Substantial first mover advantage. We also address the impact of learning spillovers between producers and find that a government might face a policy trade-off between fostering diffusion by facilitating learning spillovers and protecting the relative advantage of a national technological leader. (C) 2008 Published by Elsevier B.V.

Tags

Agent-based modeling Learning by doing Fuel cell vehicles Hydrogen