Gaming in Air Pollution Data? Lessons from China
Bibliographic record
Abstract
Abstract Protecting the environment during economic growth is a challenge facing every country. This paper focuses on two regulatory measures that China has adopted to incentivize air quality improvement: publishing a daily air pollution index (API) for major cities since 2000 and linking the API to performance evaluations of local governments. In particular, China defines a day with an API at or below 100 as a blue sky day. Starting in 2003, a city with at least 80% blue sky days in a calendar year (among other criteria) qualified for the “national environmental protection model city” award. This cutoff was increased to 85% in 2007.Using officially reported API data from 37 large cities during 2000-2009, we find a significant discontinuity at the threshold of 100 and this discontinuity is of a greater magnitude after 2003. Moreover, we find that the model cities were less likely to report API right above 100 when they were close to the targeted blue sky days in the fourth quarter of the year when or before they won the model city award. That being said, we also find significant correlation of API with two alternative measures of air pollution – namely visibility as reported by the China Meteorological Administration (CMA) and Aerosol Optical Depth (AOD), corrected for meteorological conditions, from NASA satellites. The discontinuity around 100 suggests that count of blue sky days could have been subject to data manipulation; nevertheless, API does contain useful information about air pollution.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".