Supporting energy pricing reform and carbon pricing policies through crediting
Bibliographic record
Abstract
With the successful conclusion of the Paris Conference of the Parties to the United Nations Framework Convention on Climate Change (UNFCCC), international carbon market mechanisms became a key element of the future, post-2020, international climate policy architecture.Paris delivered a new UNFCCC-governed baseline and crediting mechanism, applicable both in developing and developed countries.Paris also recognized voluntary cooperative approaches that enable international transfer of mitigation outcomes to be accountable against countries' mitigation pledges (in the form of nationally determined contributions).In both cases, the scope of mitigation activities is likely to go beyond the project-by-project level as in Clean Development Mechanism (CDM) and Joint Implementation (JI).Earlier discussions suggested that sectoral crediting approaches (or approaches using other aggregates, such as the level of a city), might emerge.Crediting of policies is clearly a further option, and the Paris agreement encourages results-based payments for policy approaches in the context of REDD+.Policy crediting-i.e., the crediting of the emission reductions resulting from the implementation of a policy action or components of it-is a new concept.So far there exist no real case examples.Some work was done on regulatory policies (such as energy-efficiency standards, including under the CDM and with an aim to reforming the CDM beyond a project-level scope) both from a methodological side and through blueprinting of operational models.Similar approaches were developed for policies such as feed-in tariffs for renewable energy.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".