Biophysical and Economic Analysis of Black Spruce Regeneration in Eastern Canada Using Global Climate Model Productivity Outputs
Bibliographic record
Abstract
We explore the biophysical potential and economic attractiveness of black spruce (Picea mariana) regeneration in eastern Canada under the high greenhouse gas emission scenario (RCP 8.5) of the Intergovernmental Panel on Climate Change (IPCC). The study integrates net primary productivity and net ecosystem productivity estimates from three major global climate models (GCMs), growth and yield equations specific to black spruce, and economic analyses to determine spatially varying investment values of black spruce regeneration—both including and excluding carbon sequestration benefits. Net present value (NPV) was used to represent financial attractiveness. It was assumed that stands would not be harvested at volumes less than 80 m3·ha−1. A baseline case with the stumpage price set to $20 m−3, stand establishment cost $500 ha−1, and the discount rate 4%, was used with wide-ranging sensitivity analyses conducted around these assumptions. These values represent the wide range of choices and outcomes possible with black spruce regeneration investments. The results indicated a latitudinal gradient in economic attractiveness, with higher forest productivity and NPVs in the southern portion of the study area; however, black spruce regeneration was not economically attractive unless regeneration costs were very low (representing something closer to a natural regeneration type scenario) and/or carbon sequestration benefits of at least $5 ton−1 CO2 were realized. In general, the optimal harvest rotation age increased with increasing carbon price by approximately 9 to 18 years. Results of this study highlight the importance of future price and yield expectations and establishment costs in evaluating forest investments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".