How does personalized feedback on carbon emissions impact intended climate action?
Bibliographic record
Abstract
Abstract Carbon emissions associated with individual choices are difficult to conceptualize and seldom considered in daily decision making. We examine the impact of personalized feedback on carbon emissions on intended climate action. In a pre-registered experiment (N=790), participants reported their consumption in 2019 and received personalized feedback on carbon emissions and recommendations for reduction in the feedback condition, or no feedback in the control condition. Subsequently, all participants indicated their consumption intentions in 2023, which was one year from the year the experiment was conducted. After receiving feedback, participants showed a significant emission reduction of 1.42tCO2e (-12.60%) per capita between 2019 and 2023, whereas those in the control condition increased their emissions by 0.05tCO2e (+0.045%). Importantly, there was no impact on intentions to engage in civic climate action. Civic climate intentions were instead associated with eco-guilt and climate concerns. These findings suggest personalized feedback has the potential to reduce carbon emissions without impacting civic climate intentions, providing implications for strategies to encourage climate action.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".