Document Type
Article
Publication Date
2015
College/Unit
Davis College of Agriculture, Natural Resources and Design
Department/Program/Center
Division of Resource Economics & Management
Abstract
Predicting the locations of future surface coal mining in Appalachia is challenging for a number of reasons. Economic and regulatory factors impact the coal mining industry and forecasts of future coal production do not specifically predict changes in location of future coal production. With the potential environmental impacts from surface coal mining, prediction of the location of future activity would be valuable to decision makers. The goal of this study was to provide a method for predicting future surface coal mining extents under changing economic and regulatory forecasts through the year 2035. This was accomplished by integrating a spatial model with production demand forecasts to predict (1 km2) gridded cell size land cover change. Combining these two inputs was possible with a ratio which linked coal extraction quantities to a unit area extent. The result was a spatial distribution of probabilities allocated over forecasted demand for the Appalachian region including northern, central, southern, and eastern Illinois coal regions. The results can be used to better plan for land use alterations and potential cumulative impacts.
Digital Commons Citation
Strager, Michael P.; Strager, Jacquelyn M.; Evans, Jeffrey S.; Dunscomb, Judy K.; Kreps, Brad J.; and Maxwell, Aaron E., "Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia" (2015). Faculty & Staff Scholarship. 2221.
https://researchrepository.wvu.edu/faculty_publications/2221
Source Citation
Strager MP, Strager JM, Evans JS, Dunscomb JK, Kreps BJ, Maxwell AE (2015) Combining a Spatial Model and Demand Forecasts to Map Future Surface Coal Mining in Appalachia. PLoS ONE 10(6): e0128813. https://doi.org/10.1371/journal.pone.0128813
Comments
© 2015 Strager et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited