Assessing the impacts of forest taxation programs on optimal management of Douglas-fir under adaptive harvest strategies in Western Oregon
Bibliographic record
Abstract
This paper investigates the impacts of two taxation programs, the Forestland (FL) program, based on a site value tax, and the Small Tract Forestland (STF) program, based on a combination of a site value tax and a severance tax, on the optimal management of Douglas-fir in Western Oregon amidst timber price uncertainty. The study reveals that the optimal reservation prices are lower under the STF program, making it more advantageous for forest landowners in terms of tax liabilities. Specifically, the STF program leads to significantly lower tax payments ($185.8–$277.4 per acre) and tax burdens (6.0%–5.9%) compared to those of the FL program ($709.2–$1092.5 per acre; 22.9%–23.2%). When factoring in the harvest age, the STF program results in an increased harvest age, whereas the FL program remains fiscally neutral. The FL program exhibits a slightly progressive taxation system, while the SFT program displays a slightly regressive taxation system. Although our findings suggest that the STF program is a better option for landowners, a more detailed analysis regarding the impacts of these taxation programs on the overall welfare of the state’s economy is required.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".