Bibliographic record
Abstract
The purpose of this study is to analyze the pollution-housing price relationship within a Canadian context, with a special focus on the effects of particular matter 2.5 (PM2.5) in terms of air pollution.The objective is important because environmental quality acts as a relatively less tangible characteristic to housing compared with other physical characteristics (such as number of bedrooms), but it has its own implicit price.Once we make explicit the implicit cost of air contaminant, it will guide public policy decisions on the measures that should be taken to reduce air pollution.This study examines the subject between 1997 and 2013 across Canada's benchmark cities.In order to provide an accurate analysis, two types of housing price index data are used: CANSIM New Housing Price Index (NHPI) and Teranet-National Bank Housing Price Index (THPI).PM2.5 is employed as the proxy of air pollution and the data are collected from Environment Canada.First difference, lagged values, fixed effect and random effect models are the methods being used to produce an accurate and robust analysis.As a result, as this study improves the specifications with better HPI (which is THPI), the negative association between housing prices and air pollution surfaces.The results from the specifications that applied firstdifference, logarithmic function, year dummies and fixed effects or random effects methods, suggest that air pollution has a negative effect on housing prices with a two-year lag.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".