Climate Futures are Political Futures: Integrating Political Development Into the Shared Socioeconomic Pathways (SSPs)
Bibliographic record
Abstract
Abstract The Shared Socioeconomic Pathways (SSPs) are the key scenarios used by the climate change research community for evaluating mitigation pathways and the costs and challenges of meeting the Paris goals as well as climate risks along these different pathways. Despite ample evidence that political factors – such as institutional strength, rule of law-based accountability, and violent conflict – are critical determinants of climate action and vulnerability to climate hazards, the SSPs currently acknowledge but do not include quantified political factors systematically. We argue that without integrating political development into socioeconomic scenarios for climate mitigation and adaptation, projections are unlikely to reflect the challenges from climate change nor provide serious guidance on the political barriers to climate action. Consequently, models under-estimate climate risks. It is of immediate concern to extend the SSPs by integrating relevant political factors. In this paper, we examine how political development co-evolves with and influences climate futures, covering a wide range of issues from institutions to armed conflict. We showcase existing quantified political factors and the state of the art of the research on political futures, which may inform current SSP update processes. By outlining a research agenda to explore opportunities to integrate the co-evolution of political factors with socioeconomic, technical, and environmental developments as integral part of scenarios, we aim to contribute to the building blocks for a new generation of climate scenarios.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".