Climate <scp>change‐triggered</scp> land degradation and planetary health: A review
Bibliographic record
Abstract
Abstract Land is a vital natural resource for human socio‐ecological wellbeing. Around the world, land is being degraded due to various natural and anthropogenic factors such as flooding, wind erosion, agriculture and human settlement, and anthropogenic climate change. While significant research has been conducted on the separate dyads of: (1) anthropogenic climate change and land degradation and (2) land degradation and health, limited consideration has been given to the cause‐and‐effect relationships between anthropogenic climate change‐triggered land degradation and planetary health consequences. Using a systematic literature review and the driving force, pressure, state, exposure, effect (DPSEE) framework, this study synthesizes the complex causal relationships of anthropogenic climate change‐triggered land degradation and its planetary health consequences. Our findings demonstrate that anthropogenic climate change has induced and accelerated natural and anthropogenic land degradation through an array of pathways, resulting in planetary health consequences that can be grouped into six categories: (1) food and nutritional insecurity, (2) communicable and noncommunicable diseases, (3) livelihood insecurity, (4) physical and mental health, (5) health hazards related to extreme weather events, and (6) migration and conflict. Interlinkages exist between these six planetary health impact categories, adding to the complexity of the causal pathways. These collective impacts are hampering the realization of the UN Sustainable Development Goals around the world. The findings of this study and our DPSEE framework can help policymakers identify and integrate actions to better manage the planetary health impacts of climate change‐induced land degradation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".