An Environmental-Economic Evaluation of Hybrid Electric and Other Advanced Vehicles
Bibliographic record
Abstract
<div class="htmlview paragraph">Hybrid electric vehicles (HEV) and other advanced propulsion technologies offer greater fuel economy and lower pollutant and tailpipe emissions. Some experts regard the HEV technology as a practical, attractive solution to social concerns about fuel economy (and related fossil fuel conservation) and vehicle emissions. [<span class="xref">Maples 98</span>] The technology is practical; however, an HEV is more expensive to produce than a conventional internal combustion engine (ICE). In a world of limited resources and many petroleum users and emissions sources, the policy question is whether the best use of resources is to build HEV, to improve the fuel economy and lower emissions from other sources, or to devote the resources to other environmental projects.</div> <div class="htmlview paragraph">We compare the second generation (designed for the United States) Toyota Prius, to the conventional internal combustion engine Toyota Corolla. We examine both private (vehicle purchase price, maintenance, and fuel) and social (the pollutant [non-methane organic gases, carbon monoxide, nitrogen oxides] and carbon dioxide emissions) costs. For each of the vehicles, we evaluate lifetime vehicle exhaust emissions as well as upstream emissions from producing the fuel.</div> <div class="htmlview paragraph">We find that the second generation Prius is still not cost-effective in improving fuel-economy and lowering emissions. For the Prius to be attractive to U.S. consumers, the price of gasoline would have to be more than three times greater than the present level. To be attractive to regulators, the social value of abating tailpipe emissions would have to be 14 times greater than conventional values. Alternatively, the value of abating greenhouse gas emissions would have to be at least $220 per ton. We judge that any HEV would have a difficult time competing with the Corolla because of the Corolla's already high fuel economy and low pollutant emissions.</div> <div class="htmlview paragraph">We also examine the costs and benefits of making a GM Silverado pickup truck into a gasoline hybrid. Like the analysis of the Prius, we again find that the price of gasoline or social value of abating carbon dioxide would have to be higher than the base case values in order to payback the cost of converting the vehicle to an HEV. Finally, we find that at current gasoline prices and carbon dioxide valuation, a conventional vehicle would need to have a fuel economy of 13 mpg or less in order for a $4,000 cost of converting it to an HEV to get 30% better fuel economy would pay off. We conclude that HEV will not have significant sales unless fuel prices rise several-fold or unless regulators mandate them.</div>
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".