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Record W2113889274 · doi:10.5539/mas.v8n1p36

Biofuel Policy as a Key Driver for Sustainable Development in the Biofuel Sector: The Missing Ingredient in Zimbabwe’s Biofuel Pursuit

2013· article· en· W2113889274 on OpenAlexvenueno aff
Phanankosi Moyo, Mahluli Moyo, Donatus Dube, Oswell Rusinga

Bibliographic record

VenueModern Applied Science · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicEnergy and Environment Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsBiofuelNatural resource economicsFossil fuelAviation biofuelSustainable developmentGreenhouse gasBusinessAgricultural economicsEconomicsBioenergyPolitical scienceEngineeringEcologyWaste management

Abstract

fetched live from OpenAlex

As global warming continues to be a challenge, there is urgency to look into fossil fuel alternatives for sustainable energy supplies which have also been hastened by the volatility of crude oil prices and the fact that fossil fuel depletion is clearly in sight. Zimbabwe has not been spared of the negative effects of climate change which have affected both the food and energy sectors. This has proved to be a major challenge for the sustainable development of the country’s economy. In an effort to be energy independent, the country has for over decades looked into the prospect of utilising its vast biomass resources to produce biofuels. These programmes have been met with a number of challenges which has seen most such projects not succeeding. The current paper gives a narration of the history of biofuel programmes in Zimbabwe looking at their driving factors and reasons that led to their collapses. Lack of a national biofuel policy is identified as the major factor that has been the missing link in the past and must be addressed. The paper goes further to call for the crafting of a National Biofuel Policy for Zimbabwe highlighting the major thematic issues it would need to cover. An implementation plan for the programme has been proposed.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.639
Threshold uncertainty score0.730

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
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 head0.012
GPT teacher head0.227
Teacher spread0.215 · 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

Citations10
Published2013
Admission routes1
Has abstractyes

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