How does personalized feedback on carbon emissions impact intended climate action?
Bibliographic record
Abstract
Climate change is driven in part by the lifestyle choices individuals make every day, and yet the emissions associated with these choices are difficult for people to conceptualize and seldom considered in daily decision making. Here we examine the impact of personalized feedback on carbon emissions on intended climate action. In a pre-registered between-subjects experiment (N=790), participants first reported their past consumption behaviors in 2019 in domains of food, transportation, housing, and material purchases by using a personal carbon calculator. In the feedback condition, participants received information on their total carbon emissions, a breakdown by consumption domain, a 50% reduction target, and personalized recommendations for reduction. Participants in the control condition did not receive any information. Afterward, all participants indicated their future consumption intentions in 2023. We found that participants in the feedback condition showed a significant emission reduction of 1.42 tCO2e (-12.60%) per capita from 2019 to 2023, whereas those in the control condition increased their emissions by 0.05 tCO2e (+0.045%) per capita. This reduction was found in domains of food, transportation, and material purchases. Importantly, there was no difference in intentions to engage in civic climate action between conditions. Civic climate intentions were instead associated with eco-guilt, climate concern, and climate worry experienced by participants regardless of feedback. These findings suggest personalized feedback on carbon emissions has a great potential to reduce individual carbon footprint without impacting intentions to engage in civic climate action. The study provides useful implications for designing 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 | 0.004 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".