Understanding Carbon Markets: An Agent-based Approach to Building an Analytical Model
Bibliographic record
Abstract
Putting a price on carbon emissions has, in recent years, emerged as the single most promising component of any combination of strategies necessary to mitigate climate change, and is an attempt at correcting externality-omissions in cost calculations within capitalist economic frameworks. Our study examines some design alternatives for such a pricing system by exploring the fledgling Alberta carbon market. We attempt to evaluate the performance of these designs on the bases of trade volume, cost efficiency and stability. To achieve this we construct an empirically-calibrated but simple agent-based model, certain aspects of which we selectively modify to incorporate various design options. We make comparisons among these options based on data simulated from the ensuing family of models. We find strong evidence that market design features such as source-of-credits, the scale of the market, and pricing-mechanism are very important considerations that influence the performance of the market. In addition, we find support for the notion that the level of the price cap relative to the average cost of abatement in the market matters, and beyond a threshold, higher price caps are associated with lower levels of performance. Our study holds insights for academic researchers interested in climate issues and market regulation, and presents an analytical tool for stakeholders in government and regulated corporations.
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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 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".