Demand and Supply of Crude Palm Oil for Biodiesel Production towards Food and Energy Security
Bibliographic record
Abstract
Energy and food security are two key national policies and agenda of Thailand. The Department of Alternative Energy Development and Efficiency (DEDE), Ministry of Energy has modified a 15-year (2008-2021) Alternative Energy Development Plan (AEDP). According to the modified AEDP, the share of renewable and alternative energy consumption should increase to 25% by 2021. The latest policy aims to promote the biodiesel consumption and production by 5.97 million liters/day within 2021, up from its previous plan of 4.50 million liters/day while nowadays production capacity is 1.62 million liters/day. The current and anticipated production of biodiesel require raw materials, notably crude palm oil (CPO) from oil palm plantations. This paper presents a feasibility study, with respect to the AEDP plan, for biodiesel production by using domestic fresh fruit oil palm as a raw material. Demand and supply are analyzed based on statistical modeling and forecasting techniques, as well as GIS spatial analysis. The results show that the domestic CPO consumption for cooking palm oil will be 1.02 million tons/year, while biodiesel production will be 1.67 million tons/year in 2021. This corresponds to an oil palm plantation area of 11,152 km2 in 2021. Based on GIS spatial analysis along with land-use database and other relevant databases, it was found that the suitable area for palm oil plantation in Thailand is 14,639 km2, which is sufficient for domestic demand and consumption. The suitable area consists of 9,664 km2 for quite suitable and 4,975 km2 for medium suitable. However, the government should provide the guidelines, along with pro-active and supportive policies for worst case scenarios, e.g. drought and flood leading to the insufficient raw material, in order to reduce the negative impacts of biodiesel production for domestic demand and consumption.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".