High emissions or carbon neutral? Inclusion of “anthropogenic” forest sinks leads to underreporting of forestry emissions
Bibliographic record
Abstract
Recent research has shown forest-related emissions reported in national greenhouse gas inventories are much lower than global estimates from models summarized in Intergovernmental Panel on Climate Change reports. A substantial part of this discrepancy could be explained by conceptual differences in what is counted as part of the anthropogenic forest carbon sink and the way countries report on their forest harvesting sectors. With Canada as a case study, we used published National Inventory Report and Common Reporting Format tables to isolate emissions and removals directly associated with forestry from those associated with forests more broadly. Forestry-related factors that affect CO 2 emissions and removals include tree harvesting, post-harvest forest regeneration and growth, and carbon storage in long-lived harvested wood products. We found that between 2005 and 2021, forestry in Canada represented a net source of carbon (annual mean = 90.8 Mt. CO 2 e), and that total area logged was a significant predictor of net forestry emissions. In contrast, Canada’s NIR reported a small net carbon sink during the same time period (annual mean = −4.7 Mt. CO 2 e). We show this discrepancy can be explained by Canada’s GHG reporting approach that claims GHG emissions from wildfires are natural, but GHG removals from forests at the age of commercial maturity, despite being primarily natural disturbance origin, are anthropogenic. This reporting approach may lead to climate mitigation policies that are ineffectual or detrimental to reducing net carbon in the global atmosphere.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".