Comparison of ex-ante modelling assessments of emissions trading - 2023
Bibliographic record
Abstract
- This policy brief synthesises the results from the first annual workshop on ex-ante assessment of emissions trading. It focuses on models assessing the schemes in the EU, UK, China, California, and Québec. - At a time when emissions trading systems (ETSs) are increasing in number and face similar issues, only a few comparisons of ex-ante models exist. - The models show considerable heterogeneity. The differences stem from the specific aim, design, scope, ambition and maturity of each market modelled. - Regarding modelling assumptions, there is an overall reliance of models on Marginal Abatement Cost Curves (MACCs) and a strong impact of parameters such as the discount rate on the assessments. - In terms of predicted prices, an overall increasing trend is observed across jurisdictions, with predicted prices of non-EU ETSs remaining at a lower level than EU prices. This divergence is due to uncertainty regarding abatement costs, scope, maturity, and overlapping policies. - There is a growing interest in capturing market imperfections and investor behaviour. Evaluation of carbon leakage, which still requires extensive modelling work, is also identified as relevant future model extensions. - There is a need for discussion on model comparison to include industry feedback, share experiences and improve the robustness of modelling assumptions. - Closing the loop between the policy process and modelling work is necessary to enhance the predictability of carbon markets and to showcase the consequences of different policy and design choices. Models may also be useful to attribute certain effects to either ETS policies or other policies. This can ultimately improve our understanding of carbon markets in an increasingly dynamic policy landscape.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".