Optimal Feed-in Tariff Schedules

We analyze the design of optimal feed-in tariff schedules under production-based learning. We examine least cost policies in a simple two-period model that focuses on bringing down the levelized cost of renewable technologies to a predefined target under two well-known dynamics: learning-by-doing (LBD) and economies of scale (EOS). We show that, when the levelized cost reduction target is stringent, subsidies are required in both periods, regardless of the dynamic. However, when the target is moderate, the optimal policy is to subsidize only in one of the two periods: under the LBD dynamics it is optimal to subsidize as early as possible, whereas under the EOS dynamics it is optimal to subsidize as late as possible. Under the LBD dynamics the prevailing factor is the impact of early investment on cumulative experience, whereas under the EOS dynamics the prevailing factor is capital depreciation. The key takeaway is that, based on the underlying dynamics, the policy maker needs to adopt fundamentally different kinds of policies to promote renewable technologies. Managerial Relevance This paper provides guidelines for designing feed-in tariffs in such a way that they produce desired improvements in renewable technologies while being cost effective from the perspective of society. This is relevant to a wide range of policy makers, at national and sub-national levels, who are faced with questions about how to design renewable energy policies. The paper underlines the importance of understanding the cost dynamics of the particular technology that is being subsidized. In addition, policies such as feed-in tariffs are of great interest and import to firms in the renewable energy industry. This paper provides such firms with a policy-makers perspective on the costs and benefits of such policies, as well as some insight into how optimal investments in renewable energy technologies are effected by feed-in tariffs.