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Accounting for Forest Carbon Loss from Natural Gas Development in a Major U.S. Gas Region

Formally Refereed
Download (PDF 4.75 MB): https://research.fs.usda.gov/download/treesearch/81414.pdf

Abstract

Natural gas development in forested landscapes can cause substantial forest disturbance, yet its impacts on forest carbon remain poorly quantified. In the Appalachian Basin of the United States, where natural gas production is expected to continue expanding in the coming decades, understanding these impacts is important for evaluating trade-offs between energy production and forest ecosystem services. Here, we combine nationwide forest inventory and remotely sensed data within a Bayesian spatiotemporal modeling framework to quantify forest disturbance, carbon loss, and opportunity cost associated with natural gas development between 2008 and 2021. Using permitted well locations in Ohio, Pennsylvania, and West Virginia, we generate pixel-level estimates with associated uncertainty and aggregate results to characterize regional patterns. We estimate that 10 854 ha of forest land were disturbed, resulting in 542 675 (±4275) Mg of forest carbon loss and an opportunity cost of 575 246 (±30 774) Mg of carbon (equivalent to the annual emissions of approximately 464 000 and 492 000 gasoline-powered passenger vehicles, respectively). Disturbance rates were greatest in northern West Virginia, while opportunity costs were highest in northeastern Pennsylvania. To our knowledge, this study provides the first regional-scale quantification of forest carbon loss associated with natural gas development in the Appalachian Basin. The results demonstrate the importance of accounting for forest carbon impacts when evaluating energy development and provide information needed to support future mitigation, planning, and optimization efforts.

Citation

Shannon, Elliot S.; Finley, Andrew O.; May, Paul B.; Domke, Grant M.; Foster, Jane R. 2026. Accounting for Forest Carbon Loss from Natural Gas Development in a Major U.S. Gas Region. Environmental Science & Technology. 60: 25590-25596. https://doi.org/10.1021/acs.est.6c00986.
Citations