ASSESSING THE VALUE OF ENVIRONMENTAL ATTRIBUTES AFFECTING HOUSEHOLDS’ HOUSING CHOICES USING A CHOICE EXPERIMENT METHOD IN NAVAB NEIGHBOURHOOD
Bibliographic record
Abstract
One technique that has been recently used to assess the value of environmental attributes is choice experiment method (CEM). CEM permits value estimates for different goods (services) sharing a common set of attributes to be pieced together using the results of a single multinomial (conditional) logic model. The CEM approach to environmental value assessment is illustrated in the context of housing choices by Naval residents in Tehran. A standard CEM questionnaire has been developed and completed in a sample of 200 residents in the Naval project neighborhood in 2008. Several log it regression models were developed in order to estimate the impacts of selected environmental attributes on households’ choices of dwellings. We have calculated the willingness to pay for environmental pollution, accessibility, security, sociability, neighborhood facilities and home facilities using house prices as a monetary value. The results show that except for the home facilities, all other environmental attributes have significant expected impacts on households’ choices. In the context of this neighborhood the results show that households would be willing to pay more for purchasing a house with less environmental pollution (noise and air pollution, more sociability, security, neighborhood facilities, and accessibility respectively. CEM is found to provide flexible and cost-effective results for estimating use and passive use values of environmental attributes in urban environment, particularly when several alternative proposals and attributes need to be considered.
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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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".