Uncertainty Discounting for Land-Based Carbon Sequestration" Submitted to Canadian
Bibliographic record
Abstract
The effect of stochastic factors on soil carbon makes the quantity of carbon generated under a sequestration project uncertain. Hence, the quantity of sequestered carbon may need to be discounted to avoid liability from shortfalls. We present a potentially applicable uncertainty discount and discuss difficulties that might arise in empirical use. We insist that the variance in historical crop yields across geographical areas is used to derive a proxy variance for forming an uncertainty discount for carbon projects. Application of our approach suggests that project level uncertainty discounts would be 15–20 % for the East Texas region. Key Words: carbon seqestration, discount, uncertainty JEL Classifications: H43, Q54 Reduction of atmospheric carbon dioxide (CO2), a major greenhouse gas (GHG), is central to addressing the climate change problem and in the formation of policies that aim to limit atmo-spheric GHG levels (see discussion in IPCC 2007a,b). Land-based carbon sequestration—a process whereby plants and trees, through pho-tosynthesis processes, trap atmospheric CO2 and fix carbon into soil and plant body mass—has drawn attention as a strategy for GHG reduction. If GHG emissions reductions are pursued, a carbon market may be created as advocated for example in the Kyoto Protocol (UNFCCC) or potential legislation like Lieberman-Warner bill (Lieberman and Warner Bill) where entities se-questering carbon may be able to generate GHG reduction credits that buying emitters can use to offset their emissions (as discussed in Butt and McCarl; Kim and McCarl). Various studies have explored the potential of land-based carbon sequestration strategies such as afforestation, reforestation and other land use changes (Adams et al.; Parks and
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.118 | 0.009 |
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".