Cost, innovation, and emissions leakage from overlapping climate policy
Bibliographic record
Abstract
Jurisdictions have implemented a variety of policy instruments to mitigate greenhouse gas emissions. However, interactions between overlapping climate policies can lead to unintended impacts. This study demonstrates how interactions between an incomplete emissions cap and additional climate policy can result in higher emissions and higher average abatement costs relative to an emissions cap alone. This sectoral policy emissions displacement effect is then quantified through simulations using a computable general equilibrium model for the case of California's low-carbon fuel standard (LCFS) and cap-and-trade program. Emissions increase as a result of the LCFS incentivizing greater production of alternative transportation fuels with emissions not covered by the emissions cap. Emissions leakage can be mitigated by incorporating elements of a fixed-price instrument (i.e. carbon tax) to improve policy complementarity or requiring an obligation for the lifecycle GHG emissions of fuels under the emissions cap. • Overlapping climate policies can unintentionally increase emissions and cost. • Simulations find that California's LCFS with CAT drives emissions leakage. • Leakage can be mitigated by regulating lifecycle GHG emissions under the cap. • Induced innovation from LCFS reduces but fails to offset higher policy costs. • Fixed-price instruments provide greater policy complementarity than fixed-quantity.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".