Bibliographic record
Abstract
This paper identifies for transportation planners five key implications of extending cap-and-trade for greenhouse gas emissions to the transportation sector, as envisaged in legislative and regulatory proposals in the U.S. Congress and in the western states and Canadian provinces. First, cap-and-trade would increase gasoline prices as refiners and fuel importers pass on the cost of carbon allowances; a $30 per metric ton price of carbon allowances equates to 27 cents per gallon of gasoline. Second, transit, smart growth, and other emission reduction projects might be eligible for billions of dollars in revenue from carbon allowance auctions. Third, as emissions would be constrained at the level of the cap, transportation projects would be unlikely to have any impact on aggregate emissions. Any environmental benefit of a project (reduced emissions) would be converted into an economic benefit (reduced carbon allowance prices and thus reduced compliance costs in other sectors). Fourth, the converse of this argument suggests a weakening of the potential to use the environmental review process to mitigate emissions from development projects. There may be an economic impact (higher carbon allowance prices), but not an environmental impact (emissions would be constrained at the level of the cap). Finally, extending cap-and-trade to the transportation sector would eliminate the potential for revenue from the sale of offsets, as this would double count emission reductions.
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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.045 | 0.008 |
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".