In-Lieu Fee Credit Allocations on Public Lands in the United States: Ecosystem Prioritization and Development-Driven Impacts
Bibliographic record
Abstract
In-Lieu Fee programs are an important mechanism for compensatory mitigation in the United States and received wide-spread standardization after the regulatory mitigation rule change of 2008. On public lands, they are especially important for pooling funds from numerous small-scale impacts that might otherwise go unmitigated. This study examines the use cases of fee program credits on public lands since 2008. Using data from the Regulatory In-Lieu Fee and Bank Information Tracking System, I analyzed eleven active In-Lieu Fee programs approved post-2008 across 78 service areas, encompassing 1043 credit transactions. Transactions were categorized by credit amount, proportion, target ecosystems, and impact designations. The analysis highlights the influence of residential and commercial development, alongside resource extraction, as major contributors to fee program transactions, underscoring the program’s role in mitigating various development pressures. Residential, commercial, and government projects frequently co-occur within service areas, which can support policy planning to anticipate potential cumulative impacts and expected future impacts and credit demands. Furthermore, my analysis shows that impacts from resource extraction require proportionally larger offsets than those from residential or recreational activities. The findings suggest that programs on public lands can fill a niche distinct from mitigation banks, as they address a multitude of impacts while further allowing for the pooling of resources and funds from small-scale impacts, while the use of advance credits remains contentious for achieving no net 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".