Comparison of ex-ante modelling assessments of emissions trading - 2023
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 it