Skip to main content

KDF Search Results

Displaying 1 - 11 of 11

Simulations under this dataset were targeted to a specific fuelshed in Iowa.
Integrated land management (ILM) applications were targeted under this research, although the results of these simulations are at the county level; downscaling post-processing will be applied.

Organization:
DOE
Author(s):
Maggie R. Davis
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

Abstract: Cellulosic-based biofuels are needed to help meet energy needs and to strengthen rural investment and development in the midwestern United States (US). This analysis identifies 11 categories of indicators to measure progress toward sustainability that should be monitored to determine if ecosystem and social services are being maintained, enhanced, or disrupted by production, harvest, storage, and transport of cellulosic feedstock.

Author(s):
Virginia H. Dale , Keith L. Kline , Tom L. Richard , Doug L. Karlen
Funded from the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Bioenergy Technologies Office.

There is an inextricable link between energy production and food/feed/fiber cultivation with available water resources. Currently in the United States, agriculture represents the largest sector of consumptivewater usemaking up 80.7%of the total. Electricity generation in the U.S. is projected to increase by 24 % in the next two decades and globally, the production of liquid transportation fuels are forecasted to triple over the next 25-years, having significant impacts on the import/export market and global economies.

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

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,

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

Agroecosystem models that can incorporate management practices and quantify environmental effects
are necessary to assess sustainability-associated food and bioenergy production across spatial scales.
However, most agroecosystem models are designed for a plot scale. Tremendous computational capacity
on simulations and datasets is needed when large scales of high-resolution spatial simulations are conducted.
We used the message passing interface (MPI) parallel technique and developed a master–slave

Author(s):
S. Kang

The US Congress passed the Renewable Fuels Standard (RFS) seven years ago. Since then, biofuels have gone from darling to scapegoat for many environmentalists, policy makers, and the general public. The reasons for this shift are complex and include concerns about environmental degradation, uncertainties about impact on food security, new access to fossil fuels, and overly optimistic timetables. As a result, many people have written off biofuels.

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

The sustainability of future bioenergy production rests on more than continual improvements in its environmental, economic, and social impacts. The emergence of new biomass feedstocks, an expanding array of conversion pathways, and expected increases in overall bioenergy production are connecting diverse technical, social, and policy communities. These stakeholder groups have different—and potentially conflicting—values and cultures, and therefore different goals and decision making processes. Our aim is to discuss the implications of this diversity for bioenergy researchers.

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

The production of biobased feedstocks (i.e., plant– or algal-based material use for transportation fuels, heat, power and bioproducts) for energy consumption has been expanding rapidly in recent years. Biomass now accounts for 4.1% of total U.S. primary energy production. Unfortunately, there are considerable knowledge gaps relative to implications of this industry expansion for wildlife.

Author(s):
Rupp, S. P., L. Bies, A. Glaser, C. Kowaleski, T. McCoy, T. Rentz, S. Riffell, J. Sibbing, J. Verschuyl, and T. Wigley.

ABSTRACT. Adding bioenergy to the U.S. energy portfolio requires long‐term profitability for bioenergy producers and long‐term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a regional scale.

Author(s):
Latha Baskaran

When we think about sustainable bioenergy feedstocks in the United States, we ask ourselves what we will grow, where we will grow it, and how much we will grow. We also must consider the local as well as the broad-scale implications. From the perspective of landscape ecology, we tend to look at the broader scales. It is one of the big challenges of bioenergy, not just looking at what happens to the local farmer but thinking about broader implications. From a global perspective, we also need to ask the same questinos, how much, what type and where?

We present a system dynamics global LUC model intended to examine LUC attributed to biofuel production. The model has major global land system stocks and flows and can be exercised under different food and biofuel demand assumptions. This model provides insights into the drivers and dynamic interactions of LUC, population, dietary choices, and biofuel policy rather than a precise number generator.