Picking winners: assessing the costs of technology-specific climate policy for U.S. passenger vehicles
Bibliographic record
Abstract
Policymakers implementing climate policies that aim to direct technological change must decide the extent to which such policies will be technology-neutral or technology-specific.There is a debate over the effect that such alternative designs will have on a policy's expected cost-effectiveness.Researchers have investigated this question in top-down models that focus on the early phases of technological change (R&D), but no one has yet compared technology-neutral and technology-specific policy designs for the later stages of technological change (commercialization and diffusion), in a model that includes explicit energy technologies.I model these policy designs using a case study of the US passenger vehicle sector in a hybrid simulation model that is not only technology explicit, but behaviourally-realistic and that possesses some degree of macroeconomic feedbacks.I find that technology-specific vehicle mandates results in lower policy cost-effectiveness than a carbon tax on vehicle fuel because the vehicle mandate has a higher risk of policymakers "picking the wrong winner."However, I find as well that a technology-specific electric vehicle mandate is able to meet a key adoption threshold for getting low-cost emission reductions from PHEVs.Key limitations of my model include: consumers have zero foresight, exogenous assumptions for fuel supply and cost, and assumptions that seem to implicitly favour the adoption of plug-in hybrid electric vehicles over biofuels.The implications of my results are that further research should investigate ways in which technology-neutral and technology-specific policies can be combined to increase expected policy cost-effectiveness and minimize the risk of "picking the wrong winner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".