The value of cleaner waterways: Evidence from the Black-and-Odorous water program
Bibliographic record
Abstract
This study investigates the economic impacts of cleaning up heavily polluted waterways in urban neighborhoods. We leverage the Black-and-Odorous water program, a major urban environmental campaign in China, as a natural experiment to identify the causal impact of cleaner waterways on local housing prices, housing supply, and business growth. Implemented in 2016, the program remediated heavily polluted waterways in China’s 36 most developed cities. Using a difference-in-differences estimator, we find that the program mainly benefits properties within 1 mile of cleaned-up waterways: These properties saw a 2.3 % appreciation in market value after the program. Beyond the impacts on the housing market, we identify two novel mechanisms associated with community revitalization following pollution management and examine their implications for housing prices. First, new real estate developments near treated waterways are more likely to offer high-end units after the program. Second, service businesses flourish in neighborhoods near cleaned waterways, indicating a commercial rejuvenation of these areas.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".