Technology Assumptions and Climate Policy: The Interrelated Effects of U.S. Electricity and Transport Policy
Bibliographic record
Abstract
Although economists prefer a unique, economy-wide carbon price, climate policies are likely to continue to combine technology- and sector-specific regulations with, at best, some degree of carbon pricing. A hybrid energy-economy model that combines technological details with partial macro-economic feedbacks offers a means of estimating the likely effects of this kind of policy mix, especially under different scenarios of technological innovation. We applied such a model, called CIMS-US, in a model comparison project directed by the Energy Modeling Forum at Stanford University (EMF 24) and present here the interrelated effects of policies focused separately on electricity and transportation. We find that technological innovation encouraged by transportation regulation can inadvertently increase emissions from electricity generation and ethanol production to the extent that abatement from the regulation itself is effectively neutralized. When, however, regulation of electricity generation is combined with transportation policy or there is economy-wide carbon pricing, substantial abatement occurs.
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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".