Economic and ecological trade-off analysis of forest ecosystems: options for boreal forests
Bibliographic record
Abstract
Intensive forest management practices for production forestry can potentially impact the sustainability of ecological functions and associated forest ecosystem services. Understanding the trade-offs between economic gains and ecological losses is critical for the sustainable management of forest resources. However, economic and ecological trade-offs are typically uncertain, vary at temporal and spatial scales, and are difficult to measure. Moreover, the methods used to quantify economic and ecological trade-offs might have conflicting priorities. We reviewed the most current published literature related to trade-off analysis between economic gains and sustainability of forest ecosystem functions and associated services, and we found that most economic and ecological trade-offs studies were conducted in tropical and temperate forests, with few having their focus on boreal forests. Analytical methods of these published studies included monetary valuation, biophysical models, optimization programming, production possibility frontier, and multi-objective optimization. This review has identified the knowledge gaps in the understanding and measurement of the economic and ecological trade-offs for the sustainable management of boreal forests. While it remains uncertain how economic activities might best maintain and support multiple ecological functions and associated services in the boreal forests, which are susceptible to climate change and disturbances, we propose the use of optimization methods employing multiple objectives. For any tool to provide sustainable and optimal forest management solutions, we propose that appropriate and robust data must be collected and analyzed.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".