Valuing a Logging Externality: Loss of the Water Purification Service of Temperate Coastal Rainforests
Bibliographic record
Abstract
In many biodiversity rich watersheds, there is a lack of understanding concerning the trade-offs between timber harvesting and maintaining the watershed’s other ecosystem services, where losses of these services can occur as an externality from timber harvesting. As a result, the potential benefit from an appropriate mix of activities in multiple-use watersheds frequently remains unrealized. Our study provides insight into such trade-offs by estimating the value of a loss in a forest’s water purification/filtration service due to sedimentation caused by logging (the externality). More specifically, we develop a model to quantify the economic impact of increased sedimentation from forest roads on the quality of raw water withdrawn by a municipal water utility. Our approach is novel in several ways. First, we recognize the complex response of the water treatment plant to elevated sedimentation (turbidity) by considering a stochastic environmental influence on water system performance; to accommodate this complexity, we estimate the number of times turbidity exceeds an acceptable threshold by using a count data estimation procedure. Second, we generate alternative time series for turbidity that vary according to assumptions about forest management (logging versus no logging), traffic volume (road use intensity) and aggregate road length. We find that reductions in the economic value of the water purification/filtration service is more sensitive to traffic volume than other considerations but only when the road use is High, as the welfare effect in other cases is modest. Our analysis will be helpful to forest planners who must consider the trade-offs in forest management when timber harvesting can have harmful impacts on important ecosystem services, such as water purification/filtration.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".