Assessing Conservation and Mitigation Banking Practices and Associated Gains and Losses in the United States
Bibliographic record
Abstract
Conservation and mitigation banks allow their proponents to buy credits to offset the negative residual impacts of their development projects with the goal of no net loss (NNL) in the ecosystem function and habitat area. However, little is known about the extent to which these bank transactions achieve NNL. We synthesized and reviewed 12,756 transactions in the United States which were related to meeting area and ecological equivalence (n = 4331) between the approved negative impact and offset. While most of these transactions provided an offset that was equal to or greater than the impacted area, approximately one quarter of the transactions, especially those targeting wetlands, did not meet ecological equivalence between the impact and offset. This missing ecological equivalence was often due to the significantly increasing use of preservation, enhancement, and rehabilitation over creating new ecosystems through establishment and re-establishment. Stream transactions seldom added new ecosystem area through creation but mainly used rehabilitation in order to add offset benefits, in many cases leading to a net loss of area. Our results suggest that best practice guidance on habitat creation as well as the incentivization of habitat creation must increase in the future to avoid net loss through bank transactions and to meet the ever-accelerating global changes in land use and the increased pressure 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".