Sustainability of the Palm Oil Industry: An Empirical Study of the Development of Sustainable Oil Palm in Bengkalis Regency, Indonesia
Bibliographic record
Abstract
Bengkalis' economy has continued to contract since 2012. This is due to its very high dependence on oil and gas. It is necessary to develop other sectors that have high potential, such as oil palm plantations and the CPO processing industry which are in line with the Sustainable Development Goals (SDGs). Taking into account the complexities and problems that are quite complicated in the processing of the CPO industry, it is necessary to conduct research to determine the sustainability status of the CPO industry, identify factors that have high sensitivity/influence, and formulate the best scenario for its development. The analytical method used in this study is the Multi Dimensional Scaling (MDS) technique through the Rapid Appraisal Technique (RAP) approach with a multidimensional approach (economic, social, environmental, technological, and institutional). The research was conducted in Bengkalis Regency with respondents being managers of CPO processing industry companies with instruments in the form of questionnaires and structured through in-depth interviews. The data used is primary data. The results showed that the CPO processing industry was classified as sustainable with an index of 59.76. Dimensions included in the less sustainable category are environmental and technological dimensions. This shows that the CPO processing industry has not paid attention to environmental conditions and has not maximized technology to achieve its goals. With an optimistic scenario, the results show an increase in status to be very sustainable with an index of 78.78.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".