Bibliographic record
Abstract
Worldwide, transportation accounts for roughly a quarter of the total final energy demand and a similar share of energy-based carbon dioxide emissions (IEA, 2016f). The transport sector has the most homogenous of fuel mixes, with petroleum-based products accounting for roughly 95% of the overall final share (Kahn Ribeiro et al., 2012). Biofuels and other options, like electric vehicles, have the potential to displace a notable portion of petroleum and CO2 emissions in the transport sector. Global use of ethanol, the most widely used among biofuels, has grown significantly in recent years. Between 2000 and 2010 alone, ethanol utilization increased 350% worldwide, with trade increasing by a factor of 5 and usage equaling 74 billion liters in 2010 (Valdes, 2011). This chapter examines the underlying roots of the biofuels transition in Brazil. Two micro-shifts—one that is government- led and a second that is industry-led—are evaluated, demonstrating how a new, energy market and industry can develop at a national scale through the retooling of existing industries and infrastructure. Insights on policy inflections, market longevity, and dual-use technology are also covered. Brazil is the historical leader in biofuels and the only country to substantially alter its automotive fuel mix with ethanol, shifting from 1% in 1970 to 34% in 2014 (see the section entitled “Modern Transition” later in this chapter). Ranked sixth globally for its population of roughly 206 million people and eighth for its economy of $3.1 trillion in mid-2016 (CIA, n.d.), Brazil has been a leading pioneer in the production and export of ethanol, its principal biofuel. In 2015, Brazilian ethanol equaled 28% of the global supply (Renewable Fuel Association [RFA], 2016). The country is known for having the lowest production costs of ethanol (Goldemberg, 2008; Shapouri, and Salassi, 2006; Valor International, 2014). Brazil also has a unique distribution network of more than 35,000 fuel stations supplying the renewable fuel (Agência Nacional do Petróleo, Gás Natural e Biocombustíveis, 2008).
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".