Politiques pro-biocarburants et climatique américaines : impact sur les choix énergétiques du Brésil et des Etats-Unis et bilan carbone
Bibliographic record
Abstract
In this paper, a partial trade equilibrium model is developed to explore the impacts of US energy policies on the use and trade of first-generation biofuels (ethanol) and second-generation biofuels (ligno-cellulosic ethanol) in the United-States and Brazil. In addition, we investigate their impacts on direct and indirect carbon emissions. The first policy is the biofuels mandatory target. The second defines a cap on carbon emissions. Our study reveals that the biofuels mandatory target encourages ligno-cellulosic ethanol production, reductions in carbon emissions being marginal. The second policy increases energy prices leading to a decrease in energy consumption as well as in direct carbon emissions. However, this policy has a significant impact on deforestation in Brazil resulting in a rise in indirect carbon emissions. The biofuels subsidy needed to reach the mandatory target amounts to US $ 1.1 per gallon. The US carbon tax reaches US $ 120 per ton equivalent carbon. A differential tax is imposed on gasoline, ethanol and ligno-cellulosic ethanol based on the carbon content. It is respectively equal to US $ 0.38, US $ 0.204 and US $ 0.024.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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".