Allocation of pollution abatement under learning by doing

This paper examines how learning by doing affects the allocation of pollution abatement between heterogeneous technologies over time. The optimal policy balances current abatement costs against reductions in future costs and infant technologies may be preferred to mature technologies despite greater initial costs. We characterize when a technological winner might emerge and we identify conditions under which optimal abatement is shared across all technologies. Pigouvian taxes can implement the first-best, but may need to be differentiated across technologies. When technical change is induced, an important role of environmental policies is to put abatement on the right technological trajectories. (c) 2004 Elsevier B.V. All rights reserved.