Support programs for renewable electricity generation in Ontario have been in place since 2005, including feed in-tariffs and a competitive procurement process. These programs have been criticized on a number of fronts. In particular, critics claim the level of support was excessive and creating surplus supply. However, prior studies have ignored one potential benefit of renewable energy support that it can help to promote cost reductions in new technologies through learning-by-doing. This paper uses a recursive-dynamic computable general equilibrium (CGE) model featuring learning-by doing effects to assess the renewable support programs provided in Ontario. Our results, in line with previous studies, do not justify the high support rates paid in Ontario given our core range of assumptions. But our modeling approach allows us to identify the combination of key parameter values relating to learning effects and environmental damages that justify the observed rates. These parameters are hard to measure empirically, and our modeling approach introduces a new tool for examining the impact of variations in these parameters on policy analysis.