Forest succession, management and the economy under a changing climate: coupling economic and forest management models to assess impacts and adaptation options
Bibliographic record
Abstract
Abstract Climate change is expected to have significant impacts on forests by affecting the successional dynamics of tree species and the performance of plantations, among others. Research is needed to better understand how these factors will affect forests and economies in different regions, and how we can best adapt. To shed some light on these issues, we couple an economic (Computable General Equilibrium) model with a forest management (Woodstock) model to analyze the potential climate change impacts and adaptation options on timber supply and the economy over the 2015-95 period in a case-study province of New Brunswick, Canada. We estimate that climate change may have relatively large negative impacts on softwood timber supply (at 26% by 2095), softwood forestry & logging sector output quantity (at 12% by 2095), and softwood-dependent forestry manufacturing sector output (ranging from 6% to 27% by 2095). Negative impacts on GDP may be relatively smaller (at up to a 0.33% reduction by 2095). Adapting to these climate-related changes by planting drought-resistant softwood seedlings or hardwood seedlings in place of failed softwood plantations can reduce these negative impacts. While the former adaptation option is supported using cost-benefit analysis, the latter is not – due to the large incremental costs of growing, planting, and tending hardwood seedlings. Methods developed in this study can be applied in other regions to help guide decision-making around forest management in the face of a changing climate.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".