DEMAND ANALYSIS OF INDONESIAN PULPWOOD USING TRANSCENDENTAL LOGARITHMIC MODEL: A STUDY OF THE WORLD AND SELECTED ASIAN MARKETS
Bibliographic record
Abstract
Indonesia’s pulpwood export has shown an increasing trend since 1990s. Along with Brazil, Canada, USA and Chile, Indonesia became one of the top five pulpwood exporter countries in the world. Indonesia’s pulpwood was traded mainly to some Asian countries. This paper examines Indonesian pulpwood export demand during the period 1994-2014 using a Transcendental Logarithmic (TL) model with Seemingly Unrelated Regression (SUR) estimation. Export data from the five top exporter countries in four different markets (China, Korea, Japan and the world) were analysed. The important findings are as follow: firstly, logarithmic income and second order logarithmic income significantly influence the Chinese and Korean markets. Secondly, in general, Indonesia’s own-prices are elastic and have negative signs (-2.308, -1.06 and -2.04 in the Korean, Japanese and the world markets, respectively). Thirdly, due to its positive sign of crossprice elasticity and also positive signs of income elasticity (1.002, 1.722 and 0.625 in the Chinese, Korean and the world markets, respectively), Indonesian pulpwood could be categorized as a substitute and normal goods. Lastly, regarding to negative and elastic Indonesia’s pulpwood own-prices, one possible policy that could be applied by the Government of Indonesia (GoI) is giving a subsidy to reduce pulpwood price by 10%. Subsidy could be implemented by reducing tax and retribution such as property tax (Pajak Bumi dan Bangunan) and local retribution (Retribusi Daerah).
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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) | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".