Global Ethanol Mandates: Opportunities for U.S. Exports of Ethanol and DDGS
Bibliographic record
Abstract
Before 2001, only Brazil and Paraguay required ethanol to be blended with gasoline for fuel use. With biofuel production still in the nascent stage, these countries were unable to meet those mandates. From 2001 to 2010, ethanol-use mandates adopted by the United States and the European Union (EU), along with favorable market conditions, stimulated a rapid increase in ethanol production in the United States, the EU, and Brazil. By 2016, an additional 26 countries had adopted mandates, and others had set ethanol targets or were using ethanol without an official requirement. Many of these countries have difficulty meeting their mandates with domestic production. Some import ethanol (e.g., Canada and Japan); others have barriers against imports (e.g., Argentina and China). If these countries strive to meet their mandates and open their borders to trade, they could present strong export opportunities for U.S. ethanol, assuming the United States can sufficiently expand production. The United States currently is the world’s largest producer and exporter of ethanol. It also supplies 85 percent of the world’s distillers’ dried grains with solubles (DDGS), a coproduct of grain-based ethanol production that is used in animal feed. This report also discusses the potential for changes in DDGS trade.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".