How You Count Carbon Matters: Implications of Differing Cookstove Carbon Credit Methodologies for Climate and Development Cobenefits
Bibliographic record
Abstract
The opportunity to apply for carbon credits for cookstove projects creates a source of funding that can be leveraged to promote the "win-win" environmental and development benefits of improved cookstoves. Yet, as in most environment-development efforts, unacknowledged trade-offs exist under the all-encompassing "win-win" claims. This study therefore compares different scenarios for calculating cookstove carbon credits, including comparing different types of stoves using different fuels, different methodologies and theoretical scenarios to account for a range of climate-relevant emissions. The results of the study highlight the following: 1) impacts of different assumptions made within carbon credit methodologies, 2) discussion around potential trade-offs in such projects, and 3) considerations needed to truly promote sustainable development. The Gold Standard methodology was more comprehensive in its accounting and generally calculated more carbon credits per scenario than the Clean Development Mechanism methodology. Including black carbon in calculations would be more reflective of climate-relevant stove emissions and greatly increase the number of credits calculated. As health and other development benefits are not inherently included in carbon credit calculations, to achieve "win-win" outcomes, deliberate decisions about project design need to be made to ensure objectives are met and not simply assumed.
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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 | 0.059 | 0.169 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".