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Title: Vision of Biofuels and Biorefining in the US: Opportunities for Biomass


No abstract prepared.

Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
OSTI Identifier:
DOE Contract Number:
Resource Type:
Journal Article
Resource Relation:
Journal Name: In Vitro Cellular and Developmental Biology - Animal; Journal Volume: 43; Journal Issue: 2007; Related Information: Plant Symposia Abstract P-1
Country of Publication:
United States

Citation Formats

Darzins, A. Vision of Biofuels and Biorefining in the US: Opportunities for Biomass. United States: N. p., 2007. Web.
Darzins, A. Vision of Biofuels and Biorefining in the US: Opportunities for Biomass. United States.
Darzins, A. Mon . "Vision of Biofuels and Biorefining in the US: Opportunities for Biomass". United States. doi:.
title = {Vision of Biofuels and Biorefining in the US: Opportunities for Biomass},
author = {Darzins, A.},
abstractNote = {No abstract prepared.},
doi = {},
journal = {In Vitro Cellular and Developmental Biology - Animal},
number = 2007,
volume = 43,
place = {United States},
year = {Mon Jan 01 00:00:00 EST 2007},
month = {Mon Jan 01 00:00:00 EST 2007}
  • Rising energy prices and depleting reserves of fossil fuels continue to renew interest in the conversion of biomass to biofuels production. Biofuels derived from renewable feedstocks are environmentally friendly fuels and have the potential to meet more than a quarter of world demand for transportation fuels by 2050. Moreover, biofuels are expected to reduce reliance on imported petroleum, reduce greenhouse gas emissions, and stimulate regional economies by creating jobs and increasing demand and prices for bioproducts.
  • The Biomass Scenario Model (BSM) is a system-dynamics simulation model intended to explore the potential for rapid expansion of the biofuels industry. The model is not predictive — it uses scenario assumptions based on various types of data to simulate industry development, emphasizing how incentives and technological learning-by-doing might accelerate industry growth. The BSM simulates major sectors of the biofuels industry, including feedstock production and logistics, conversion, distribution, and end uses, as well as interactions among sectors. The model represents conversion of biomass to biofuels as a set of technology pathways, each of which has allowable feedstocks, capital and operatingmore » costs, allowable products, and other defined characteristics. This study and the BSM address bioenergy modeling analytic needs that were identified in recent literature reviews. Simulations indicate that investments are most effective at expanding biofuels production through learning-by-doing when they are coordinated with respect to timing, pathway, and target sector within the biofuels industry. Effectiveness metrics include timing and magnitude of increased production, incentive cost and cost effectiveness, and avoidance of windfall profits. Investment costs and optimal investment targets have inherent risks and uncertainties, such as the relative value of investment in more-mature versus less mature pathways. These can be explored through scenarios, but cannot be precisely predicted. Dynamic competition, including competition for cellulosic feedstocks and ethanol market shares, intensifies during times of rapid growth. Ethanol production increases rapidly, even up to Renewable Fuel Standards-targeted volumes of biofuel, in simulations that allow higher blending proportions of ethanol in gasoline-fueled vehicles. Published 2014. This document is a U.S. Government work and is in the public domain in the USA. Biofuels, Bioproducts, Biorefining published by John Wiley & Sons, Ltd on behalf of Society of Chemical Industry.« less
  • Quantification of the various impacts of biofuel feedstock production on hydrology and water quality is complex. Mathematical models can be used to efficiently evaluate various what if scenarios related to biofeedstock production and their impacts on hydrology and water quality at various spatial and temporal scales. Currently available models, although having the potential to serve such purposes, have many limitations. In this paper, we review the strengths and weaknesses of such models in light of short- and long term biofeedstock production scenarios. The representation of processes in the currently available models and how these processes need to be modified tomore » fully evaluate various complex biofeedstock production scenarios are discussed. Similarly, issues related to availability of data that are needed to parameterize and evaluate these models are presented. We have presented a vision for the development of decision support tools and ecosystem services that can be used to make watershed management decisions to minimize any potentially adverse environmental impacts while meeting biofeedstock demands. We also discuss a case study of biofeedstock impact simulation in relation to watershed management policy implications for various state and federal agencies in the USA.« less