Sinks, Emissions Intensity Caps and Barriers to Emissions Trading
Bibliographic record
Abstract
The European Union (EU) has raised concerns about the use of sinks and an Emissions Intensity system in Canada and has decided not to allow sinks to be included in its trading system. Despite this restriction, the EU has shown interest in expanding its trading system to include other countries such as Japan and Canada, while Canada hopes to use sinks and a domestic trading system with an Emissions Intensity regulatory mechanism to meet its Kyoto GHG commitments. In this paper, we briefly discuss some of the implications of the Emissions Intensity regulations scheme, and then develop a simple credit model with trade to illustrate the effect of a trade ban put in place by the EU, first, when it is fully binding and second, when there are countries that can act to arbitrage both markets (e.g., Japan). We also look at the possibilities of using harmonization frameworks to control trade, as well as using a form of discounting with respect to Canadian credits. We show that it is highly unlikely that a trade barrier will increase the use of emission reduction (and decreased use of sinks), and that, particularly in the likely case that Canada will import credits, trade barriers will actually increase the use of sinks. We do find, however, that the use of discounting could serve as a possible policy alternative to increase the use of EU reductions, while decreasing the quantity of Canadian sink credits.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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".