Measuring what works: quantifying greenhouse gas emission reductions of behavioural interventions to reduce driving, meat consumption, and household energy use
Bibliographic record
Abstract
Interventions that promote pro-environmental behaviours are increasingly necessary in reducing use of high-emissions goods and services to meet international climate change targets. Here we assess the greenhouse gas emissions reductions associated with behavioural interventions in three high-emitting domains (personal vehicle use, meat consumption, and household energy use) based on an analysis of the peer-reviewed literature. We examined 40 studies from 1991–2018 involving 886 576 subjects. We found that some of the most robust interventions shown to reduce emissions were financial incentives for personal vehicles, defaults for reduced meat consumption, and feedback for home energy use. We estimate mean annual reductions of 571 kgCO 2 e per vehicle driver for reduced vehicle use, 51 kgCO 2 e per individual for reduced meat consumption, and 149 kgCO 2 e per household for reduced energy use. Despite substantial attention to behavioural interventions in the literature, we find that few studies are suitable for quantifying emissions reductions ( N = 6 for diet, N = 5 for personal vehicles) and few ( N = 3) are conducted outside OECD countries. Due to this imbalance in the literature, we focus our findings on western economies. We find substantial variation in the emission reductions achieved with different interventions within each domain; interventions in diet ranged from reductions of 231 kgCO 2 e to increases of 116 kgCO 2 e per person per year (both statistically significant). Further, emissions reductions are sensitive to external factors, such as the emissions intensity of the electrical grid, which may change over time. Key gaps in the literature include a lack of studies conducted using randomized controlled trials or follow-ups, and in high-impact areas for emissions reductions including air travel. We highlight promising areas of intervention, such as habit changes to promote mode shifts in personal transportation, which would benefit from an analysis of greenhouse gas emissions reductions in future research.
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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 | 0.037 | 0.121 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".