Co‐benefits of and trade‐offs between natural climate solutions and Sustainable Development Goals
Bibliographic record
Abstract
Combating climate change and achieving the UN Sustainable Development Goals (SDGs) are two important challenges facing humanity. Natural climate solutions (NCSs) can contribute to the achievement of these two commitments but can also generate conflicting trade‐offs. Here, we reviewed the literature and drew on expert knowledge to assess the co‐benefits of and trade‐offs between 150 SDG targets and NCSs within 12 selected ecosystems. We demonstrate that terrestrial, coastal, and marine NCSs enable the attainment of different sets of SDG targets, with low redundancy. Implementing NCSs in various ecosystems would therefore maximize achievement of SDG targets but would also induce trade‐offs, particularly if best practices are not followed. Reliance on NCSs at large scales will require that these trade‐offs be taken into consideration to ensure the simultaneous realization of positive climate outcomes and multiple SDG targets for diverse stakeholders.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".