Learning abatement costs: On the dynamics of optimal regulation of experience goods

Abstract

We study the introduction of new technologies when their costs are subject to idiosyncratic uncertainty and can only be fully learned through individual experience. We set up a dynamic model of clean experience goods that replace old polluting consumption options and show how optimal regulation evolves over time. In our base setting where social and private learning incentives coincide, the optimal tax of the polluting consumption is increasing over time. However, if social and private learning incentives diverge, we show that it will be optimal to temporarily increase the tax rate beyond net marginal external damages to induce optimal learning, before reducing the tax rate to the steady-state level. Alternatively, one needs to complement the tax by subsidies for first-time users which will be phased out over time. Similar results apply if consumers have biased expectations. We therefore give a rationale for introductory subsidies of new, clean technologies and non-monotonic tax paths from a perspective of consumer learning