Status Review of Oregon’s Clean Fuels Program, 2016–2018 Q3 (Revised Version)
Notice bibliographique
Résumé
\n Highlights \n \n As part of the state’s overall strategy to reduce greenhouse gas (GHG) emissions, Oregon’s Clean Fuels Program (CFP) aims to reduce transportation sector emissions by incentivizing innovation, technological development, and deployment of low-emission alternative fuels and vehicles. It isdesigned as a performance standard, rather than a prescriptive approach to emissions reduction. It sets an annual declining target in fuel carbon intensity (CI) with a goal of 10% reduction by 2025 relative to 2015 levels.\n The CFP has been in effect for three years, with relatively small but growing CI reduction targets of 0.25% in 2016, 0.5% in 2017, and 1.0% in 2018, with a 2019 CI target of 1.5%. The CFP had 163 registered parties and 283 transportation fuel pathways available for use as of the end of 2018.\n From 2016 through 2018 Q3, total emissions reduction requirements were 2.4 million metric tons (MMT) CO2e and reported emissions reductions were 2.0 MMT CO2e, representing overcompliance of over 421,000 tons CO2e and creating a systemwide “bank” of program credits(each representing 1 MT CO2e) that can be used to meet future targets. Data for 2018 lacked residential electricity credits at the time of writing.\n The program generated excess credits relative to deficits in every quarter through 2017. With 2018 electricity credits not yet reported, 2018 deficits through Q3 exceeded credits by under 1,700, well below the 30,000 credits generated by residential electricity in 2017 Q1–Q3, and theabout 29,000 credits for the same category that would be generated under 2018 standards given the same energy.\n Aggregate alternative fuel energy consumption remained approximately stable over the program period—the program’s operation thus far. Ethanol contributed the largest share of alternative fuel and remained between 10% and 11% by volume of blended gasoline, at or just above the“blendwall” of 10% blends, through the period. Between 2016 and 2017, the only two years of complete data, transport energy from fossil natural gas, biogas, propane, and non-residential electricity each grew by over 50%, and from biodiesel grew by over 7%.\n The average annual CI rating for most reported alternative fuels declined between 2016 and 2018 through Q3, including the biggest volume contributors, ethanol (just under 1.5% decline) and biodiesel (just over 17% decline).\n Prices of CFP compliance credits (each representing 1 MT CO2e) remained in the $40–$50 range through 2016 and 2017. The yearly average increased to $84 in 2018 as volumes traded also rose. Data through March 2019 indicate an average price around $145.\n Oregon’s CFP shares some design similarities with California’s Low Carbon Fuel Standard(LCFS), but also has some differences in terms of program targets and baseline fuel blends, treatment of indirect land use change, residential electricity for electric-vehicle (EV) charging, and other credit generation and credit market elements. The programs, along with a similar policyin British Columbia, are part of the Pacific Coast Collaborative commitment to low carbon fuels and economies among these jurisdictions. Washington state is currently considering a similar clean fuel standard as part of its legislative process.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,003 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,006 |
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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».