The development of oil and gas extraction technologies, including hydraulic fracturing (fracking), has increased fossil fuel reserves in the US. Despite benefits, uncertainty over environmental damages has led to fracking bans, both permanent and temporary, in many jurisdictions. We develop a stochastic dynamic learning model parameterized with a computable general equilibrium model to explore if uncertainty about damages, combined with the ability to learn about risks, can explain fracking bans in practice. Applying the model to a representative Colorado municipality, we quantify the quasi-option value (QOV), which creates an additional incentive to ban fracking temporarily in order to learn, though it only influences policy in a narrow range of oil and gas prices. To our knowledge, this is the first attempt to quantify an economy-wide QOV associated with a local environmental policy decision.
JEL: C61, C68, Q38, Q58
Keywords: hydraulic fracturing; quasi-option value; stochastic dynamic program; computable general equilibrium model