Measuring the Indirect Land-Use Change Associated with Increased Biofuel Feedstock Production: A Review of Modeling Efforts: Congressional Report
DIANE Publishing, 2011 - 56 pages
Summarizes the current state of knowledge of the drivers of land-use change and describes the analytic methods used to estimate the impact of biofuel feedstock production on land use. The larger the impact of domestic biofuels feedstock production on commodity prices and the availability of exports, the larger the international land-use effects are likely to be. The amount of pressure placed on land internationally will depend in part on how much of the land needed for biofuel production is met through an expansion of agricultural land in the U.S. If crop yield per acre increases through more intensive management or new crop varieties, then less land is needed to grow a particular amount of that crop. Illustrations. This is a print on demand report.
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aggregate agricultural agricultural land agricultural production assess assumptions baseline billion gallons biodiesel bioenergy feedstocks biofuel feedstock production biofuel policy biomass Brazil CARB carbon cellulosic biofuel cellulosic ethanol cLCA competition coproduct corn ethanol production countries crop production cropland DDGs domestic and international domestic biofuels drivers of land-use effects EISA elasticity energy equilibrium model estimates of land-use expansion exports factors FAPRI FASOM food demand forest forestry fossil fuels future land-use gasoline GHG emissions greenhouse gas GTAP model Hertel increased biofuel production indirect land-use change inputs international land-use change internationally land conversion land demand land required land-use change associated land-use impacts life-cycle livestock market-mediated modeling efforts modeling frameworks newly converted land parameters Partial equilibrium pasture patterns percent potential projections reduced regional regulatory Regulatory Impact Analysis relative Renewable Fuel Standard scenarios Searchinger sector modeling sensitive significant sources soybean supply technologies trade uncertainty associated United Updated USDA variables