Climate change, increasing forest fire incidence, and the value of visibility: evidence from British Columbia, Canada
Bibliographic record
Abstract
Climate change may increase the occurrence and severity of forest fires, leading to worsening wildfire seasons. More frequent burn events would have various effects due to increased haze and smoke, including a greater incidence of impacts on human health and reduced or impaired visibility. In areas such as the Lower Fraser Valley of British Columbia, which prides itself on panoramic mountain and city views, individuals may be willing to pay to address deteriorating visibility conditions arising from wildfires or other sources. However, studies consistently show that any attempt to ask individuals how much they are willing to pay to improve local visibility will be confounded with the benefits of improving local health conditions. We used a discrete choice experiment to estimate the value of potential improvements in local visibility in the Lower Fraser Valley, but we included the consideration of health effects and found that these two attributes were indeed linked. As human preferences are rarely homogeneous, we also considered heterogeneity in respondents’ preferences for increases in the number of improved-visibility days versus healthy days. Finally, we applied our results to estimate the value of damages from visibility disruptions related to wildfire smoke from 2002 to 2018 in the Lower Fraser Valley.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".