Reporting Bias and Monitoring in Clean Development Mechanism Projects*
Bibliographic record
Abstract
ABSTRACT The Clean Development Mechanism (CDM) is a flexible carbon market mechanism managed by the United Nations. The program grants tradable carbon emissions credits (Certified Emission Reductions) for carbon‐reducing projects in developing countries. A project can only be admitted to the program if it is not financially profitable, and thus would not take place without the emission credits granted through the CDM. In this paper, we examine how monitoring reduces incentives of companies to bias the reported expected financial viability of potential CDM projects to gain admission to the program. We find that reported rates of return, which are a key factor for admission to the program, tend to be downwardly biased and are negatively associated with the expected benefits stemming from forecasted greenhouse gas reductions. However, monitoring from various sources mitigates some of the distorted incentives and related reporting bias. Furthermore, the monitoring effect becomes much stronger after 2008, when the CDM Executive Board implemented a series of measures to strengthen the additionality testing that provides guidance for program applications.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".