Economic-ecological evaluation of temporary biodiversity offsets in Alberta's boreal forest
Bibliographic record
Abstract
SUMMARY To conserve biodiversity on forest landscapes, it is necessary to understand how incentives in an offset market affect the dynamics of habitat loss and restoration. In this study, a model of firm behaviour in a temporary biodiversity offset market is developed to understand the impact of offset rules on the dynamics of land use and offset policy costs and benefits for Alberta's boreal forest. Policy treatments include eligibility rules for restoration versus avoided loss; time lags for crediting restoration benefits; and geographic trading restrictions. The analysis highlights the assumptions and trade-offs embedded in offset principles such as additionality. Restoration-based policies, which require biodiversity benefits to be established prior to development, are over 200 times more costly than policies that include avoided loss. Geographic trading restrictions result in a significant redistribution of policy costs and ecological risks between regions, with little impact on aggregate policy costs and benefits. Including avoided loss results in a decline in biodiversity intactness by 2% to 2.2% compared to a decline of 3.6% under a no-offset policy. Increasing time lags for crediting restoration to match ecological recovery trajectories reduces restoration effort when policies include both restoration and avoided loss.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".