An open-source model of the Western Climate Initiative cap-and-trade programme with supply-demand scenarios to 2030
Bibliographic record
Abstract
The Western Climate Initiative (WCI) cap-and-trade programme consists of two linked carbon markets in California and Québec. It is intended to play a central role in reducing greenhouse gas emissions pursuant to both jurisdictions’ 2030 limits on economy-wide emissions, but the programme features a growing bank of surplus compliance instruments (allowances and carbon offsets) that could put participating governments’ climate targets at risk. To aid in understanding the range of possible outcomes, we built WCI-RULES, an open-source model that simulates the WCI programme’s supply-demand balance through to 2030. By using the latest historical data and representing all relevant programme regulations in computer code, WCI-RULES accurately depicts the supply side of the WCI programme and allows users to explore programme outcomes across a range of future demand-side scenarios. Model users can specify three demand-side inputs that have the largest effect on projections of the programme’s supply-demand balance: future trends in emissions covered by the programme, allowance auction outcomes, and regulated parties’ use of carbon offset credits. The model simulates neither allowance prices nor price-induced mitigation, but is interoperable with other analyses that do, and can therefore serve as a basis for inter-model comparison with other studies. We find that oversupply conditions persist across a wide range of scenarios, including one that matches the assumptions of California’s official 2030 climate strategy. If unaddressed, these conditions could frustrate participating governments’ ability to reach their economy-wide 2030 climate targets. Key policy insightsWCI programme caps have exceeded regulated emissions since the programme’s inception, creating an oversupply that could jeopardize the programme’s ability to achieve its expected emission reductions.Using a new open-source model, WCI-RULES, we show that oversupply conditions persist across a range of modelled emission scenarios through to 2030, so that emissions could significantly exceed programme caps in the mid-to-late 2020s.If regulated emissions exceed programme caps in the mid-to-late 2020s, it will be difficult for California and Québec to reduce their economy-wide emissions below statutory limits.WCI-RULES can be extended in future work to simulate the effects of potential regulatory reforms as well as any new entrants to the WCI programme. WCI programme caps have exceeded regulated emissions since the programme’s inception, creating an oversupply that could jeopardize the programme’s ability to achieve its expected emission reductions. Using a new open-source model, WCI-RULES, we show that oversupply conditions persist across a range of modelled emission scenarios through to 2030, so that emissions could significantly exceed programme caps in the mid-to-late 2020s. If regulated emissions exceed programme caps in the mid-to-late 2020s, it will be difficult for California and Québec to reduce their economy-wide emissions below statutory limits. WCI-RULES can be extended in future work to simulate the effects of potential regulatory reforms as well as any new entrants to the WCI programme.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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".