Evaluation of climate change mitigation strategies for Irish forests using the CBM-CFS3 model
Bibliographic record
Abstract
BACKGROUND: The Irish Forestry greenhouse gas (GHG) profile is undergoing a transition from a net sink to net emission because of persisting emissions from organic soils, an increase in harvest and shifts in the age class structure of plantation forests. The forestry GHG trend diverges from the required National and European Union (EU) policy pathway for land use land use change and forestry (LULUCF) and agriculture aimed at halving emissions by 2030 and achieving carbon neutrality by 2050. A recalibrated version of the Carbon Budget Model of the Canadian Forest Service (CBM-CFS3) was used to assess the impact of identified national forest policy measures on the forest GHG profile over the short to long term. RESULTS: An analysis of projected scenarios revealed that, under current silvicultural practices and afforestation policies (with existing measures-WEMs), Irish forests will continue to be a long-term emission beyond 2070 unless harvest rates and management practices are adjusted to negate the adverse impact of emissions from organic soils and fluctuations in historic afforestation rates. The implementation of additional measures (WAM) suggests that the forest sink can be sustained if harvest rates exceed 75% of the net annual increment (NAI), additional afforestation targets are met and if plantation rotation age is increased. Although additional afforestation and a reduction in deforestation is required to meet long-term carbon-neutral goals, the implementation of these policies has a minimal short-term impact on the 2030 targets set out under the National Climate Change Plan (CAP 24) and the revised EU LULUCF regulation (841/2023). CONCLUSION: The results show that the extension of rotation age and associated reductions in harvest levels will have the greatest short-term impact on climate change mitigation, which can be delivered at a negative marginal abatement cost. However, even if WAM forest measures are implemented, Ireland is unlikely to meet the National and EU LULUCF targets by 2030 because of a decreasing forest sink.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".