What is Canada’s “Fair Share” of the Global Emissions Burden? An Examination of Fair and Proportional Emissions Reduction Targets
Bibliographic record
Abstract
According to the United Nations Intergovernmental Panel on Climate Change, global temperatures are warming by approximately 0.1-0.3°C per decade. As an estimated 1.1°C of global temperature warming above pre-industrial levels has already occurred, 1.5°C of global warming will likely occur sometime between 2030 and 2052. While international coordination is critically needed to allocate emissions amongst states, such a suggestion raises the contentious question of how to equitably distribute emissions amongst states. This paper uses several equity approaches to consider what might comprise Canada’s “fair” emissions reduction target. A literature review conducted by this author revealed two studies which allow for higher atmospheric concentrations that would not limit warming to 1.5°C \nas well as three studies which comply with 1.5°C pathways. Every “fair” target suggested by these five studies is significantly more ambitious than Canada’s present emissions reduction target. At minimum, these proposed targets call for Canada to nearly double its emissions reduction target, however, multiple targets call for Canada to reach net-zero emissions by 2030 and undertake mitigation efforts to further reduce emissions beyond its own borders. This paper \nconcludes by highlighting several strategies to work towards setting and meeting fair emissions reduction targets in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".