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The Cost of Producing Lignocellulosic Biomass for EthanolBusby, David Preston 11 August 2007 (has links)
The United States has become dependent on nonrenewable resources such as nuclear, coal, and crude oil as major sources of energy and fuel. Ethanol has been identified as a renewable fuel source that may help alleviate this dependence. Recent technological advances have developed a method to produce ethanol from lignocellulosic biomass. The purpose of this study is to determine production and transportation costs of switchgrass, eastern gammagrass, and giant miscanthus using Mississippi and Oklahoma data. This study also estimated the returns above the cost of feedstock for a biorefinery and the incentive package needed to pay for feedstock and construction cost. Results indicate cost difference across species, method of harvest, and location. The biorefinery returns and the incentive package explain the amount of capital needed for a biorefinery to compensate for the cost of feedstock and construction.
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Producer Preferences For Contracts On A Risky Bioenergy CropKrah, Kwabena 14 August 2015 (has links)
This study employed a stated choice experiment survey to identify southeastern U.S. farmers’ preferences for contracts to produce Giant Miscanthus. We developed a more theoretically consistent framework which takes into account risk preference and perception information and also accounts for heterogeneous status-quo alternatives. Results from our Random Parameter Logit model indicated that price per ton of harvested Giant Miscanthus, biorefinery harvest, and establishment cost-share all had significant positive effects on the probability of a producer accepting a contract to produce Giant Miscanthus, whereas contract length had a significant negative effect. Our analysis also found evidence of significant preference heterogeneity in producers’ preferences for biorefinery harvest, yield insurance, and contract length. We also found that incorporating risk perception and risk preference information, as well as accounting for heterogeneous status-quo alternatives in the decision framework improved overall model performance even though the respective individual coefficients for these variables were not statistically significant.
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