Does carbon pricing policy improve energy efficiency? New evidence from Canadian provinces
Bibliographic record
Abstract
Given the increasing damage effect of climate change, the Canadian government has taken a further step to ensure that carbon pricing is implemented in all provinces to reduce its GHGs emissions as a means of addressing climate change. Thus, this paper examines the effect of carbon pricing on energy consumption which is the main source of CO2 emission to enhance our understanding beyond carbon-pricing effects on households and revenue recycling in the prior research. Our study analyzes the energy efficiency of Canada using the province-level data between 2000 and 2021, by developing an SFA production model to understand the drivers of energy consumption and then compute the total factor energy efficiency index while controlling for the role of carbon pricing. Our empirical results reveal that increased labor input, as well as output, mitigate the rising capital-induced energy consumption, while carbon pricing policy plays no significant role in influencing energy consumption. However, the effectiveness of carbon pricing is found in reducing province-level energy efficiency. This striking evidence suggests that policymakers should consider a labor-oriented energy saving scheme and conduct a comprehensive review of the current carbon pricing policy in order to align with energy efficiency interventions toward the achievement of Canada’s Paris Agreement 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 | 0.003 | 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) | 0.005 | 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".