Assessing the impact of three emission (3E) parameters on environmental quality in Canada: A provincial data analysis using the quantiles via moments approach
Bibliographic record
Abstract
Existing studies rarely examine the simultaneous effects of three emitting indicators (3E) – full emitting non-renewable, non-emitting renewable, and low-emitting nuclear - on three specific greenhouse gases: CO2, CH4, and N2O. We investigate the impact of three energy types on environmental quality, using Canadian data from 1990-2022. It incorporates macroeconomic policies, economic uncertainty, geopolitical risks, and eco-innovation, and employs the quantiles via moments method to explore the evolving relationships among these factors, considering provincial variances. Findings reveal that non-renewable energy sources deteriorate environmental quality by increasing CO2, CH4, and N2O emissions across all quantiles (from q.5 to q.95), while renewable and nuclear energies, along with eco-innovation initiatives, have a beneficial effect by reducing greenhouse gas emissions across all quantiles. Economic policy uncertainty is a contributing factor to greenhouse gas emissions across all quantiles, whereas geopolitical risks primarily impact the middle to upper quantiles (from q.50 to q.95). To counteract the lack of cross-sectional dependence in the quantiles via moments methodology, this paper employs Driscoll and Kraay’s standard errors approach to fortify its findings’ reliability. It concludes with policy suggestions promoting renewable energy and eco-innovation through increased investment, vibrant long-term policies, provincial collaboration, and adoption of green technologies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".