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Record W2955407230 · doi:10.1139/cjfr-2018-0309

Climate change, increasing forest fire incidence, and the value of visibility: evidence from British Columbia, Canada

2019· article· en· W2955407230 on OpenAlexaffvenueabout
Wolfgang Haider, Duncan Knowler, Ryan Trenholm, J. N. Moore, Phil Bradshaw, Ken Lertzman

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsBC Hydro (Canada)Capital Regional DistrictSimon Fraser University
Fundersnot available
KeywordsVisibilityHazeClimate changeHomogeneousGeographyDamagesHuman healthValue (mathematics)Environmental scienceEnvironmental healthMeteorologyEcologyMedicineStatisticsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.093
GPT teacher head0.253
Teacher spread0.159 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations18
Published2019
Admission routes3
Has abstractyes

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