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Biomass power offers utilities a potential pathway to increase their renewable generation portfolios for compliance with renewable energy standards and to reduce greenhouse gas (GHG) emissions relative to current fossil-based technologies. To date, a large body of life-cycle assessment (LCA) literature assessing biopower’s life-cycle GHG emissions has been published.
Phase A of this project performed an exhaustive search of the biopower LCA literature yielding 117 references that passed quality and relevance screening criteria. Fifty-seven papers reported 280 life-cycle GHG emission estimates. Literature indicates that, excluding land use change (LUC), well-managed and well-designed biopower systems can deliver electricity with low life cycle GHG emissions compared to fossil fuels. The use of residues and organic wastes for biopower could result in significantly lower life-cycle GHG emissions if biomass is diverted from landfill or open-air burning. Using carbon mitigation technologies such as carbon capture and storage, rarely studied for biopower systems, could yield even deeper emission reductions.
Phase B of this project constructed a spreadsheet model of the biopower life cycle to conduct a sensitivity analysis using biomass supply chain parameters that were taken from applicable literature in the LCA literature review. The spreadsheet model, created from NREL’s Systems Advisor Model (SAM) structure, was expanded to evaluate GHG emissions from dedicated biomass crops. These capabilities were integrated into SAM.



Adding bioenergy to the U.S. energy portfolio requires long‐term profitability for bioenergy producers and
long‐term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of
the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water
Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a
regional scale. To quantify feedstock production, we compared lowland switchgrass yields simulated by SWAT with estimates
from a model based on empirical data for the eastern U.S. The two produced similar geographic patterns. Average yields
reported in field trials tended to be higher than average SWAT‐predicted yields, which may nevertheless be more
representative of production‐scale yields. As a preliminary step toward quantifying bioenergy‐related changes in water
quality, we evaluated flow predictions by the SWAT model for the Arkansas‐White‐Red river basin. We compared monthly
SWAT flow predictions to USGS measurements from 86 subbasins across the region. Although agreement was good, we
conducted an analysis of residuals (functional validation) seeking patterns to guide future model improvements. The analysis
indicated that differences between SWAT flow predictions and field data increased in downstream subbasins and in subbasins
with higher percentage of water. Together, these analyses have moved us closer to our ultimate goal of identifying areas with
high economic and environmental potential for sustainable feedstock production.

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