More future synergies and less trade‐offs between forest ecosystem services with natural climate solutions instead of bioeconomy solutions
Bibliographic record
Abstract
To reach the Paris Agreement, societies need to increase the global terrestrial carbon sink. There are many climate change mitigation solutions (CCMS) for forests, including increasing bioenergy, bioeconomy, and protection. Bioenergy and bioeconomy solutions use climate-smart, intensive management to generate high quantities of bioenergy and bioproducts. Protection of (semi-)natural forests is a major component of "natural climate solution" (NCS) since forests store carbon in standing biomass and soil. Furthermore, protected forests provide more habitat for biodiversity and non-wood ecosystem services (ES). We investigated the impacts of different CCMS and climate scenarios, jointly or in isolation, on future wood ES, non-wood ES, and regulating ES for a major wood provider for the international market. Specifically, we projected future ES given by three CCMS scenarios for Sweden 2020-2100. In the long term, fulfilling the increasing wood demand through bioenergy and bioeconomy solutions will decrease ES multifunctionality, but the increased stand age and wood stocks induced by rising greenhouse gas (GHG) concentrations will partially offset these negative effects. Adopting bioenergy and bioeconomy solutions will have a greater negative impact on ES supply than adopting NCS. Bioenergy or bioeconomy solutions, as well as increasing GHG emissions, will reduce synergies and increase trade-offs in ES. NCS, by contrast, increases the supply of multiple ES in synergy, even transforming current ES trade-offs into future synergies. Moreover, NCS can be considered an adaptation measure to offset negative climate change effects on the future supplies of non-wood ES. In boreal countries around the world, forestry strategies that integrate NCS more deeply are crucial to ensure a synergistic supply of multiple ES.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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".