Sustainability assessment of palm oil-based refinery systems for food, fuel, and chemicals
Bibliographic record
Abstract
Palm-based biorefinery system has gained attention worldwide because of potentially high economic returns. However, environmental impacts also increase with the additional production. Therefore, this study aims to assess the sustainability of (1) current palm-based biorefinery system in Thailand, including cooking oil and biodiesel, and (2) palm-based biorefinery system with value-added products, i.e., succinic acid, lactic acid, bio-hydrogenated diesel (BHD), and epichlorohydrin (ECH) that represent biomaterial, biofuel, and biochemical products, respectively. Accordingly, seven palm-based biorefinery scenarios were designed, and their sustainability was assessed through life cycle assessment (LCA), net energy balance (NEB) and net energy ratio (NER), employment generation, and eco-efficiency. The results revealed that value-added production increased global warming impacts by around 3 – 79% compared with the current system. Although environmental impacts increased due to the additional processes related to the production of the value-added products, total product values also increased, especially for succinic acid, generally leading to higher eco-efficiency values. The current palm-based biorefinery system with succinic acid production had the highest eco-efficiency among all the scenarios considered. The BHD production scenario had the highest NEB and NER because the products were used for energy. Employment generation increased for all the scenarios between 2 – 86% compared with the current system.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".