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.
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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".