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Using a Spatial Model and Demand Predictions to Map Appalachian Surface Coal Mining in the Future | OMICS International| Abstract
ISSN: 2168-9806

Journal of Powder Metallurgy & Mining
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Using a Spatial Model and Demand Predictions to Map Appalachian Surface Coal Mining in the Future

Michaele Stranger*
Division of Resource Management, West Virginia University, Morgantown, West Virginia, United States of America
*Corresponding Author : Michaele Stranger, Division of Resource Management, West Virginia University, Morgantown, West Virginia, United States of America, Email: michstranger@wvu.edu

Received Date: Dec 01, 2022 / Published Date: Dec 31, 2022

Abstract

For a variety of reasons, predicting where future surface coal mining will take place in Appalachia is difficult. Forecasts of future coal production do not directly predict changes in site of future coal output, but economic and regulatory considerations have an impact on the coal mining industry. Considering the potential environmental effects of surface coal mining, decision-makers would find it useful to estimate where future activity would take place. This study's objective was to provide a strategy for estimating future surface coal mining extents in light of shifting economic and governmental projections until the year 2035 [1]. This was done by combining a spatial model with projections of production and demand to forecast changes in land cover on a scale of 1 km2. These two inputs may be combined using a ratio that connected coal extraction amounts to unit area extent. As a result, the Appalachian region, which includes the northern, central, southern, and eastern coal districts of Illinois, received a spatial distribution of probabilities distributed over predicted demand. The findings can be applied to more effectively plan for changes in land use and potential cumulative repercussions.

Citation: Stranger M (2022) Using a Spatial Model and Demand Predictions to Map Appalachian Surface Coal Mining in the Future. J Powder Metall Min 11: 339.

Copyright: © 2022 Stranger M. 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.

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