Market Power of China’s Chemical Wood Pulp Imports Based on the Pricing-to-Market Model
Bibliographic record
Abstract
Abstract China is the world’s largest importer of chemical wood pulp, which exerts influence in the international market. This paper assesses China’s market power in chemical wood pulp imports by using a fixed-effects variable coefficient pricing-to-market model based on panel data from January 2001 to December 2023. Regression results indicate that China’s chemical wood pulp import sources can be categorized into three groups. China holds superlative market power in imports from Thailand, Japan, and Russia; holds strong market power in imports from Indonesia and New Zealand; and holds no significant market power in imports from Chile, the United States, Germany, Sweden, Finland, Canada, and Brazil. This fact is associated with multiple factors, including the market share of exporting countries, the development level of the wood-processing industry, production costs, product quality, transportation convenience, presence of Chinese production bases in exporting countries, frequency of natural disasters, and the degree of price manipulation. From these analyses, this paper suggests that China should improve its chemical wood pulp import trade conditions and alleviate domestic resource shortages.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".