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This dataset was utilized in a report to highlight parameters that affect near-term sustainable supply of corn stover and forest resources at $56 and $74 per dry ton delivered. While the report focus is restricted to 2018, the modeling runs are available from 2016-2022. In the 2016 Billion-ton Report (BT16), two stover cases were presented. In this dataset, we vary technical levels of those assumptions to measure stover supply response and to evaluate the major determinants of stover supply.

Author(s):
Maggie Davis , Laurence Eaton , Matt Langholtz
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

The Biomass Scenario Model (BSM) is a system dynamics model that represents the entire biomass-to-biofuels supply chain, from feedstock to fuel use. It is a tool designed to better understand biofuels policy as it impacts the development of the supply chain for biofuels in the United States.

This page houses the BSM articles that have been published. For more information, see the link to NREL's list of publications on the BSM.

Vimmerstedt, L. J., Bush, B. W., Hsu, D. D., Inman, D. and Peterson, S. O. (2014), Maturation of biomass-to-biofuels conversion technology pathways for rapid expansion of biofuels production: a system dynamics perspective. Biofuels, Bioprod. Bioref.. doi: 10.1002/bbb.1515
 
 
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Author(s):
NREL

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.

Author(s):
Inman, D.; Vimmerstedt, L.; Bush, B.; Peterson, S.
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

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.

Author(s):
Lin, Y. ; , Newes, E. , Bush, B. , Peterson, S. , Stright, D.
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Biomass Scenario Model: Supplemental Tableau workbook for Christopher M Clark et al 2013 Environ. Res. Lett. 8 025016 doi:10.1088/1748-9326/8/2/025016 Growing a sustainable biofuels industry: economics, environmental considerations, and the role of the Conservation Reserve Program

To explore this file download Tableau reader: http://www.tableausoftware.com/products/reader

This paper describes the current Biomass Scenario Model (BSM) as of August 2013, a system dynamics model developed under the support of the U.S. Department of Energy (DOE). The model is the result of a multi-year project at the National Renewable Energy Laboratory (NREL). It is a tool designed to better understand biofuels policy as it impacts the development of the supply chain for biofuels in the United States.

Author(s):
Peterson, Steve

In support of the national goals for biofuel use in the United States, numerous technologies have been developed that convert biomass to biofuels. Some of these biomass to biofuel conversion technology pathways are operating at commercial scales, while others are in earlier stages of development. The advancement of a new pathway toward commercialization involves various types of progress, including yield improvements, process engineering, and financial performance.

Author(s):
Laura J. Vimmerstedt , Brian W. Bush
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Biofuels are promoted in the United States through aggressive legislation, as one part of an overall strategy to lessen dependence on imported energy as well as to reduce the emissions of greenhouse gases (Office of the Biomass Program and Energy Efficiency and Renewable Energy, 2008). For example, the Energy Independence and Security Act of 2007 (EISA) mandates 36 billion gallons of renewable liquid transportation fuel in the U.S. marketplace by the year 2022 (U.S. Government, 2007).

Author(s):
Emily Newes, Daniel Inman, Brian Bush

Transitioning to a larger biofuels industry requires a robust biomass-to-biofuels system of systems that operates within existing agriculture, forestry, energy, and transportation markets. Using the existing fuel supply chain infrastructure as a framework, this paper discusses a vision for biomass-based fuels and the challenges associated with a massive market and infrastructure transformation.

Author(s):
Riley, Cynthia J. , Sandor, Debra
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

The Biomass Program is one of the nine technology development programs within the Office of Energy Efficiency and Renewable Energy (EERE) at the U.S. Department of Energy (DOE). This 2011 Multi-Year Program Plan (MYPP) sets forth the goals and structure of the Biomass Program. It identifies the research, development, demonstration, and deployment (RDD&D) activities the Program will focus on over the next five years, and outlines why these activities are important to meeting the energy and sustainability challenges facing the nation.

Author(s):
Office of the Biomass Program
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Discussions of alternative fuel and propulsion technologies for transportation often overlook the infrastructure required to make these options practical and cost-effective. We estimate ethanol production facility locations and use a linear optimization model to consider the economic costs of distributing various ethanol fuel blends to all metropolitan areas in the United States. Fuel options include corn-based E5 (5% ethanol, 95% gasoline) to E16 from corn and switchgrass, as short-term substitutes for petroleum-based fuel.

Author(s):
William R. Morrow

Biomass Scenario Model Zotero References
National Renewable Energy Laboratory

Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Understanding the Growth of the Cellulosic Ethanol Industry, D. Sandor and R. Wallace, National Renewable Energy Laboratory, S. Peterson The Peterson Group, Technical Report, NREL/TP-150-42120 April 2008

Author(s):
D. Sandor and R. Wallace National Renewable Energy Laboratory, S. Peterson The Peterson Group

Production costs of bio-ethanol from sugarcane in Brazil have declined continuously over the last three decades. The aims of this study are to determine underlying reasons behind these cost reductions, and to assess whether the experience curve concept can be used to describe the development of feedstock costs and industrial production costs. The analysis was performed using average national costs data, a number of prices (as a proxy for production costs) and data on annual Brazilian production volumes.

Author(s):
J.D. van den Wall Bake

The location of ethanol plants is determined by infrastructure, product and input markets, fiscal attributes of local communities, and state and federal incentives. This empirical
analysis uses probit regression along with spatial clustering methods to analyze investment activity of ethanol plants at the county level for the lower U.S. 48 states from 2000 to 2007.
The availability of feedstock dominates the site selection decision. Other factors, such as access to navigable rivers or railroads, product markets, producer credit and excise tax

Author(s):
D.M. Lambert