Current lifestyles in the context of future climate targets: analysis of long-term scenarios and consumer segments for residential and transport
Bibliographic record
Abstract
Abstract The carbon emissions of individuals strongly depend on their lifestyle, both between and within regions. Therefore, lifestyle changes could have a significant potential for climate change mitigation. This potential is not fully explored in long-term scenarios, as the representation of behaviour change and consumer heterogeneity in these scenarios is limited. We explore the impact and feasibility of lifestyle and behaviour changes in achieving climate targets by analysing current per-capita emissions of transport and residential sectors for different regions and consumer segments within one of the regions, namely Japan. We compare these static snapshots to changes in per-capita emissions from consumption and technology changes in long-term mitigation scenarios. The analysis shows less need for reliance on technological solutions if consumption patterns become more sustainable. Furthermore, a large share of Japanese consumers is characterised by consumption patterns consistent with those in scenarios that achieve ambitious climate targets, especially regarding transport. The varied lifestyles highlight the importance of representing consumer heterogeneity in models and further analyses.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".