Climate and transportation policy sequencing in California and Quebec
Bibliographic record
Abstract
Abstract We compare flexible low‐carbon regulations in the transportation sector and their interaction and sequencing with greenhouse gas emissions trading systems in California and Quebec. As momentum builds for greater climate action, it is necessary to better understand how carbon markets and other low‐carbon transportation policies influence one another. First, we demonstrate that emissions trading between California and Quebec has been asymmetric, with linking having little influence on carbon prices from California's perspective but leading to a considerable cost reduction from the point of view of Quebec. Second, we present evidence that Quebec has replicated many of California's low‐carbon transportation policies that promote increased electric vehicle use, where Quebec has an advantage, while deferring to the Canadian federal government with regard to policies that incentivize the production of other low‐carbon transportation fuels. Third, we demonstrate that while the stringency of the policy mix of carbon pricing and flexible transportation regulations has increased over time in both jurisdictions, the stringency of flexible regulations has been more aggressively ratcheted up and is expected to continue to dominate. Overall, our findings suggest that the policy sequence observed in California and Quebec can be attributed to the political economy benefits that the selected instruments confer to governments seeking to move from the middle towards the bottom of the clean technology experience curve. We discuss a number of important research questions and associated hypotheses emanating from our findings, which provide the basis for more in‐depth studies involving a larger universe of cases and economic sectors.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".