Productivity Spillovers From Pollution Reduction: Reducing Coal Use Increases Crop Yields
Bibliographic record
Abstract
Air pollution reduces crop yields by slowing down photosynthesis. We estimate the increase in US corn and soybean yields attributed to the recent dramatic reductions in emissions of nitrogen oxides (NOx) from electric power plants. In response to the observed changes in power plant NOx emissions over the eight‐year period from 2003–05 to 2011–13, we estimate that average corn yields improved by 2.46% and soybean yields by 1.62%. These improvements imply an increase in total surplus of $1.60 billion annually across the two crops. The estimated yield improvements vary substantially across states depending on the change in NOx emissions. For corn, they range from 0.32% to 6.87% and for soybeans, they range from 0.21% to 4.30%. The demand for the two crops is quite inelastic, which means that prices decrease by more than production increases in response to this positive productivity shock and the implied rightward shift of the crop supply curve. Due to the low elasticities of supply and demand for U.S. corn and soybeans, we conclude from a welfare analysis that these changes made consumers better off and farmers worse off.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".