Evaluating willingness to pay for the temporal distribution of different air quality improvements: Is China's clean air target adequate to ensure welfare maximization?
Bibliographic record
Abstract
Abstract Stated preference analyses seeking to determine the public's value for air quality improvements often estimate willingness to pay (WTP) for days at a specified minimum quality threshold (e.g., days with clean air), but do not consider the temporal distribution of pollution levels below this threshold. This paper develops a choice experiment designed to evaluate WTP for a more complete distribution of air quality improvements, including the number of days per year at multiple air quality levels. The model is applied to a case study of air quality improvement in the core districts of Xi'an City, China. Results from a linearly constrained mixed logit model demonstrate that average household WTP for improving a lightly polluted, moderately polluted, heavily polluted, or severely polluted day to a clean air day is 7.42, 8.90, 13.06, and 24.28 RMB per year, respectively. These results show that WTP depends not only on the total number of clean air days, but on the total distribution of pollution levels across all days of the year. Results are directly relevant to the development of clean air policies in China, for which benefit estimates are currently unavailable.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".