Bibliographic record
Abstract
The Canadian government has just laid out a new plan to reduce green house gas emissions. A central pillar of the plan is to decrease the greenhouse gas (GHG) intensity of production in a number of key sectors rather than impose a direct cap on emissions. This paper considers the proposal in light of historical trends in these sectors. To assess the possible impact of the policy on the performance of targeted sectors, we decompose the change in emission intensities into composition and technique effects using a divisia index approach. Our results demonstrate that the proposed policy pushes Canadian businesses into reductions in emission intensities that they have not previously accomplished. It is not business as usual. Depending on how credits are given for past emission reductions, total sectoral emissions could switch from positive growth to negative growth although it would take much longer to reduce emissions back to 1990 levels. Further, though these sectors have seen a decrease in emission intensities, the policy would accelerate these reductions significantly. Mention the UK in this paragraph as it comes as a surprise in the next paragraph However, the data also shows that Canadian and UK businesses have had only limited success in improving their techniques of production in terms of reducing GHG emissions.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.749 | 0.685 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".