Does carbon pricing policy influence carbon emission intensity? New Evidence from Canadian Provinces
Bibliographic record
Abstract
Exploring the role of carbon pricing policy in reducing carbon emission intensity remains an urgent and ongoing debate among academics and practitioner communities.Unlike the prior research relying on a single-factor indicator for carbon emission intensity with inadequate attention to endogeneity issues, this study investigates the influence of carbon pricing policy on Canadian provinces' carbon emissions over the sample period 2000-2022.The study makes a novel contribution by developing a theoretically grounded empirical model to mitigate the risk of biased estimates from a single-factor indicator while allowing for heterogeneity and addressing the issue of endogeneity in its production SFA (stochastic frontier analysis) settings.The study's SFA results reveal that carbon pricing policy significantly influences the level of the province's carbon emissions by reducing carbon inefficiency.Furthermore, economic growth mitigates carbon emissions intensified by an increase in the amount of capital equipment and energy consumption.On the other hand, multi-factor carbon emission efficiency exhibits significant variations across Canadian provinces.Thus, it is a worthy recommendation for Canadian policymakers to align the use of advanced equipment with carbon emission reduction targets.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".