Social dynamics can delay or prevent climate tipping points by speeding the adoption of climate change mitigation
Bibliographic record
Abstract
Social dynamics are increasingly integrated into climate change models, yet tipping points in coupled social-climate systems remain understudied. This is concerning given that processes like permafrost thaw and forest dieback can trigger positive feedback loops that accelerate carbon release and global warming. We have developed a coupled model combining an Earth system component with a social behaviour model, each including tipping mechanisms. The climate model includes an additional carbon release term representing tipping reservoirs. The social model captures opinion dynamics shaped by social learning rates, mitigation costs and the strength of social norms. Our results show that weak social norms have little effect on tipping dynamics. However, faster social learning can delay or even prevent climate tipping by accelerating mitigation responses. Simulations reveal nonlinear, regime-dependent dynamics, with bifurcation-like behaviour separating tipping and no-tipping pathways. In some scenarios, a climate tipping point can trigger secondary tipping in social behaviour. Crucially, the model enables estimation of the time to tipping, offering a forward-looking risk measure. These findings underscore the importance of incorporating social feedbacks in climate models to better anticipate, and potentially avoid, catastrophic tipping events.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".