Abstract

Future carbon management during energy production will rely on carbon capture and sequestration technology and carbon sequestration methods for offsetting non-capturable losses. The present study quantifies carbon sequestration via reforestation using measurements and modeling for recent and legacy surface coal mining grasslands that are re-restored through tree planting. This paper focuses on a case study of legacy coal mining sites in the southern Appalachia the United States. This five million-hectare region has a surface mining footprint of approximately 12% of the land area, and the reclamation method was primarily grassland. The results of the soil carbon sequestration rates for restored forest soils approach 2.0 MgC ha−1 y−1 initially and average 1.0 MgC ha−1 y−1 for the first fifty years after reclamation. Plant, coarse root and litter carbon sequestration rates were 2.8 MgC ha−1 y−1 with plant carbon estimated to equilibrate to 110 MgC ha−1 after forty years. Plant, root and litter carbon stocks are projected to equilibrate at an order of magnitude greater carbon storage than the existing conditions, highlighting the net carbon gain. Reforestation of legacy mine sites shows carbon sequestration potential several orders of magnitude greater than typical land sequestration strategies for carbon offsets. Projections of future scenarios provide results that show the study region could be carbon neutral or a small sink if widespread reforesting during reclamation was implemented, which is contrary to the business-as-usual projections that result in a large amount of carbon being released to the atmosphere in this region.

Document Type

Article

Publication Date

12-1-2020

Notes/Citation Information

Published in Energies, v. 13, issue 23, 6340.

© 2020 by the authors. Licensee MDPI, Basel, Switzerland.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Digital Object Identifier (DOI)

https://doi.org/10.3390/en13236340

Funding Information

This project was funded by the National Science Foundation Awards 0754888 and 1632888.

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