Status Review of Oregon’s Clean Fuels Program, 2016–2018 Q3 (Revised Version)
Bibliographic record
Abstract
\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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.006 |
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; both teacher heads agree on what is shown here.
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".