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The U.S. Department of Energy’s (DOE’s) Co-Optimization (Co-Optima) initiative is accelerating the introduction of affordable, scalable, and sustainable fuels and high-efficiency, low-emission engines with a first-of-its-kind effort to simultaneously tackle fuel and engine research and development (R&D).
This report provides a status of the markets and technology development involved in growing a domestic bioenergy economy as it existed at the end of calendar year 2013. It compiles and integrates information to provide a snapshot of the current state and historical trends influencing the development of bioenergy markets. This information is intended for policy-makers as well as technology developers and investors tracking bioenergy developments. It also highlights some of the key energy and regulatory drivers of bioenergy markets.
Conventional feedstock supply systems exist and have been developed for traditional agriculture and forestry systems. These conventional feedstock supply systems can be effective in high biomass-yielding areas (such as for corn stover in Iowa and plantation-grown pine trees in the southern United States), but they have their limits, particularly with respect to addressing feedstock quality and reducing feedstock supply risk to biorefineries. They also are limited in their ability to efficiently deliver energy crops.
Peer-reviewed letter written in response to a March 11, 2015, letter to US EPA Administrator Gina McCarthy (http://bit.ly/1HsSaWf), in which the Ecological Society of America objected to EPA’s proposal that sustainably harvested woody biomass could reduce carbon emissions.
For analyzing sustainability of algal biofuels, we identify 16 environmental indicators that fall into six categories: soil quality, water quality and quantity, air quality, greenhouse gas emissions, biodiversity, and productivity. Indicators are selected to be practical, widely applicable, predictable in response, anticipatory of future changes, independent of scale, and responsive to management.
As U.S. energy policy turns to bioenergy, and second-generation biofuels in particular, to foster energy security and environmental benefits, consideration should be given to the implications of climate risk for the incipient bioenergy industry. As a case-in-point, we review evidence from the 2012 U.S. drought, underscoring the risk of extreme weather events to the agricultural sector in general, and the bioenergy supply chain in particular, including reductions in feedstock production and higher prices for agricultural commodities and biofuels.
Landscape design provides an approach under which bioenergy production systems can be integrated into other components of the land, environment and socioeconomic system. Landscape design is a spatially explicit collaborative plan for resource allocation and management. It should be applied to a particular area and developed with the involvement of key stakeholders.
A global energy crop productivity model that provides geospatially explicit quantitative details on biomass
potential and factors affecting sustainability would be useful, but does not exist now. This study describes a
modeling platform capable of meeting many challenges associated with global-scale agro-ecosystem modeling.
We designed an analytical framework for bioenergy crops consisting of six major components: (i) standardized
natural resources datasets, (ii) global field-trial data and crop management practices, (iii) simulation units and
Potential global biodiversity impacts from near-term gasoline production are compared to biofuel, a renewable liquid transportation fuel expected to substitute for gasoline in the near term (i.e., from now until c.
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.
The Biomass Energy Data Book is a statistical compendium prepared and published by Oak Ridge National Laboratory (ORNL) under contract with the Biomass Program in the Energy Efficiency and Renewable Energy (EERE) program of the Department of Energy (DOE). Designed for use as a convenient reference, the book represents an assembly and display of statistics and information that characterize the biomass industry, from the production of biomass feedstocks to their end use, including discussions on sustainability.
Indicators are needed to assess environmental sustainability of bioenergy systems. Effective indicators
will help in the quantification of benefits and costs of bioenergy options and resource uses. We identify
19 measurable indicators for soil quality, water quality and quantity, greenhouse gases, biodiversity, air
quality, and productivity, building on existing knowledge and on national and international programs
that are seeking ways to assess sustainable bioenergy. Together, this suite of indicators is hypothesized
Land-use change (LUC) estimated by economic models has sparked intense international debate. Models estimate how much LUC might be induced under prescribed scenarios and rely on assumptions to generate LUC values. It is critical to test and validate underlying
The U.S. Department of Energy Biomass Program sponsored the Land-Use Change and Bioenergy workshop in Vonore, Tennessee, from May 11 to May 14, 2009. More than 50 experts from around the world gathered to review the state of the science, identify opportunities for collaboration, and prioritize next steps for the research and data needed to address key issues regarding the land-use effects of bioenergy policies. A key outcome of the workshop was the identification of research areas that may improve our understanding of land-use change in a bioenergy context.
Despite recent claims to the contrary, plant-based fuels developed in economically and environmentally sensible ways can contribute significantly to the nation’s— indeed, the world’s—energy security while providing a host of benefits for many people worldwide.
IN THEIR REPORTS IN THE 29 FEBRUARY ISSUE (“LAND CLEARING AND THE BIOFUEL CARBON debt,” J. Fargione et al., p. 1235, and “Use of U.S. croplands for biofuels increases greenhouse gases through emissions from land-use change,” T. Searchinger et al., p. 1238), the authors do not provide adequate support for their claim that biofuels cause high emissions due to land-use change. The conclusions of both papers depend on the misleading premise that biofuel production causes forests and grasslands to be converted to agriculture.
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.
Biomass is a significant contributor to the US economy--agriculture, forest and paper products, food and related products account for 5% of our GDP. While the forest products industry self generates some of their energy, other sectors are importers. Bioenergy can contribute to economic development and to the environment. Examples of bioenergy routes suggest that atmospheric carbon can be cycled through biofuels in carefully designed systems for sustainability. Significant potential exists for these options.
Abstract: To ensure effective biomass feedstock provision for large-scale ethanol production, a three-stage supply chain was proposed to include biomass supply sites, centralized storage and preprocessing (CSP) sites, and biorefi nery sites. A GIS-enabled biomass supply chain optimization model (BioScope) was developed to minimize annual biomass-ethanol production costs by selecting the optimal numbers, locations, and capacities of farms, CSPs, and biorefi neries as well as identifying the optimal biomass fl ow pattern from farms to biorefi neries.