Asymmetric environmental regulation, interfuel substitution and carbon leakage
Bibliographic record
Abstract
This paper examines how plants adjust their production in response to asymmetric carbon pricing. When plants compete across areas, asymmetric regulation can lead to carbon leakage, shifting emissions from regulated to unregulated areas. I build a production model with multiple fuel inputs, imperfect competition, and region-specific carbon taxes. Using publicly available Canadian plant-level data on a wide range of air pollutants, I invert the chemical reactions from combustion to back out plants’ fuel usage. I then estimate the model by exploiting variation in the British Columbia (B.C.) and Quebec carbon taxes, which were implemented in 2008 and 2007, respectively. Findings indicate substantial emissions reductions in British Columbia, with 95 % confidence intervals ranging from 7 % to 48 %, and no reduction in Quebec. Contrary to theoretical predictions of carbon leakage, the analysis reveals no statistically significant shift in production toward unregulated provinces. A detailed decomposition reveals that the absence of leakage was primarily due to the regulated plants’ ability to absorb the tax by switching from oil to natural gas and by reallocating output from dirtier to cleaner plants within British Columbia.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".