Bibliographic record
Abstract
In 2015, greenhouse gas (GHG) emissions were 722 Mt CO2e (million tonnes CO2 equivalent) in Canada.1 Canada's GHG emissions goals from the Paris Agreement include reducing emissions to 523 Mt CO2e/year by 2030 and 149 Mt CO2e/year by 2050.2 This paper considers four areas of potential emissions reductions, based on known technology and methods that are currently in use globally. Essentially, what can we do easily? Road transportation, sour gas plants, residential buildings, and commercial and institutional buildings are studied to determine recommendations for GHG emissions reductions. A series of quantitative metrics are laid out to test key aspects of each type of emissions reduction considered, including the economic, environmental and social feasibility in Canada, offering comparative analysis and recommendations. Based on this analysis, with known technology in the four areas noted above, an estimated reduction of 68 Mt CO2e/year or a 9% reduction relative to the 2015 baseline can be achieved. Of the areas considered, the most significant potential GHG reductions are in 2 |passenger and freight vehicles. The retrofitting of existing sour gas plants offers a concentrated CO2 stream and therefore an efficient location in which to capture CO2 with known technology. However, the total volume is relatively small and the costs are concentrated on a small stakeholder group. Strong economic incentives may be required in order to proceed with this measure. In addition to GHG emissions reductions, the vehicle and residential retrofits considered here have a modest operating cost savings incentive for consumers due to the associated reduction in energy usage. The proposed emissions abatement projects outlined here can potentially help Canada reduce emissions by 9%, not huge but a start toward Canada's Paris Agreement goals. However, achievement of these reductions will take time. Policy choices are key to accelerating timing and level of uptake of these abatement methods. In addition, policies must consider Canadian preferences and performance over the last twenty-five years. Lower GHG emissions measures have been in the marketplace for years, but consumer preferences do not always follow even when there are economic incentives. This paper also explores the quandary of why Canadians demonstrate two behaviours: they support carbon reduction initiatives in the forms of carbon tax and participation in the Paris Agreement, while at the same time steadfastly owning larger vehicles and living in larger homes. Evidence suggests Canadians embrace current policies and financial incentives to lower their emissions, but in general do not search out how they can reduce emissions further. In fact, their personal preferences align with economic success and growth, rather than policy ideals.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".