Implied Objectives of U.S. Biofuel Subsidies

Biofuel subsidies in the United States have been justified on the following grounds: energy independence, a reduction in greenhouse gas emissions, improvements in rural development related to biofuel plants, and farm income support. The 2007 energy act emphasizes the first two objectives. In this study, we quantify the costs and benefits that different biofuels provide. We consider the first two objectives separately and show that each can be achieved with a lower social cost than that of the current policy. Then, we show that there is no evidence to disprove that the primary objective of biofuel policy is to support farm income. Current policy favors corn production and the construction of corn-based ethanol plants. We find that favoring corn happens to be the best way to remove land from food and feed production, thus providing higher commodity prices and income to farmers and landowners. Next, we calculate two sets of alternative biofuel subsidies that are targeted to meeting income transfer objectives and either greenhouse gas emission reductions or fuel energy reductions. The first of these assumes that greenhouse gas emissions and high crop prices are joint objectives, and the second assumes that fuel independence and high crop prices are the joint objectives. Finally, we infer the social willingness to pay for biofuel services. This, in turn, allows us to propose a subsidy schedule that maintains (inferred) social preferences and provides a higher incentive for farmers to choose production of cellulosic materials. This is particularly relevant since the 2007 energy act sets a renewable fuels standard that relies heavily on cellulosic biofuel but does not specify a higher per gallon incentive to producers

Statistical Learning Algorithms for Forensic Scientists

Learning Overview: The goals of this workshop are to: (1) introduce attendees to the basics of supervised learning algorithms in the context of forensic applications, including firearms and footwear examination and trace evidence, while placing emphasis on classification trees, random forests, and, time permitting, neural networks; (2) introduce the concept of a similarity score to quantify the similarity between two items; (3) show how learning algorithms can be trained to classify objects into pre-determined classes; (4) discuss limitations of Machine Learning (ML) algorithms and introduce methods for assessing their performance; and (5) discuss the concept of a Score-based Likelihood Ratio (SLR): computation, advantages, and limitations

Sensitivity of Carbon Emission Estimates from Indirect Land-Use Change

We analyze the sensitivity of greenhouse gas (GHG) emissions from land-use change to modifications in assumptions concerning crop area, yield, and deforestation. For this purpose, we run a modified version of the Center for Agricultural and Rural Development (CARD) Agricultural Outlook Model, which was used previously to assess the impacts of energy price increases and biofuel policy changes on land conversion. To calculate the GHG implications of agricultural activity, we use GreenAgSiM, a model developed to evaluate emissions from land conversion and agricultural production. Both models are applied to scenarios that lead to higher US ethanol production. The results are contrasted with the findings of Searchinger et al., and we explain the role of model assumptions to elucidate the differences. We find that the payback period of corn ethanol’s carbon debt is sensitive to assumptions concerning land conversion and yield growth and can range from 31 to 180 years.biofuel, crop yield, greenhouse gas emissions, indirect land-use change.

Biofuel Expansion, Fertilizer Use, and GHG Emissions: Unintended Consequences of Mitigation Policies

Increased biofuel production has been associated with direct and indirect land-use change, changes in land management practices, and increased application of fertilizers and pesticides. This has resulted in negative environmental consequences in terms of increased carbon emissions, water quality, pollution, and sediment loads, which may offset the pursued environmental benefits of biofuels. This study analyzes two distinct policies aimed at mitigating the negative environmental impacts of increased agricultural production due to biofuel expansion. The first scenario is a fertilizer tax, which results in an increase in the US nitrogen fertilizer price, and the second is a policy-driven reversion of US cropland into forestland (afforestation). Results show that taxing fertilizer reduces US production of nitrogen-intensive crops, but this is partially offset by higher fertilizer use in other countries responding to higher crop prices. In the afforestation scenario, crop production shifts from high-yielding land in the United States to low-yielding land in the rest of the world. Important policy implications are that domestic policy changes implemented by a large producer like the United States can have fairly significant impacts on the aggregate world commodity markets. Also, the law of unintended consequences results in an inadvertent increase in global greenhouse gas emissions