Tracking banking in the Western Climate Initiative cap-and-trade program
Bibliographic record
Abstract
Abstract The Western Climate Initiative is a multilateral cap-and-trade program in California and Québec. The California climate regulator has called for cap-and-trade to deliver nearly half of the emission reductions needed to achieve the state’s legally binding limit on greenhouse gas emissions in 2030, making the program the single biggest driver of the state’s post-2020 policy portfolio. However, the program’s supply of compliance instruments has persistently exceeded emissions subject to the program—a condition known as overallocation, which independent studies have projected may continue into the mid-2020s. If market participants purchase and bank excess compliance instruments for future use, they may be able to comply with the program’s regulations while nevertheless emitting significantly in excess of the state’s legally binding 2030 limit. Here, we present methods for tracking observed banking behavior on both an annual and multi-year compliance period basis. By the end of 2018, market participants had already acquired more unused compliance instruments than the regulator anticipated for 2020. The size of the private bank is now comparable to the cumulative mitigation expected from the program over the period 2021 through 2030, raising questions about the program’s ability to achieve its expected reductions. Beyond diagnosing market conditions, banking metrics can also help policymakers design dynamic program reforms that increase program stringency conditional on observed market behavior deviating from expectations.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".