Biofuels Production in Nigeria: The Policy and Public Opinions
Bibliographic record
Abstract
In order to reduce the country’s over dependence on oil and gas economy and establish a strong link between the downstream petroleum industry and agricultural activities, the Nigerian government has recently indicated commitment to biofuels production from local feedstock. Emphasis was given to bioethanol and biodiesel with projected annual local market possibility of 5.04 billion and 900 million Liters respectively. The study reports an over view of the biofuels policy and a survey of the public opinions on the potential impacts of its implementation. A questionnaire containing six research questions, covering the key positive and negative impacts of commercial biofuels production was designed in line with the policy objectives. 200 samples were randomly distributed to people with good biofuels education across the country, within 90 days. The recovered questionnaires (PQR = 92.50 %) were treated statistically. Additional respondents’ comments were also captured and analysed. 97.30 % of the respondents expressed optimism in terms of positive impacts such as generation of revenue to the government, investments, jobs creation, energy access to rural areas and environmental sustainability. However, the remaining respondents with percentage cumulative response (PCR) of 2.7 % showed that negative consequences such as food price hike, soil degradation and diversion of food land would be the net result due to high level of corruption, poor technology and lack of transportation network. To achieve the policy objectives, appropriate planning is required. Research covering the views of all stake holders and lessons from prior countries like Brazil and India would be very important. Emphasis should be given to pre-exploited agricultural land and non-food crops that are adaptive to current and foreseeable climatic conditions in Nigeria.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".