An impact analysis of climate change on the forestry industry in Quebec
Bibliographic record
Abstract
Quebec’s forests represent 20% of Canadian forests and 2% of the world forests. Over the entire planet, forests play a major role in habitat preservation and in supplying goods and services to the population. However, climate change will have an impact on the forest through inter alia increased droughts, forest fires, warmer weather, and infestations. In this paper, we analyze the economic impact of climate change on the forest industry in Quebec over a 40-year period using a recursive dynamic computable general equilibrium model. We find that the climate change effects will be relatively weak on most macroeconomic variables as agents adjust their behavior over time and factors are reallocated across sectors. We find that climate change could generate losses in gross domestic product of up to Can$300 million (0.12% of gross domestic product) at the end of a 40-year period for Quebec’s economy. However, we find relatively more important effects within the sectors of the forest industry, with losses ranging from 3% to 7.5%.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".