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Record W3133915350

Economic Value of Water Quality Improvements in Ontario

2021· dissertation· en· W3133915350 on OpenAlexaboutno aff
Rashadur Rahman

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2021
Typedissertation
Languageen
FieldEnvironmental Science
TopicEnvironmental and Social Impact Assessments
Canadian institutionsnot available
Fundersnot available
KeywordsValue (mathematics)Water qualityQuality (philosophy)EconomicsEnvironmental scienceMathematicsStatisticsPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

This study estimates the economic value of lake water quality changes in South Eastern region of Ontario using the hedonic price method. The research combines 58,085 house transaction data between 2005-2014 time periods and water quality (WQ) data from 494 unique lakes. I examine the effect of total phosphorus and Secchi depth (SD) on house price using a log-linear regression. Considering SD as the variable of interest, results indicate that house buyers are willing to pay a 1.9% higher price for a one-meter improvement in SD if the house is located close to the lake WQ station. The marginal willingness to pay (MWTP) for SD reaches the peak, $7,627 per meter, for houses located within 500 meters to 750 meters distance to the lake stations. However, the price premium starts to decrease as the lake distance increases; house buyers are willing to pay 4.4% less for a marginal increase in SD if the house is located within 2,000 to 3,000 meters of the WQ stations. I assess the robustness of the results across the alternative data specification and estimate the highest level of MWTP for water quality ($6, 142) considering houses within 3 kilometres to the lake stations. The estimated local benefits can inform the design of WQ improvement programs.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.232
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0190.000

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 head0.007
GPT teacher head0.190
Teacher spread0.184 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

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