This is a joint report between three national labs, ORNL, INL, and ANL, that describes outcomes from a workshop. The Bioenergy Solutions to Gulf Hypoxia Workshop gathered stakeholders from industry, academia, national laboratories, and U.S. federal agencies to discuss how biomass feedstocks could help decrease nutrient loadings to the Gulf of Mexico (Gulf), a root cause of the large hypoxic zone that forms each summer.
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With the goal of understanding environmental effects of a growing bioeconomy, the U.S. Department of Energy (DOE), national laboratories, and U.S. Forest Service research laboratories, together with academic and industry collaborators, undertook a study to estimate environmental effects of potential biomass production scenarios in the United States, with an emphasis on agricultural and forest biomass. Potential effects investigated include changes in soil organic carbon (SOC), greenhouse gas (GHG) emissions, water quality and quantity, air emissions, and biodiversity.
This dataset reports the pre-treatment hydrology and pre- and post-treatment water quality data from a watershed-scale experiment that is evaluating the effects of growing short-rotation loblolly pine for bioenergy on water quality and quantity in the southeastern U.S. The experiment is taking place on the Savannah River Site, near New Ellenton, South Carolina, USA. Beginning in 2010, water quality and hydrology were measured for two years in 3 watersheds (R, B, C).
The Regional Feedstock Partnership (the Partnership) has published a report to summarize its accomplishments from 2008–2014. DOE’s Bioenergy Technologies Office (BETO) partnered with the Sun Grant Initiative and Idaho National Laboratory to co-author this report.
One approach to assessing progress towards sustainability makes use of multiple indicators spanning the
environmental, social, and economic dimensions of the system being studied. Diverse indicators have different
units of measurement, and normalization is the procedure employed to transform differing indicator
measures onto similar scales or to unit-free measures. Given the inherent complexity entailed in interpreting
information related to multiple indicators, normalization and aggregation of sustainability indicators