Journal of Economic Perspectives—Volume 26, Number 1—Winter 2012—Pages 93–118 Reducing Petroleum Consumption from Transportation
Bibliographic record
Abstract
The he United States consumes more petroleum-based liquid fuel per capita than any other OECD high-income country—30 percent more than the second-highest country (Canada) and 40 percent more than the third-highest (Luxembourg). The transportation sector accounts for 70 percent of U.S. oil consumption and 30 percent of U.S. greenhouse gas emissions. Gasoline and diesel fuels alone account for 60 percent of oil consumption. The economic argu-ment for seeking to reduce this level of consumption of petroleum-based liquid fuel begins with the externalities associated with high levels of U.S. consumption of petroleum-based fuels. First, burning petroleum contributes to local pollution. The transportation sector accounts for 67 percent of carbon monoxide emissions, 45 percent of nitrogen oxide (NOX) emissions, and 8 percent of particulate matter emissions. These pollut-ants lead to health problems ranging from respiratory problems to cardiac arrest. Furthermore, automobiles emit both NOX and volatile organic compounds which, combined with heat and sunlight, form ground-level ozone, or smog. The papers by Currie and Walker (2011) and Knittel, Miller, and Sanders (2011) both find that decreases in traffic reduce infant mortality. Second, burning a gallon of gasoline causes roughly 25 pounds of carbon dioxide to be emitted into the atmosphere, which raises the risks of destructive climate change. Greenstone, Kopits, and Wolverton (2011) estimate the social cost
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.062 | 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".