The economic potential for Eucalyptus spp. production for jet fuel additives in the United States: A 20 year projection suite of scenarios ranging from $110 Mg-1 to $220 Mg-1 utilizing the POLYSYS model.
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This workshop examines the potential benefits, feasibility, and barriers to the use of biofuels in place of heavy fuel oil (HFO) and marine gas oil for marine vessels. More than 90% of world’s shipped goods
travel by marine cargo vessels powered by internal combustion (diesel) engines using primarily low-cost residual HFO, which is high in sulfur content. Recognizing that marine shipping is the largest source of
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.
This spreadsheet serves as an Input file to the National Renewable Energy Laboratory's Waste-to-Energy System Simulation (WESyS) model developed in Stella Pro (isee systems, Lebanon, NH). WESyS is a national-level system dynamics model that simulates energy production from three sectors of the U.S. waste-to-energy industry: landfills, confined animal feeding operations (CAFOs), and publically owned treatment works (POTWs).
We propose a causal analysis framework to increase understanding of land-use change (LUC) and the reliability of LUC models. This health-sciences-inspired framework can be applied to determine probable causes of LUC in the context of bioenergy. Calculations of net greenhouse gas (GHG) emissions for LUC associated with biofuel production are critical in determining whether a fuel qualifies as a biofuel or advanced biofuel category under regional (EU), national (US, UK), and state (California) regulations.
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.
Potential Avenues for High Biofuels Penetration in the U.S. Aviation Market, Supplemental Tableau Workbook, 2016
Emily Newes, National Renewable Energy Laboratory Jeongwoo Han, Argonne National Laboratory Steve Peterson, Lexidyne LLC
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.
The development of modern high efficiency bioenergy technologies has the
potential to improve energy security and access while reducing environmental impacts
and stimulating low-carbon development. While modern bioenergy production is
increasing in the world, it still makes a small contribution to our energy matrix.
At present, approximately 87% of energy demand is satisfied by energy produced
through consumption of fossil fuels. Although the International Energy Agency (IEA)
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.
The paper describes an approach to landscape design that focuses on integrating bioenergy production with their components of environmental, social and economic systems. Landscape design as used here refers to a spatially explicit, collaborative plan for management of landscapes and supply chains. Landscape design can involve multiple scales and build on existing practices to reduce costs or enhance services.
Understanding how large-scale bioenergy production can affect biodiversity and ecosystems is important if society is to meet current and future sustainable development goals. A variety of bioenergy production systems have been established within different contexts throughout the Pan American region, with wide-ranging results in terms of documented and projected effects on biodiversity and ecosystems.
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.
A framework for selecting and evaluating indicators of bioenergy sustainability is presented.
This framework is designed to facilitate decision-making about which indicators are useful for assessing
sustainability of bioenergy systems and supporting their deployment. Efforts to develop sustainability
indicators in the United States and Europe are reviewed. The fi rst steps of the framework for
indicator selection are defi ning the sustainability goals and other goals for a bioenergy project or program,
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 with all land transformation activities, effects on biodiversity and ecosystem services of producing feedstocks for biofuels are highly variable and context specific. Advances toward more sustainable biofuel production benefit from a system's perspective, recognizing spatial heterogeneity and scale, landscape-design principles, and addressing the influences of context such as the particular products and their distribution, policy background, stakeholder values, location, temporal influences, and baseline conditions. Deploying biofuels in a manner to reduce effects on biodiversity
Abstract: Farmgate prices (i.e. price delivered roadside ready for loading and transport) for biomass feedstocks directly infl uence biofuel prices. Using the latest available data, marginal (i.e. price for the last ton) farmgate prices of $51, $63, and $67 dry ton–1 ($2011) are projected as necessary to provide 21 billion gallons of biofuels from about 250 million dry tons of terrestrial feedstocks in 2022 under price-run deterministic, demand-run deterministic, and stochastic simulations, respectively.
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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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.