Formulating a Long Term Strategy for Sustainable Palm Oil Biodiesel Development in Indonesia
Bibliographic record
Abstract
Indonesia, the largest producer of palm oil, has been developed palm oil biodiesel as renewable energy in the last decade. Indonesia biodiesel development policies aim to increase domestic value added of palm oil product and reduce the reliance on fossil fuel. Indonesia has embarked on a comprehensive palm oil biodiesel program since 2006 and targeted the 20% biodiesel blend (B20) in 2016. This article explores the strategy formulation by accommodate the stakeholder perspective in the problems and the solutions. This research analyzes the information from in depth interview with biodiesel stakeholders (government, industry and researcher) in Indonesia by combine Strengths, Weaknesses, Opportunities, and Threats (SWOT) analysis with a Multi Actor Analysis approach. The results show the problems of biodiesel development are mainly on the high production cost due to high price of raw material, production technology and distribution infrastructure. The government policy, technology development and raw material supply are the driving forces of the biodiesel development in Indonesia. In the long term strategy, government of Indonesia should secure the biodiesel raw material, develop an environmental friendly technology in biodiesel processing, and accommodate any improvement idea from other stakeholders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".