Bibliographic record
Abstract
In the United States, proposals for gasoline tax hikes have consistently met with broad-based congressional opposition. Although such taxes are a common and effective method of conserving energy in other industrialized nations, U.S. policy has traditionally relied on regulatory programs rather than fuel taxes to promote energy efficiency in automotive transportation. This book examines both the political causes and the economic effects of this idiosyncratic policy preference. Moderating the consumption and importation of oil has been an explicit goal of the United States over the past twenty years. Pietro S. Nivola and Robert W. Crandall argue that a higher levy on gasoline would be a more efficient way of achieving this goal than current automotive fuel economy standards. In fact, they find that an additional excise of less than twenty-five cents per gallon over the past dozen years would have conserved more oil than has the existing policy of administering gas mileage requirements for new passenger vehicles. And such a tax, they maintain, would not be as detrimental to the economy as opponents fear, nor as regressive as they claim. Why, then, is there such a strong national resistance to a fuel tax in the United States? And why is there less resistance in other countries? The authors examine the development of motor-fuel excises in Great Britain, France, Germany, Japan, and Canada, and explain the historical and political factors that have led to different national policy orientations. Turning their attention back to the United States, Nivola and Crandall show how regulatory measures have fallen short of their goal and why political barriers to bolder taxation of gasoline remain formidable. They conclude by offering suggestions for new directions in U.S. energy policy at the federal, state, and local level.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.388 | 0.211 |
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".