A meta-analysis of learning curves to improve energy policy: Lessons from the United States and Brazil

Abstract

In this research we applied the learning curves approach to help policy managers decide between a set of protectionism or competitive measures in order to reduce energy prices. By estimating the competitiveness parameters of the most important energy resources in Brazil and United States, such as photovoltaics, wind, biomass, hydro, nuclear, natural gas, oil and coal, we inferred which policy should be recommended to reduce their costs to the society. The learning curves approach takes three main effects into consideration, namely learning by doing effect, scale effect and learning by searching effect (also named research and development effect). In this context, the article has two primordial purposes. The first is to estimate the effects of the learning process on the competitiveness of the main energy technologies. The second is to propose regulatory measures and energy policies aiming to encourage the deployment of renewable technologies. Data from the United States and Brazil were used to optimize the information available based on the maturity of each energy technology. The results of the learning curves for those two countries showed that the main driver leading to a decrease in costs of photovoltaics, wind, nuclear, natural gas, and oil was the learning by doing effect. The learning by searching effect led to a reduction in costs in the cases of wind, biomass, and coal. In turn, the scale effect contributed to reducing the costs of hydro, wind and biomass. Therefore, specific energy policies were proposed for each renewable source.