Forecasting and analysing the gap between Thailand’s wood pellet supply and global demand
Bibliographic record
Abstract
With the global concern for climate change on the rise, the use of biomass wood pellets as a sustainable alternative to fossil fuels is gaining popularity in numerous countries, such as the European Union (EU), the United States, Canada, Japan, and South Korea.In response, the Thai government has initiated a project to promote the cultivation of fast-growingtrees, such as Acacia, which can serve as feedstock for biomass power plants both domestically and internationally. The objective of this study is to evaluate the demand-supply gap for wood pellets in Thailand. To predict future demand for wood pellets, historical import data from January 2017 to December 2021 were examined and analysed, with a variety of time-series forecasting techniques, including the Simple Moving Average (SMA), the Holt’s Two-Parameter method, and the ARIMA method, being employed. Theappropriate techniques were subsequently chosen based on the Mean Absolute Deviation (MAD), Mean Square Error (MSE), and Mean Absolute Percentage Error (MAPE). The results of the analysis can be utilised to identify market gaps and growth opportunities, and to develop a comprehensive supply chain strategy for wood pellets, ranging from upstream tree plantation to downstream demand.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".