Energy use in Canada: environmental impacts and opportunities in relationship to infrastructure systems
Bibliographic record
Abstract
Canada exhibits high per capita energy consumption. This paper examines energy use in Canada by region and sector, focusing on four sectors most relevant to civil engineering activities: residential, commercial–institutional, construction, and transportation. Environmental impacts associated with major energy sources including coal, petroleum products, natural gas and electricity are reviewed. The relationships between energy consumption and infrastructure design are analysed. Opportunities for reductions are identified in building design, water and waste-water systems, urban form, and transportation. Large improvements in commercial and residential energy efficiency can be achieved through the implementation of existing technologies in building upgrades, retrofits, and rebuilds. Increasing surface albedos and more extensive use of vegetative shading and consideration of the geometric properties of urban canyons and their microclimatic effects also allow for considerable energy savings. The incorporation of mixed-modal transit, walking and cycling paths, and community-scale design as elements of long-term transportation planning and the development of alternative transportation technologies have the potential to considerably reduce per capita energy use. The development and implementation of alternative energy supply technologies include energy recovery from waste-water treatment.Key words: energy, infrastructure, urban, sustainability, sustainable development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".