Identifying Priority Areas for Forest Landscape Restoration to Protect Ridgelines and Hillsides: A Cost‐Benefit Analysis
Bibliographic record
Abstract
This research presents a case study using cost‐benefit analysis to identify priority areas for forest landscape restoration to protect the ridgelines and hillsides in a single county of the southern Appalachian region, which may be applicable to other communities with similar issues. Private and public benefits per dollar spent are estimated for 15 target restoration sites. The results of this study show the potential for increased benefits to the community from reforestation projects, but those benefits can vary greatly depending on a number of factors, including the area of the target reforestation site, the number of houses surrounding the target site, property values, and the proximity of houses to the site. Much of the private benefit accrues to households living close to the reforestation site while public benefits are mainly generated by indirect use values associated with the cleansing of air and water pollutants. Assuming the explicit cost of reforestation is paid by local governments, the sites generating the most public benefit per dollar could be considered high‐priority reforestation sites based on equity. Alternatively, the sites with the highest private return per dollar could be viewed as high‐priority sites if the payment system allows direct payment for reforestation of private land. The latter payment system could be implemented through a conservation easement program that restricts the development rights of private landowners and permits private donations to support the program. Le présent article porte sur une étude de cas dans laquelle une analyse coûts‐avantages a été réalisée afin de déterminer des zones prioritaires de restauration de paysages forestiers destinées à protéger les lignes de crêtes et les coteaux dans un comté du sud des Appalaches. Cette étude de cas pourrait s’appliquer à d’autres communautés confrontées à des problèmes similaires. Nous avons estimé les avantages publics et privés par dollar dépensé de 15 sites de restauration cibles. Les résultats de notre étude montrent que les communautés pourraient tirer des avantages accrus des projets de reforestation, mais que ces avantages varient grandement en fonction de certains facteurs, tels que la zone où se trouve le site de reforestation cible, le nombre d’habitations entourant le site cible, la valeur des propriétés et la proximité des habitations par rapport au site. Une grande partie des avantages privés découlent du fait que les habitations se trouvent à proximité du site de reforestation, tandis que les avantages publics découlent principalement de la valeur d’usage indirecte liée à l’assainissement de l’air et de l’eau. Dans l’hypothèse où le coût explicite de la reforestation est assumé par les administrations municipales, les sites procurant le plus d’avantages publics par dollar dépensé pourraient être considérés comme des sites de reforestation hautement prioritaires en fonction de la valeur nette (equity). En revanche, les sites procurant le plus d’avantages privés par dollar dépensé pourraient être considérés comme des sites hautement prioritaires si le système de paiement permet le versement de paiements directs pour la reforestation des terres privées. Ce système de paiement pourrait être intégré dans un programme de servitudes de conservation qui restreint les droits d’aménagement des propriétaires fonciers privés et accueille les dons de particuliers en guise d’appui au programme.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".