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Brazilian Biofuels: Distilling Solutions

2018· book-chapter· en· W3102374160 on OpenAlexaboutno aff
Kathleen Araújo

Bibliographic record

VenueOxford University Press eBooks · 2018
Typebook-chapter
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural and Food Sciences
Canadian institutionsnot available
Fundersnot available
KeywordsBiofuelQuarter (Canadian coin)PopulationGovernment (linguistics)Agricultural economicsAutomotive industryMarket shareBusinessEconomyNatural resource economicsEconomicsEngineeringGeographyWaste managementFinance

Abstract

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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).

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.028
Threshold uncertainty score0.077

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0230.006

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.

Opus teacher head0.044
GPT teacher head0.191
Teacher spread0.147 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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