Impact of Various Palm-Based Biodiesel Blend Mandates on Malaysian Crude Palm Oil Stock and Price: A System Dynamics Approach
Bibliographic record
Abstract
With an increasing concern over world population, energy security, volatile fuel prices and rising in greenhouse gas emissions, many countries are considering to a more friendly alternative fossil fuels. United States and also the European Union has already introduce of legislation mandating the use of biofuels in the energy mix. This action has stimulated the demand for vegetable oils. Malaysia is currently the largest palm oil exporter and the second largest producer after Indonesia has launched her palm-based biodiesel blending mandate of 5 percent (B5) in year 2011. However the mandate has not been implemented nationwide. Thus, this study, therefore seeks to contribute to our understanding of the impacts of various blend mandates specifically B5, blending mandate of 7 percent (B7) and blending mandate of 10 percent (B10) on the Malaysian crude palm oil market stock and price. A system dynamics model was developed for the Malaysian palm oil industry which provides a framework to understand the feedback structure and how changes in various blend mandates impact the behaviour of the crude palm oil stock and price. This research suggests that increasing the blending rate is not a favourable policy, although it managed to decrease the crude palm oil stock to the targeted level but it does not contribute to palm-based biodiesel industry production due to loss in terms of profitability because of high crude palm oil prices.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".