Price Regulation and Environmental Externalities: Evidence from Methane Leaks
Bibliographic record
Abstract
We estimate expenditures by US natural gas distribution firms to reduce natural gas leaks. Reducing leaks averts commodity losses (valued at around $5/Mcf), but also climate damages ($27/Mcf) because the primary component of natural gas is methane, a potent greenhouse gas. In addition to this private/social wedge, incentives to abate are weakened by this industry's status as a regulated natural monopoly: current price regulations allow many distribution firms to pass the cost of any leaked gas on to their customers. Our estimates imply that too little is spent repairing leaks-we estimate expenditures substantially below $5/Mcf, i.e. less than the commodity value of the leaked gas. In contrast, expenditures on accelerated pipeline replacement are in general higher than the combination of gas costs and climate benefits (we estimate expenditures ranging from $48/Mcf to $211/Mcf). We conclude by relating these findings to regulatory-induced incentives in the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".