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Uncertainty, learning, and local opposition to hydraulic fracturing

Joshua Hess (), Dale Manning, Terry Iverson and Harvey Cutler

Resource and Energy Economics, 2019, vol. 55, issue C, 102-123

Abstract: New extraction technologies, including hydraulic fracturing (fracking), have increased fossil fuel reserves in the United States. Despite local economic benefits, many jurisdictions have adopted bans on fracking. We develop a dynamic learning model parameterized with a computable general equilibrium (CGE) model to quantify the associated quasi-option value (QOV), and to explore if uncertainty about environmental damages with the potential to learn can rationalize such bans. The model is calibrated to a representative municipality in Colorado, the site of several fracking bans. With plausible damages, we find that the QOV increases the incentive to delay drilling within a range of energy prices. The results suggest that improving the ability to learn about fracking impacts could increase the prevalence of bans in the short run and lead to better policymaking over time. Incorporating CGE output into a dynamic learning framework permits calculation of the locality-wide QOV associated with a range of policies.

Keywords: Hydraulic fracturing; Quasi-Option value; Stochastic dynamic program; Computable general equilibrium model; Learning (search for similar items in EconPapers)
JEL-codes: C61 C68 Q38 Q58 (search for similar items in EconPapers)
Date: 2019
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Working Paper: Uncertainty, Learning, and Local Opposition to Hydraulic Fracturing (2016) Downloads
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DOI: 10.1016/j.reseneeco.2018.11.001

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