This dissertation study investigates the impact of the US biofuel policies related to greenhouse gas (GHG) emission regulation, tax credit and renewable fuel standard (RFS2) mandate over production and consumption of ethanol as well as technical and environmental performance of corn ethanol plants. The study develops analytical models and provides quantitative estimation of the impact of various biofuel policies in each of the three chapters.;Chapter 1 of this dissertation examines the tradeoff between achieving the environmental goal of minimizing life cycle GHG emissions and minimizing production costs in recently built dry-grind corn ethanol plants. The results indicate that the average ethanol plant is able to reduce GHG emissions by 36 % relative to the level under cost minimization, but production costs are 22 % higher. To move from least cost to least emissions allocations, ethanol plants would on average produce 25 % more of wet byproduct and 47% less of dry byproduct.;Using a multi-output, multi-input partial equilibrium model, Chapter 2 explores the impact of the tax credit and RFS2 mandate policy on market price of ethanol, byproducts, corn, and other factor inputs employed in the production of corn ethanol. In the short-run, without tax credit ethanol plants will not have the incentive to produce the minimum level of ethanol required by RFS2. In the long-run, if ethanol plants to have the incentive to produce the minimum RFS2 mandate without tax credit policy, gasoline price will need to increase by order of 50% or more relative to the 2011 price.;Chapter 3 develop meta-regression model to investigate the extent to which statistical heterogeneity among results of multiple studies on soil organic carbon (SOC) sequestration rates can be related to one or more characteristics of the studies in response to conventional tillage (CT) and no-till (NT). Regarding the difference in the rate of SOC sequestration between NT and CT, our results shows that the percentage of heterogeneity in the true treatment effect that is attributable to between-study variability is 49%, whereas 51 % is attributable to within-study sampling variability.
展开▼
