Potentials and costs of climate change mitigation in the Norwegian forest sector — Does choice of policy matter?
Bibliographic record
Abstract
Forests are important contributors to the global carbon cycle and mitigate climate change through carbon sequestration and the supply of wood that substitutes for fossil fuels and greenhouse gas (GHG)-intensive building materials. However, current climate policies only partially credit forest carbon sequestration and bioenergy policies are handled independently of forestry. Using Norway as a case study, we analyze two sets of simulated carbon tax/subsidy policies, one crediting forest carbon sequestration while maintaining predetermined harvest levels and utilization of wood, and another targeting GHG fluxes in the entire forest industrial sector allowing harvest levels and wood markets to change in response to the policy. Results indicate that GHG emission reduction potentials differ substantially between the two policies, being several times higher for the latter than the former policy at a given carbon price. This suggests that (i) previous research efforts in Europe have not captured the full mitigation potential as they have not included adaptations in the harvest level and the wood market and (ii) climate policies should target GHG fluxes in the entire sector to utilize its potential contribution for mitigating 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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".