Fuel Economy and Greenhouse Gas Emissions Labeling and Standards for Plug-In Electric Vehicles from a Life Cycle Perspective
Bibliographic record
Abstract
Reflecting the greenhouse gas (GHG) emissions attributable to plug-in electric vehicles (PEV) on energy and emissions labels, and in vehicle GHG emissions regulations, is complex due to spatial and temporal variation in fueling sources and vehicle use. The relative environmental performance for conventional gasoline vehicles can be reflected by the fuel economy of the vehicle due to the strong correlation between fuel economy and vehicle life cycle emissions. However, this correlation does not hold for PEVs and a more comprehensive emissions accounting methodology needs to be utilized to evaluate their environmental performance. This thesis is organized into two studies. The first evaluates PEV GHG emissions vehicle labeling and the effects of regional grids and regional daily vehicle miles traveled (VMT) on the total vehicle life cycle energy and GHG emissions. The model results indicate that only 25% of the life cycle emissions from a representative plug-in hybrid vehicle are reflected on current U.S. Environmental Protection Agency (EPA) vehicle labeling. Unexpectedly, for two regional grids the life cycle GHG emissions results were higher in electric mode than in gasoline mode. A recommendation is made that labels include stronger language on their deficiencies and provide ranges for GHG emissions from vehicle charging in regional electricity grids to better inform consumers. The second study evaluates U.S. EPA’s GHG emissions accounting methodology and current and future standards for new electrified vehicles. The current approach employed by the EPA is compared with an accounting mechanism where the actual regional sales of PEVs, and the regional electricity emission factor in the year sold, is used to determine the vehicle compliance value. The results showed that in the absence of a major policy shift, the small changes in the emission factors observed suggest that the complexity involved in tracking and accounting for regional PEV sales will not dramatically increase the effectiveness of the regulations to capture PEV electricity related GHG emissions.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".