Cutting through market trends: the impact of macroeconomy, COVID-19 pandemic, and climate-related disasters costs on wood product prices in North America
Bibliographic record
Abstract
The market of wood products is the primary system used to assess the value of timber. Understanding its dynamics could help in forecasting future trends and guiding decision-makers on what and when to harvest, enabling better market anticipation. Previous research has mainly studied wood products prices through general indices, which does not allow differentiation of wood products based on their dimensions, key information that can be linked with the structure of forest stands. We examined how macroeconomic variables, the coronavirus disease of 2019 (COVID-19) pandemic, and climate-related disasters costs affected the prices of eight wood products (seven softwood lumber products and one oriented strand board) in North America. We fitted first-order autoregressive models with a variance function, on time series of prices recorded between 1990 and 2023. We found that, contrary to common assumptions, price changes cannot be attributed to the same variables, nor do these variables impact prices with the same magnitude across different products. Moreover, disruptive events, such as the COVID-19 pandemic, mainly contributed to uncertainty without entirely determining prices. Such findings can help link the value of standing trees, based on the products they can generate, and therefore inform on optimal silviculture and harvesting strategies.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".