Biodiesel in Brazil: A Market Analysis and Its Economic Effects
Bibliographic record
Abstract
The commercial production of biodiesel in Brazil began in 2005 and increased in such a way that the country has become one of the largest producers in terms of volume in 2012. This study aims to analyze the biodiesel market in Brazil and its socio-economic effects resulting from the mandatory addition of biodiesel in the distribution system of mineral diesel. This work is characterized as a qualitative and exploratory study. The technical procedures adopted include literature research and data collection from government agencies involved. It is highlighted that the Programa Nacional de Produção e Uso de Biodiesel no Brasil - PNPB (“National Program for Production and Use of Biodiesel in Brazil”) was designed for the purposes of: promoting rural development; the growth of biodiesel production plants; and positively impact the environment. The survey showed the following results: i) significant increase in investment in renewable energy, especially biodiesel, ii) a high unused capacity in the biodiesel production plants; iii) a high dependence on soy for biodiesel production, iv) unfavorable prices of oilseeds; v) high concentration of the biodiesel market; vi) difficulty in standardizing the biodiesel auctions; vii) a small increase in inflation; viii) increased generation of income, employment and Gross Domestic Product (GDP) due to the presence of biodiesel in the energy market. It was found that by adding a percentage of biodiesel to diesel oil caused positive impacts on the national economy, contributing to improvement in employment policies, income distribution, environmental issues, technological and regional development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".