database

The goal of this repository is to promote transparency and ease-of-access to the U.S. Department of Energy Bioenergy Technologies Office (BETO) supported public studies involving techno-economic analysis (TEA). As such, this database summarizes the economic and technical parameters associated with the modeled biorefinery processes for the production of biofuels and bioproducts, as presented in a range of published reports and papers. The database serves as a quick reference tool by documenting and referencing the results of techno-economic analyses from the national laboratories and in peer-reviewed journals.
 
The analyses presented in this database may be distinguished in several regards, such as cost year, feedstock cost, and financial assumptions (tax rate, percent equity, project lifetime, etc.), and reflect details as they were provided in the original studies. Accordingly, the intent of this database is not to directly compare one technology pathway against another, and caution should be taken in interpreting the outputs as such.

Funding Acknowledgement
This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by  the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Bioenergy Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. The U.S. Government retains and the publisher, by accepting the article for publication, acknowledges that the U.S. Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for U.S. Government purposes.

FAOSTAT provides time-series and cross sectional data relating to food and agriculture for some 200 countries.

The national version of FAOSTAT, CountrySTAT, is being developed and implemented in a number of target countries, primarily in sub-saharan Africa. It will offer a two-way data exchange facility between countries and FAO as well as a facility to store data at the national and sub-national levels.

This database contains current and historical official USDA data on production, supply and distribution of agricultural commodities for the United States and key producing and consuming countries.

United States is experiencing increasing interests in fermentation and anaerobic digestion processes for the production of biofuels. A simple methodology of spatial biomass assessment is presented in this paper to evaluate biofuel production and support the first decisions about the conversion technology applications. The methodology was applied to evaluate the potential biogas and ethanol production from biomass in California and Washington states. Solid waste databases were filtered to a short list of digestible and fermentable wastes in both states. Maximum methane and ethanol production rates were estimated from biochemical and ultimate analysis of each waste and projected on a GIS database. Accordingly, the optimal locations for methane and ethanol production plants were approximately determined.

The available net power for transportation and electricity generation was evaluated considering three process efficiency factors in the waste to power life cycle. The net power from methane and ethanol would ultimately cover ~ 6 - 8% of the transportation needs for motor gasoline or cover ~ 3 - 4% of the electrical power consumption in each state.

Converting forest lands into bioenergy agriculture could accelerate climate change by emitting carbon stored in forests, while converting food agriculture lands into bioenergy agriculture could threaten food security. Both problems are potentially avoided by using abandoned agriculture lands for bioenergy agriculture. Here we show the global potential for bioenergy on abandoned agriculture lands to be less than 8% of current primary energy demand, based on historical land use data, satellite-derived land cover data, and global ecosystem modeling. The estimated global area of abandoned agriculture is 385?472 million hectares, or 66?110% of the areas reported in previous preliminary assessments. The area-weighted mean production of above-ground biomass is 4.3 tons ha?1 y?1, in contrast to estimates of up to 10 tons ha?1 y?1 in previous assessments. The energy content of potential biomass grown on 100% of abandoned agriculture lands is less than 10% of primary energy demand for most nations in North America, Europe, and Asia, but it represents many times the energy demand in some African nations where grasslands are relatively productive and current energy demand is low.

The preceding two chapters of this volume have discussed physical and economic data bases for global agriculture and forestry, respectively. These form the foundation for the integrated, global land use data base discussed in this chapter. However, in order to utilize these data for global CGE analysis, it is first necessary to integrate them into a global, general equilibrium data base. This integration is the subject of the present chapter

In recent years, considerable concern has been raised about the sustainability of the world's forested ecosystems (FAO, 2003). With deforestation rates in tropical regions estimated to be as high as 12 million hectares per year (FAO, 2003; Houghton, 2003), much of the concern has centered around tropical deforestation. In contrast to these developments in tropical areas, there is evidence that the area of forests in temperate regions is expanding. Given the large potential storage of carbon in both temperate and tropical forests, these changes in land use can potentially lead to large fluxes of carbon both into and out of forests (Houghton, 2003; Plattner et al. 2002; Dixon et al., 1994). In addition to the potential carbon fluxes, forest management and land use change influences a host of other local and global environmental impacts.

Agricultural activities have dramatically altered our planet?s land surface. To understand the extent and spatial distribution of these changes, we have developed a new global data set of croplands and pastures circa 2000 by combining agricultural inventory data and satellite-derived land cover data. The agricultural inventory data, with much greater spatial detail than previously available, is used to train a land cover classification data set obtained by merging two different satellite-derived products (Boston University?s MODIS-derived land cover product and the GLC2000 data set). Our data are presented at 5 min ( 10 km) spatial resolution in longitude by longitude, have greater accuracy than previously available, and for the first time include statistical confidence intervals on the estimates. According to the data, there were 15.0 (90% confidence range of 12.2?17.1) million km2 of cropland (12% of the Earth?s ice-free land surface) and 28.0 (90% confidence range of 23.6?30.0) million km2 of pasture (22%) in the year 2000.

The Center for Sustainability and the Global Environment (SAGE) at the University of Wisconsin has been developing global databases of contemporary and historical agricultural land use and land cover. SAGE has chosen to focus on agriculture because it is clearly the predominant land use activity on the planet today, and provides a vital service?i.e., food?for human societies. SAGE has developed a ?data fusion? technique to integrate remotely-sensed data on the world?s land cover with administrative-unit-level inventory data on land use (Ramankutty and Foley, 1998; Ramankutty and Foley, 1999; Ramankutty et al., in press). The advent of remote sensing data has been revolutionary in providing consistent, global, estimates of the patterns of global land cover. However, remote sensing data are limited in their ability to resolve the details of agricultural land cover from space. Therein lies the strength of the ground-based inventory data, which provide detailed estimates of agricultural land use practices. However, inventory data are limited in not being spatially explicit, and these data are also plagued by problems of inconsistency across administrative units. The ?data fusion? technique developed by SAGE exploits the strengths of both the remotely-sensed data as well as the inventory data.