Optimal timing of environmental policy under partial information

I explore the optimal timing of environmental policy when the stock of natural capital is unobserved and can only be imperfectly measured. I present two key insights. First, noisy signals about the natural capital stock blur the inference process, thereby easing the conditions under which policy adoption becomes optimal. Second, the interaction be tween natural capital stock volatility and the inference process gives rise to new effects that are absent under perfect information. Specifically, the impact of increased volatility on the conditions for optimal policy adoption varies depending on the information set. My work contributes to both the environmental policy timing literature and the field of resource man agement under incomplete information.