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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 dynamics. 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.