Clearing the Air: How Fine Particulate Matter Regulations Reshape Farmland Values in U.S. Corn and Soybean Regions
Bibliographic record
Abstract
ABSTRACT We investigate the impact of air quality regulations targeting fine particulate matter (PM 2.5 ) on farmland values in corn and soybean producing counties in the United States over the period 1997–2022. Using self‐reported farmland value data from the Agricultural Census and county‐level pollution classifications provided by the Environmental Protection Agency, we employ a difference‐in‐differences event‐study design—incorporating inverse probability weighting and doubly robust estimators—to estimate the causal effect of regulatory interventions. Our primary analysis contrasts ‘non‐attainment’ counties, which failed to meet the National Ambient Air Quality Standards for PM 2.5 , with those that consistently maintained compliance. We further assess heterogeneous treatment effects by extending our analysis with a triple‐difference specification comparing counties with high versus low fertiliser use. Additionally, we employ the recentered influence function to conduct an unconditional quantile analysis across the entire distribution of farmland values. Our estimates indicate an 8.80%–8.94% decline in farmland values in ‘non‐attainment’ counties in response to the enforcement of PM 2.5 standards, suggesting that the economic costs of the prescribed standards were capitalised into farmland values, particularly in regions with higher fertiliser use. However, this impact was not uniform, with more pronounced effects observed among counties at the lower end of the farmland value distribution.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".