Economic impacts of climate change on forests: a PICUS–LANDIS–CGE modeling approach
Bibliographic record
Abstract
Climate change is expected to alter both forest stand- and landscape-level dynamics through a change in environmental characteristics. While numerous studies have employed models to assess the ecological and/or economic impacts of such changes on forests throughout the world, there is need to further refine such analyses. In this paper, we contribute to this literature by coupling an ecological (PICUS–LANDIS II) modeling framework with an economic (computable general equilibrium) model to better account for the economic impact associated with climate-induced impacts on forest stand- and landscape-level structure and composition dynamics. Applying this framework to a case-study region of New Brunswick, Canada, we estimate that climate change will reduce softwood supply by 16%–73% and impact hardwood supply in the range of −2% to +4% by 2150. The change in wood supply is estimated to reduce the value of the softwood and hardwood forestry and logging sector output by up to 51% and 17%, respectively, by 2150. These sector-level impacts may lead to a 0.08%–0.88% reduction in annual gross domestic product by 2150. The methodological advances established in this study can be used to better inform future forest management and economic plans that aim to lessen both the ecological and economic impact of climate change.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 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".