A diversity of sustainable lifestyles in 2050: Future SLIM scenario narratives for deep climate change mitigation
Bibliographic record
Abstract
Sustainable lifestyle changes can play a critical role in climate change mitigation. This paper presents and discusses a set of four comprehensive lifestyle scenario narratives, collectively named Sustainable Living in Models, or SLIM, scenario narratives. These narratives describe plausible alternative long-term pathways in which lifestyle changes play a major role in achieving many sustainability goals. As narratives, they are designed to both support strategic dialogue and form the basis for model-based scenario analysis. The four SLIM scenario narratives emerged from multidisciplinary workshops with lifestyle change experts, scenario analysts and integrated assessment modellers. The narratives diverge along two critical uncertainties: focus on individual versus communal values and the level of access to centralised versus distributed support for the transition to sustainable lifestyles. These SLIM scenario narratives enable explorations of our underlying assumptions of lifestyle changes while also determining the robustness of plausible developments and strategies. The SLIM scenario narratives emphasise the role of society, enablers, lifestyles and behaviours in systems change. We also describe the SLIM scenario narratives in terms of contrasting characteristics. The SLIM scenario narratives provide a theoretical contribution by supporting a greater understanding of the role of sustainable lifestyles in climate change mitigation while also providing less-stylised assumptions for model-based scenarios. The enduring impact of this scenario development process is to enable continued exchange among an emerging community of practice of modellers and sustainable lifestyle practitioners. Most notably, the narratives can allow for strategic discussion and climate action by policymakers.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".