Economic impacts of climate change in the forest sector: a comparison of single-region and multiregional CGE modeling frameworks
Bibliographic record
Abstract
Climate change impacts on forests are largely expected to intensify over the next few decades. Computable general equilibrium (CGE) modeling is increasingly becoming a popular tool for assessing these impacts. Previous analyses in this area have been based on either single-region or multiregional CGE model specifications, each with their own advantages and disadvantages. To date, however, there has been no systematic comparison of the potential differences in economic impact estimates between the two CGE model specifications. To examine the extent of these potential differences, we conducted a comparative economic impact analysis of climate change in the forest sector across Canadian provinces, the United States, and the rest of the world using dynamic, single-region and multiregional CGE models over the 2006–2051 period. Results revealed that, within each region, different model specifications produced unique economic impact estimates, differing by as much as 18% under each climate change scenario considered. Overall, a majority of Canadian regions recorded smaller (in absolute value terms) and more positive economic impacts using single-region models compared with the multiregional model. Differences in international trade specifications between models, together with unique climate change impact considerations across regions, played key roles in the findings. While few general conclusions emerge from this analysis, it is clear that CGE model specifications can have a significant effect on regional economic impact estimates of climate change in the forest sector. Thus, caution is advised when using the estimates of any one CGE model for policy purposes.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| 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".