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Feed-in-tariff (FIT) policies aim at driving down the cost of renewable energy by fostering learning and accelerating the diffusion of green technologies. Under FIT mechanisms, governments purchase green energy at tariffs that are set above market price. The success or failure of FIT policies, in turn, critically depend on how these tariffs are determined and adjusted over time. This paper provides insights into designing cost-efficient and socially optimal FIT programs. Our modeling framework captures key market dynamics as well as investors’ strategic behavior. In this framework, we establish that the current practice of maintaining constant profitability is theoretically rarely optimal. By contrast, we characterize a no-delay region in the problem’s parameters, such that profitability should strictly decrease over time if the diffusion and learning rates belong to this region. In this case, investors never strategically postpone their investment to a later period. When the diffusion and learning rates fall outside the region, profitability should increase at least temporarily over some time periods and strategic delays occur. The presence of strategic delays, however, makes the practical problem of computing optimal FIT schedules very difficult. To address this issue, the regulator may focus on policies that disincentivize investors to postpone their investment. With this additional constraint, a constant profitability policy is optimal if and only if the diffusion and learning rates fall outside the no-delay region. This provides partial justifications for current FIT implementations.

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