Link to the website with documentation and download instructions for the PNNL Global Change Assessment Model (GCAM), a community model or long-term, global energy, agriculture, land use, and emissions. BioEnergy production, transformation, and use is an integral part of GCAM modeling and scenarios.
This dataset reports the pre-treatment hydrology and pre- and post-treatment water quality data from a watershed-scale experiment that is evaluating the effects of growing short-rotation loblolly pine for bioenergy on water quality and quantity in the southeastern U.S. The experiment is taking place on the Savannah River Site, near New Ellenton, South Carolina, USA. Beginning in 2010, water quality and hydrology were measured for two years in 3 watersheds (R, B, C).
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.
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
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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 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.
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.
Defining and measuring sustainability of bioenergy systems are difficult because the systems are complex, the science is in early stages of development, and there is a need to generalize what are inherently contextspecific enterprises. These challenges, and the fact that decisions are being made now, create a need for improved communications among scientists as well as between scientists and decision makers.
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.
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.
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.
Gridded Population of the World, Version 3 (GPWv3) consists of estimates of human population for the years 1990, 1995, and 2000 by 2.5 arc-minute grid cells and associated datasets dated circa 2000. The data products include population count grids (raw counts), population density grids (per square km), land area grids (actual area net of ice and water), mean administrative unit area grids, centroids, a national identifier grid, national boundaries, and coastlines.
The Global Rural-Urban Mapping Project (GRUMP), Alpha Version consists of estimates of human population for the years 1990, 1995, and 2000 by 30 arc-second (1km) grid cells and associated datasets dated circa 2000. The data products include population count grids (raw counts), population density grids (per square km), land area grids (actual area net of ice and water), mean geographic unit area grids, urban extents grids, centroids, a national identifier grid, national boundaries, coastlines, and settlement points.
USDA Agricultural Projections for 2011-20, released in February 2011, provide longrun projections for the farm sector for the next 10 years. These annual projections cover agricultural commodities, agricultural trade, and aggregate indicators of the sector, such as farm income and food prices.
Important assumptions for the projections include:
The most frequently used climate classification map is that ofWladimir Köppen, presented in its latest version
1961 by Rudolf Geiger. A huge number of climate studies and subsequent publications adopted this or a
former release of the Köppen-Geiger map. While the climate classification concept has been widely applied
to a broad range of topics in climate and climate change research as well as in physical geography, hydrology,
agriculture, biology and educational aspects, a well-documented update of the world climate classification
In a previous paper we presented an update of the highly referenced climate classification map, that of Wladimir Koppen, which was published for the first time in 1900 and updated in its latest version by Rudolf Geiger in 1961. This updated world map of Koppen-Geiger climate classification was based on temperature and precipitation observations for the period 1951–2000.