Design and Costs of a Measurement Protocol for Trades in Soil Carbon Credits
Bibliographic record
Abstract
Previous work has demonstrated that in the absence of transaction costs, contracts that pay producers per carbon (C) credit are more efficient than those that tie payments to changes in management practices. In this paper we develop a measurement protocol to support contracts for C credits and estimate its implementation costs using an empirical example. We find that the costs of implementing a measurement protocol for soil C credits depend on: the price of credits; the regional heterogeneity in C values as well as assumed error and confidence intervals. We find that the upper estimate of measurement costs associated with a contract that pays producers per C credit can be as little as 3% of the value of a credit. These contract measurement costs are less than the efficiency gains from implementing a per‐credit contract. Des travaux antérieurs montrent que si la transaction ne coûte rien, les ententes prévoyant la rémuné ration des agriculteurs par cré dit carbone (C) sont plus efficaces que celles oè les paiements sont lié s à l'adaptation des pratiques culturales. Dans leur article, les auteurs proposent une mé thode de calculpour de telles ententes et estiment ce que coûterait son implantation au moyen d'un exemple empirique. On constate que, pour les cré dits C du sol, le coût de mise en æuvre dépend du prix des cré dits, de l'hé térogénéité régionale de la valeur des crédits ainsi que dxe l'erreur présumée et des intervalles de confiance. On se rend compte que la plus haute estimation du coût des ententes rémunérant les agriculteurs enfonction des crédits C ne dépasse pas trois pour cent de la valeur du crédit. De tels coûts sont inférieurs aux gains de productivité résultant de l'adoption d'une entente articulée sur les crédits C.
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.
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 | 0.062 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".