Notice bibliographique
Résumé
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. …
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,012 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,004 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».