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Record W3175069619 · doi:10.3390/su13126881

Historical Analysis of the Role of Governance Systems in the Sustainable Development of Biofuels in Brazil and the United States of America (USA)

2021· article· en· W3175069619 on OpenAlexaff
Zaman Sajid, Maria Fátima das Graças Fernandes da Silva, Syed Nasir Danial

Bibliographic record

VenueSustainability · 2021
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsBiofuelSustainabilityRenewable energyBusinessGovernment (linguistics)Natural resource economicsAgricultureSustainable developmentAgricultural economicsCorporate governanceBioenergyRenewable resourceEconomic growthEconomicsPolitical scienceEngineeringGeographyWaste management

Abstract

fetched live from OpenAlex

The United States of America and Brazil are the world’s first and second-largest biofuels producers. The United States (U.S.) has dedicated a significant portion of agricultural land for crops to produce biodiesel, while Brazil has been using sugar cane as raw material to produce ethanol. To make the world’s top producers in global biofuel markets, various institutions in each country have played significant roles. These institutions include renewable energy legislators, bioenergy policymakers, and energy ministries of their governments. This study delineates the historical role of these institutions responsible for the sustainable development of biofuel industries in both countries. It also provides an overview of economic impacts as a result of institutional decisions. The study reveals that systematic legislations and sustainable and robust renewable energy policies of government institutions have helped the U.S. and Brazil to boost their bio-economies. As both countries intend to keep expanding their biofuel productions, the role of key government institutions is vital in the sustainability of biofuels.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.100
Threshold uncertainty score0.969

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.205
Teacher spread0.200 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations88
Published2021
Admission routes1
Has abstractyes

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