The North American Greenhouse Gas Budget: Emissions, Removals, and Integration for CO2, CH4, and N2O (2010-2019): Results From the Second REgional Carbon Cycle Assessment and Processes Study (RECCAP2)
Bibliographic record
Abstract
Accurate accounting of greenhouse-gas (GHG) emissions and removals is central to tracking progress toward climate mitigation and for monitoring potential climate-change feedbacks. GHG budgeting and reporting can follow either the Intergovernmental Panel on Climate Change methodologies for National Greenhouse Gas Inventory (NGHGI) reporting or use atmospheric-based "top-down" (TD) inversions or process-based "bottom-up" (BU) approaches. To help understand and reconcile these approaches, the Second REgional Carbon Cycle Assessment and Processes study (RECCAP2) was established to quantify GHG emissions and removals for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), for ten-land and five-ocean regions for 2010-2019. Here, we present the results for the North American land region (Canada, the United States, Mexico, Central America and the Caribbean). For 2010-2019, the NGHGI reported total net-GHG emissions of 7,270 TgCO(2)-eq yr(-1) compared to TD estimates of 6,132 +/- 1,846 TgCO(2)-eq yr(-1) and BU estimates of 9,060 +/- 898 TgCO(2)-eq yr(-1). Reconciling differences between the NGHGI, TD and BU approaches depended on (a) accounting for lateral fluxes of CO2 along the land-ocean-aquatic continuum (LOAC) and trade, (b) correcting land-use CO2 emissions for the loss-of-additional-sink capacity (LASC), (c) avoiding double counting of inland water CH4 emissions, and (d) adjusting area estimates to match the NGHGI definition of the managed-land proxy. Uncertainties remain from inland-water CO2 evasion, the conversion of nitrogen fertilizers to N2O, and from less-frequent NGHGI reporting from non-Annex-1 countries. The RECCAP2 framework plays a key role in reconciling independent GHG-reporting methodologies to support policy commitments while providing insights into biogeochemical processes and responses to climate change.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".