Non-additionality, Overestimation of Supply, and Double Counting in Offset Programs: Insight for the Mexican Carbon Market
Bibliographic record
Abstract
Abstract Mexico utilizes an emissions trading system as one of its carbon pricing instruments. Mexico’s planning, like that of other countries, includes flexible mechanisms such as offsets. Offsets allow market participants to compensate for their emissions through mitigation projects. Offsetting via participation in the Clean Development Mechanism and Joint Implementation was fundamental to the Kyoto Protocol. In contrast, the Paris Agreement is ambiguous about its use. Other national or regional offset programs, such as the EU, Australia, New Zealand, Japan, or Korea, work within emission trading systems. Subnationally, the California-Quebec program has been in effect since 2014. As Greenhouse Gases (GHGs) are global, offsetting allows market participants to compensate for their emissions through mitigation projects, whether domestically or abroad. Given their global scope, such programs present a wide variability in quality. This chapter presents an overview of offset programs worldwide and argues that non-additionality, overestimated supply, and double counting are their three most pressing quality problems. This analysis sheds light upon the nascent Mexican system and its offset program.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".