Learning by doing, learning spillovers and the diffusion of fuel cell vehicles
Fuel cell vehicles (FCVs) running on 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.
learning by doing fuel cell vehicles hydrogen agent-based modeling