Canadian energy use and greenhouse gas emissions in the 1980’s and 1990’s : decomposition of changes, extrapolation of trends and comparison to other oecd countries
Bibliographic record
Abstract
This thesis examines energy use in Canada in the early to mid-1980's to mid-1990's to investigate what factors caused energy use and greenhouse gas emissions to rise. Trends from this period in energy use and fuel mix are also projected to the years 2000 and 2010. In addition, Canada is compared to 12 other OECD countries to determine whether differences in climate, geography and industrial structure account for differences in absolute and per capita energy use between Canada and these countries. Changes in activity were the main drivers of the increases in energy use and greenhouse gas emissions in the 1980's and 1990's. This influence was partially offset by declines in energy intensity. Structural changes tended to have a less profound impact. Based on trends from this period, both energy use and greenhouse gas emissions will continue rising. More positively, there already are trends towards less greenhouse gas-intensive fuels in some sectors. Climate, geography and industrial structure do not account for differences in per capita energy use between Canada and other industrialized countries. The one exception is the United States. This implies that, with the exception of the U.S., Canada is relatively less energy efficient than other industrialized countries.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".