Logging and mill residues are currently the largest sources of woody biomass for bioenergy in the US, but short-rotation woody crops (SRWCs) are expected to become a larger contributor to biomass production, primarily on lands marginal for food production. However, there are very few studies on the environmental effects of SRWCs, and most have been conducted at stand rather than at watershed scales.
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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.
The 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy is the third in a series of Energy Department national assessments that have calculated the potential supply of biomass in the United States. The report concludes that the United States has the future potential to produce at least one billion dry tons of biomass resources (composed of agricultural, forestry, waste, and algal materials) on an annual basis without adversely affecting the environment.
Understanding the development of the biofuels industry in the United States is important to policymakers and industry. The Biomass Scenario Model (BSM) is a system dynamics model of the biomass-to-biofuels system that can be used to explore policy effects on biofuels development. Because of the complexity of the model, as well as the wide range of possible future conditions that affect biofuels industry development, we have not developed a single reference case but instead developed a set of specific scenarios that provide various contexts for our analyses.
The Biomass Scenario Model (BSM) is a system dynamics model that represents the entire biomass-to-biofuels supply chain, from feedstock to fuel use. The BSM is a complex model that has been used for extensive analyses; the model and its results can be better understood if input data used for initialization and calibration are well-characterized. It has been carefully validated and calibrated against the available data, with data gaps filled in using expert opinion and internally consistent assumed values.