Heterogenous impact of China's place‐based environmental regulations on its hog industry: A synthetic difference‐in‐differences approach
Bibliographic record
Abstract
Abstract Agricultural water pollution from the livestock industry is a growing concern in China and globally. Since 2014, China classified eight urban provinces in the southeast as a development control zone (DCZ), which prohibits new hog facility construction and encourages hog farms to relocate to other regions. Leveraging synthetic difference‐in‐differences (SDID), we systematically analyze the impacts of such place‐based regulations on the hog industry and water pollution, especially revealing heterogenous responses. Our results show that, on average, the regulations led to heterogenous reductions in hog inventories both within and across DCZ provinces, mainly resulting from the closures of existing hog farms. The effects range from a 2% increase to 40% hog inventory reduction, equivalent to a loss of over U.S. $5.06 billion in the DCZ hog sectoral revenue. We explore three channels to explain the heterogeneity: counties upstream of big cities, counties designated as main hog counties, and counties with drinking water sources serve as origins of the heterogenous effects. However, we find no significant water quality improvement.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".