Unraveling the photovoltaic technology learning curve by incorporation of input price changes and scale effects

In a large number of energy models, the use of learning curves for estimating technological improvements has become popular. This is based on the assumption that technological development can be monitored by following cost development as a function of market size. However, recent data show that in some stages of photovoltaic technology (PV) production, the market price of PV modules stabilizes even though the cumulative capacity increases. This implies that no technological improvement takes place in these periods: the cost predicted by the learning curve in the PV study is lower than the market one. We propose that this bias results from ignoring the effects of input prices and scale effects, and that incorporating the input prices and scale effects into the learning curve theory is an important issue in making cost predictions more reliable. In this paper, a methodology is described to incorporate the scale and input-prices effect as the additional variables into the one factor learning curve, which leads to the definition of the multi-factor learning curve. This multi-factor learning curve is not only derived from economic theories, but also supported by an empirical study. The results clearly show that input prices and scale effects are to be included, and that, although market prices are stabilizing, learning is still taking place.