A scoping review of human health co-benefits of forest-based climate change mitigation in Europe
Bibliographic record
Abstract
Climate change is a pressing global challenge with profound implications for human health. Forest-based climate change mitigation strategies, such as afforestation, reforestation, and sustainable forest management, offer promising solutions to mitigate climate change and simultaneously yield substantial co-benefits for human health. The objective of this scoping review was to examine research trends related to the interdisciplinary nexus between forests as carbon sinks and human health co-benefits. We developed a conceptual framework model, supporting the inclusion of exposure pathways, such as recreational opportunities or aesthetic experiences, in the co-benefit context. We used a scoping review methodology to identify the proportion of European research on forest-based mitigation strategies that acknowledge the interconnection between mitigation strategies and human impacts. We also aimed to assess whether synergies and trade-offs between forest-based carbon sink capacity and human co-benefits has been analysed and quantified. From the initial 4,062 records retrieved, 349 reports analysed European forest management principles and factors related to climate change mitigation capacity. Of those, 97 studies acknowledged human co-benefits and 13 studies quantified the impacts on exposure pathways or health co-benefits and were included for full review. Our analysis demonstrates that there is potential for synergies related to optimising carbon sink capacity together with human co-benefits, but there is currently a lack of holistic research approaches assessing these interrelationships. We suggest enhanced interdisciplinary efforts, using for example multideterminant modelling approaches, to advance evidence and understanding of the forest and health nexus in the context of climate change mitigation.
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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 | 0.017 | 0.053 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".