How do learning externalities influence the evaluation of Ontario's renewables support policies?

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.