Decomposing changes in establishment‐level emissions with entry and exit
Bibliographic record
Abstract
Abstract This paper decomposes pollution releases by US manufacturing establishments to show the relative importance of four establishment‐level channels: entry, exit, reallocation between survivors and within‐establishment adjustment of emissions intensity. Using a panel of establishment‐level output and pollution emissions to air and water for US manufacturers, we decompose changes in pollution emissions into the three channels typically presented in the literature: changes in scale (output), composition (industry market share) and industry‐level technique (emissions intensity). We then decompose changes due to industry‐level emissions intensity into four establishment‐level channels for three criteria air pollutants and water pollution. For volatile organic compound emissions, nearly two thirds of the reduction in sector‐level emissions intensity is due to within‐establishment reductions in emissions intensity. The other third is driven by reallocation to cleaner establishments. Though the magnitudes differ, results are broadly similar for particulate matter and sulfur dioxide. On‐site releases of effluents to water exhibit a similar pattern, though the relative importance of reallocation is greater. We also find that within‐establishment reductions in water emissions are associated with increased transfers to off‐site publicly owned treatment facilities. The heterogeneous contributions across channels suggests that the cleanup in the US manufacturing sector likely has multiple sources.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".