"Twin Peaks" in Energy Prices: A Hotelling Model with Pollution Learning

We study how environmental regulation in the form of a cap on aggregate emissions from a fossil fuel (e.g., coal) affects the arrival of a clean substitute (e.g., solar energy). The cost of the substitute decreases with cumulative use because of learning-by-doing. We show that energy prices may initially increase but then decline upon attaining the targeted level of pollution, followed by another cycle of rising and falling prices. The surprising result is that with pollution and learning, the Hotelling model predicts the cyclical behavior of energy prices in the long run. The alternating trends in upward or downward price movements we show may at least partially explain recent empirical findings by Lee, List and Strazicich (2006) that long run resource prices are stationary around deterministic trends with structural breaks in intercept and trend slope. The main implication of our results is that testing for secular price trends as predicted by the textbook Hotelling model may lead to incorrect conclusions regarding the predictive power of the theory of nonrenewable resource economics.