Penguatan local value chain: analisis pembiayaan hijau terhadap comparative trade CPO di Malaysia
Bibliographic record
Abstract
This study examines the comparative dysfunction of palm oil in local CPO commodities in Malaysia through green economic structures and strengthening local value chains. The green economy structure variables discussed in this study, namely green financing and local value chain variables in CPO exports, are measured by CPO products' price and CPO production's value. In addition to these variables, household consumption expenditure is the control variable used as a variable affecting the level of CPO exports. The research data uses data from the first quarter of 2013 to the fourth quarter of 2022. This research methodology describes the Autoregressive Distributed Lag (ARDL) model to examine the long-term effect between variables and the Error Correction Model (ECM) to see how quickly the economy returns to a balanced condition when there are short-term shocks. The study results show that the long-term correlation between the variables of green financing, the price of CPO products, and the value of CPO production significantly affects the level of CPO exports. However, the household consumption expenditure variable is insignificant to the level of CPO exports in the long run. Thus, the short-term correlation shows that green financing variables, CPO product prices, CPO production values, and household consumption expenditures significantly affect CPO export levels.
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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 | 0.004 | 0.010 |
| 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) | 0.008 | 0.002 |
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".