Bibliographic record
Abstract
Canada is one of the top energy users and CO2 emitters among the OECD countries. However, energy intensity has been declining, on average, by about 1.4 percent since 1980. In this paper, we use the Fisher Ideal Index to determine the contribution of changes in the composition of economic activities and efficiency to a decline in energy intensity in Canada at national, provincial, and industry levels. We also apply panel data estimation methods to further investigate the factors driving energy intensity, efficiency and activity indexes for the period 1981-2008. We test for endogeneity as well as cross-section dependency in the provincial data and control for factors such as climate, policy, and energy endowment. The national and provincial decomposition results suggest that most of the reduction in energy intensity has occurred mainly due to improvements in energy efficiency rather than shifts in economic activities. Within the industry, while energy intensity has declined significantly in manufacturing, it has remained stable in transportation, utilities, and construction, and increased significantly in oil extraction and mining industries. The provincial panel regression results indicate that energy intensity is higher in provinces with higher average incomes, faster population growth, colder climate, and a higher capital-labour ratio, and lower in provinces with higher energy prices and higher investment. The industry panel regression results show that investment has contributed to energy efficiency in utilities and mining and to a shift away from energy-intensive activities in manufacturing and transportation industries. Technological advances have been most effective in increasing energy efficiency in construction and utilities and in decreasing energy-intensive activities in manufacturing industries. The results indicate that although efficiency contributes to a reduction in energy intensity in Canada, increasing activity in energy-intensive industries, such as oil and mining, partially offsets the efficiency gains in other industries.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".