Impacts of global climate change mitigation scenarios on forests and harvesting in Sweden
Bibliographic record
Abstract
Under climate change, the importance of biomass resources is likely to increase and new approaches are needed to analyze future material and energy use of biomass globally and locally. Using Sweden as an example, we present an approach that combines global and national land-use and forest models to analyze impacts of climate change mitigation ambitions on forest management and harvesting in a specific country. National forest impact analyses in Sweden have traditionally focused on supply potential with little reference to international market developments. In this study, we use the global greenhouse gas concentration scenarios from the Intergovernmental Panel for Climate Change to estimate global biomass demand and assess potential implications on harvesting and biodiversity in Sweden. The results show that the short-term demand for wood is close to the full harvesting potential in Sweden in all scenarios. Under high bioenergy demand, harvest levels are projected to stay high over a longer time and particularly impact the harvest levels of pulpwood. The area of old forest in the managed landscape may decrease. This study highlights the importance of global scenarios when discussing national-level analysis and pinpoints trade-offs that policy making in Sweden may need to tackle in the near future.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".