A method to identify positive tipping points to accelerate low-carbon transitions and actions to trigger them
Bibliographic record
Abstract
Meeting the Paris Agreement to limit global warming to "well below 2 °C" requires a radical acceleration of action, as the global economy is decarbonising at least five times too slowly. Tipping points, where low-carbon transitions become self-propelling, could be key to achieving the necessary acceleration. We deem these normatively 'positive', because they can limit considerable, inequitable harms from global warming and help achieve sustainability. Some positive tipping points, such as the UK's elimination of coal power, have already been reached at national and sectoral scales. The challenge now is to credibly identify further potential positive tipping points, and the actions that can bring them forward, whilst avoiding wishful thinking about their existence, or oversimplification of their nature, drivers, and impacts. Hence, we propose a methodology for identifying potential positive tipping points, assessing their proximity, identifying the factors that can influence them, and the actions that can trigger them. Building on relevant research, this 'identifying positive tipping points' (IPTiP) methodology aims to establish a common framework that we invite fellow researchers to help refine, and practitioners to apply. To that end, we offer suggestions for further work to improve it and make it more applicable.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".