EVALUATING THE DUAL ROLE OF CARBON PRICING IN ENVIRONMENTAL PERFORMANCE AND INNOVATION OF CANADIAN PROVINCES USING A DIFFERENCE-IN-DIFFERENCES ANALYSIS
Bibliographic record
Abstract
Carbon taxation or other policies aiming at the mitigation of climate change become more critical in the promotion of sustainable environmental and economic results. This paper uses a panel dataset of 102 Canadian firms over the period between 2010 and 2023 sample and estimates the effect of carbon taxation on carbon emissions at the firm level and total factor productivity (TFP) using a method of difference-in-differences (DiD) estimation. The findings confirm the Porter Hypothesis which proposes that emissions can be largely reduced through the carbon tax by an estimated 0.8 units along with a 0.22 increase in the TFP which indicates that carbon tax can spur environmentally friendly efficiency in production. We found that factor intensities, such as firm size and capital intensity has a major impact on these effects. The results suggest that carbon taxation is a powerful instrument to lower environment externality without undermining, and possibly increasing, firm competitiveness. The advice to the policymakers is stated to shape carbon tax measures and pre-incentives on innovation to continue economic development with respect to climate goals.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".