Economic Analysis of the Forest Promotion, Forest-Saving Program, Installed in the Southern Half of Rio Grande do Sul State, Brazil
Bibliographic record
Abstract
This study aimed to analyze economically the forest promotion, forest-saving program installed in the southern half of Rio do Grande do Sul State, Brazil, as an income alternative and potential supplier of raw material in the forest production segment. Cost data were calculated for the total of 269 projects per hectare and divided into Inputs and Services. The revenues were derived from the sale of standing timber at the end of the forest production cycle, not including harvesting costs. For economic analysis, criteria from Net Present Value (NPV), Benefit Cost Ratio (B/C), Internal Rate of Return (IRR), and Equivalent Annual Value (EAV) were used. The interest rate used was 7.0% per year according to the promotion program. The project presents at seven years a NPV of $542.90 and an IRR of 16.0%, showing to be feasible and attractive. The costs and revenues from the year seven planting were analyzed and with addenda at years 8, 9, and 10, demonstrated that greater project profitability gains are achieved between years 8 and 9 with an increase of $463.18 in relation to year 8. This represents a profitability of 49.0% which had an increase of $229.61 when compared to year 7. The sensitivity analysis demonstrated the inverse relationship trend that exists between the NPV and the interest rate. The project’s return capacity from the seventh year is precisely referenced by the freezing of the debt, which did not accrue an interest rate adjustment, as well as the price per cubic meter of timber, which remains readjusting as zero bases.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".