Examining environmental performance : cross-country analysis of Canada and the United States
Bibliographic record
Abstract
The purpose of this Masters' Research Paper is to compare Canada and the United States' (U.S.) contributions to pollution and carbon emissions.This paper considers the Environmental portion of the Environmental, Social and Governance metric (ESG) and, in particular, a review of seven industries that are known to cause pollution.The study uses ESG data from Refinitiv with a sample of 35,678 observations over the period of 2017 to 2021.The specific industries used are Financials, Energy, Transportation, Manufacturing, Construction, Fashion, and Technology.The findings show that overall, Canada has a higher Environmental Score, with a mean environmental score of 29.301 compared with a U.S. score of 19.36.When comparing specific industries, the financial industry results suggest that Canada exhibits a statistically significant advantage compared to the U.S. in terms of environmental score.For the energy, transportation, and construction industries, Canada has significantly higher scores then the U.S.However, no significant difference was found in the mean environmental scores for the manufacturing industry.Additionally, the mean environmental scores in the fashion and technology industries were both significantly higher in Canada compared to the U.S.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".