Bibliographic record
Abstract
Abstract The current international climate greenhouse gas accounting system excludes the CO2 removed from the atmosphere and stored in harvested crops, and the CO2 emissions that occur when crops are oxidized, viewing both fluxes as carbon-neutral activities. Despite being previously described by Searchinger et al. (2009) as a “critical flaw in climate accounting,” all parties within the United Nations Framework Convention on Climate Change continue to treat these CO2 fluxes as carbon-neutral activities. A two-region trade model is used to show that when harvested-grain biomass as carbon-neutral there is an incentive for parties committed to reducing emissions under the Paris Agreement to develop policies that use grains to produce biofuels, with no offsetting policy incentive to produce the grains required to meet this additional demand. A solution to the flaw in accounting is to treat harvested grain CO2 fluxes the same as fossil fuel CO2 and expand National GHG Inventory accounts to include the CO2 sequestered in harvested grains, and the CO2 emissions that occur when grain is consumed. Including these two additional lines in National GHG Inventories would more accurately measure national emissions and would incentivize parties in the Paris agreement to increase net grain exports, thereby enhancing food security while reducing the market incentives for carbon intensive land use conversion in non-compliant countries. Applying this more comprehensive Net Ecosystem Exchange (NEE) accounting system to Canadian agricultural emissions over the past decade, demonstrates the simplicity of accounting, the magnitude of impacts, and the profound impact on policy incentives.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".