Bibliographic record
Abstract
The United States consumes more petroleum-based liquid fuel per capita than any other developed country--30 percent more than the second-highest consumer (Canada) and 40 percent more than the third-highest consumer (Luxembourg). The majority of U.S. oil consumption--70 percent--goes into the transportation sector. A variety of policies has been adopted to reduce petroleum consumption, with the justification for such policies usually being the negative effects of this consumption. For example, the transportation sector contributes to local pollution, accounting for 67 percent of carbon monoxide emissions, 45 percent of nitrogen oxide (NOx) emissions, and significant emissions of particulate matter and volatile organic compounds. These emissions contribute to air pollution and lead to health problems ranging from respiratory ailments to cardiac arrest. Both NOx and volatile organic compounds are precursors to ground-level ozone (smog). The transportation sector accounts for roughly 30 percent of U.S. greenhouse gas emissions, contributing to climate change. In addition, oil consumption leads to externalities associated with energy security and to potential macroeconomic costs associated with oil dependency. Within the United States, a number of policies aimed at reducing oil consumption rely on standards. (1) In the transportation context, performance standards require manufacturers, for example automobile manufacturers, to meet some performance benchmark. In the case of Corporate Average Fuel Economy (CAFE) standards, the geometric average fuel economy of a given manufacturer must exceed the benchmark. For local pollutants, standards are typically set on average per-mile emissions of a given pollutant, such as nitrogen oxides or carbon monoxide. Policymakers more recently have adopted performance standards for fuels. For example, California's Carbon Fuel Economy (LCFS) sets a maximum average carbon intensity for fuels--effectively a CAFE standard for fuels. The LCFS in essence requires a fuel producer to sell a prescribed amount of comparatively low-carbon fuels, such as some types of ethanol, for every gallon of gasoline sold. At the federal level, the Renewable Fuel Standard (RFS), while not setting a direct performance standard, sets a minimum total amount of different types of ethanol that must be sold in a given year. The way the RFS is implemented makes it similar to a performance standard. While the United States traditionally has relied on performance standards, taxing various externalities directly--so-called after the British economist A.C. Pigou who advocated them--would provide an alternative approach to reducing the externalities associated with fuel consumption. In a series of research studies, Stephen Holland, Jonathan Hughes, and I compare the economic consequences of fuel-based performance standards and Pigouvian taxes, notably carbon taxes. This research summary briefly describes the work and points to future directions for research. The Economic Efficiency of Low Carbon Fuel Standards Our first project in this line of research analyzes how an LCFS affects market equilibria and uses simulations to understand the outcomes of national LCFSs that reduce the average carbon intensity of fuels by 1, 5, and 10 percent. (2) Our theoretical modeling illustrates that a performance standard can be thought of as a tax-and-subsidy program. In particular, any product whose carbon intensity is better than the standard is subsidized, while any product whose carbon intensity is worse than the standard is taxed. The relative size of the tax/subsidy moves linearly with the fuels' carbon intensities. We can readily compare these pricing effects to those of Pigouvian taxes. Under Pigouvian taxes, the tax moves linearly with the fuels' carbon intensities, but no fuels are subsidized. We show that if demand is perfectly inelastic, then an LCFS can achieve economic efficiency, however, if demand is not perfectly inelastic the average cost of the LCFS per unit of carbon reduced will exceed the average cost of carbon reductions under a Pigouvian carbon tax. …
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".