Exploring the use of adaptation tipping points: A systematic review of definitions, characteristics and applications
Bibliographic record
Abstract
Adaptation Tipping Points (ATPs) are critical in adaptation pathway planning, marking thresholds where existing strategies fail, and requiring new approaches. Despite their importance, ATPs lack consistent definitions, leading to conceptual fragmentation. Following the lack of a systematic approach, existing research provides limited insights into how ATPs align with broader development trajectories, making their integration into adaptation strategies challenging. Addressing this gap requires a comprehensive characterization that clarifies how ATPs have been applied in scientific literature, ensuring consistency while allowing for context-specific adaptations. To this end, we systematically analyse and consolidate existing research on ATP definitions, methods, and outputs, laying the groundwork for a standardized approach to ATPs in climate change adaptation planning. We identify four primary ATP categories (policy-based, acceptance-based, transition-based, and opportunity-based), each representing distinct thresholds shaped by governance, societal acceptance, systemic transitions, and strategic opportunities. These categories are characterized by differences in system dynamics, triggering events, consequences, adaptation drivers, and goals, defining distinct trajectories and objectives for each category in adaptation pathway planning. Furthermore, we show that the application of different ATP categories is independent of specific contexts, methods or outcomes, highlighting the flexibility of different categories across climate change adaptation planning processes. This study underscores the potential of ATPs to enhance cross-sectoral integration and facilitate the development of adaptation pathways. Ultimately, a more coherent application of ATPs can strengthen adaptation pathway planning by enabling timely interventions, accounting for systemic interdependencies, and aligning adaptation measures with broader sustainability goals in an integrated, system-wide framework.
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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 | 0.072 | 0.243 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".