Permit sellers, permit buyers: China and Canada’s roles in a global low-carbon society
Bibliographic record
Abstract
The challenge of creating a global low-carbon society is examined from the perspectives of a slow-growing but highly developed economy (Canada) and a fast-growing developing economy (China). Both countries' responses are compared to a similar carbon price schedule (US$10/tCO2e in 2013 rising exponentially to $100 by 2050) using a hybrid technologically explicit and behaviourally realistic model with macroeconomic feedbacks (CIMS). Then additional measures are imposed based on the national circumstances of each country; for Canada we simulate a 50% reduction by 2050, and stabilization for China. The scale of the challenge in all cases requires that every available option be vigorously pursued, including energy efficiency, fuel switching, carbon capture and storage, and accelerated development of renewables; to compensate, there are significant co-reductions of local air pollutants such as SOx and NOx. Finally, the abatement cost schedules of China and Canada are compared, and implications considered for carbon permit flows if the cost schedule of the rest of the developed world is assumed to be similar to that of Canada. We found that the developed world and China could collectively reduce emissions by 50% in 2050 at a price of $175/tCO2e, with permits flowing from the developed countries to China; while abatement costs are lower in China up to $75/t, at higher prices reductions are less costly in the developed world. Our results indicate that a global low-carbon society is feasible, on condition that policy makers are willing and able to impose long-term, credible policy packages with carbon pricing policy as the core element, coupled with supplementary regulations to address market failures.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".