Net carbon accounting and reporting are a barrier to understanding the mitigation value of forest protection in developed countries
Bibliographic record
Abstract
Abstract Meeting the Paris Agreement global warming target requires deep and rapid cuts in CO 2 emissions as well as removals from the atmosphere into land sinks, especially forests. While international climate policy in the land sector does now recognize forest protection as a mitigation strategy, it is not receiving sufficient attention in developed countries even though they experience emissions from deforestation as well as from logging of managed forests. Current national greenhouse gas inventories obscure the mitigation potential of forest protection through net carbon accounting between the fossil fuel and the land sectors as well as within the different categories of the land. This prevents decision-makers in national governments, the private sector and civil society having access to all the science-based evidence needed to evaluate the merits of all mitigation strategies. The consequences of net carbon accounting for global policy were investigated by examining annual inventory reports of four high forest cover developed countries (Australia, Canada, USA, and Russia). Net accounting between sectors makes a major contribution to meeting nationally determined contributions with removals in Forest Land offsetting between 14% and 38% of the fossil fuel emissions for these countries. Analysis of reports for Australia at a sub-national level revealed that the State of Tasmania delivered negative emissions due to a change in forest management—a large and rapid drop in native forest logging—resulting in a mitigation benefit of ∼22 Mt CO 2 -e yr –1 over the reported period 2011/12–2018/19. This is the kind of outcome required globally to meet the Paris Agreement temperature goal. All CO 2 emissions from, and atmospheric removals into, forest ecosystem carbon stocks now matter and should be counted and credited to achieve the deep and rapid cuts in emissions needed over the coming decades. Accounting and reporting systems therefore need to show gains and losses of carbon stocks in each reservoir. Changing forest management in naturally regenerating forests to avoid emissions from harvesting and enabling forest regrowth is an effective mitigation strategy that can rapidly reduce anthropogenic emissions from the forest sector and simultaneously increase removals of CO 2 from the atmosphere.
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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 | 0.024 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".