A trade flow analysis of the global softwood log market: implications of Russian log export tax reduction and New Zealand log production restriction
Bibliographic record
Abstract
This study employs a recursive dynamic spatial partial equilibrium model to investigate the trade flow trends in the global softwood log market. A baseline forecast from 2012 to 2021 is first projected and then compared with three alternative scenarios: (1) Russia reduces its <it>ad valorem</it> softwood log export tax to 8 per cent to comply with its World Trade Organization accession agreements; (2) New Zealand experiences no expansion at all of its plantation forest log production due to social and environmental considerations (i.e. increased M&amacr;ori ownership of forest land and the implementation of emissions trading schemes); and (3) a combination of the proposed policies in Russia and New Zealand mentioned above. The results of the baseline projection demonstrate that softwood log prices will increase in every region globally and that China will continue to be the world's largest softwood log importer. However, softwood log exports from Russia, the US and Canada are expected to drop significantly as a result of current Russian export restrictions, the recovery of the US housing market and mountain pine beetle infestations in western Canada. A comparison of the simulated scenarios with the baseline projection reveals that reducing the Russian softwood log export tax will have a greater impact on softwood log prices and total world trade than restricting log production in New Zealand due to the comparatively large log production capacity in Russia. In any scenario, significant trade flow changes (i.e. trade-offs) in response to the proposed policy changes are observed in China and the major export regions. The results of this study offer insights for forest managers and policy makers to examine the global impacts of potential changes in trade policies and supply constraints in these two important softwood log supply regions in addition to highlighting China's role in the world softwood log market.
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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 | 0.001 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".