The complex relationship between carbon literacy and pro-environmental actions among engineering students
Bibliographic record
Abstract
Lifestyle choices and consumption play a large role in contributing to per capita greenhouse gas emissions. Certain activities, like fossil fuel ground transportation, long-haul flights, diets with animal products and residential heating and cooling contribute significantly to per capita emissions. There is uncertainty around whether literacy about these actions encourages individuals to act pro-environmentally to reduce personal carbon footprints or to prioritize the most effective actions. This study investigated the relationship between carbon literacy and pro-environmental actions performed to reduce greenhouse gas emissions among undergraduate engineering students at the University of Toronto. The pro-environmental actions by the participants produced an average carbon footprint of 4.8 tCO 2 (within the subset of actions included in the survey) which was lower than average for residents each of Toronto, Ontario, and Canada overall but still higher than the global target of ∼2.8 tCO 2 e. The carbon literacy by participants was best for high impact actions like ground transportation and dietary choices but less so for air travel and there was mixed awareness for the moderate and low impact actions. For high impact actions and many moderate and low impact actions, participants who thought the action was high impact (even if incorrect) had lower carbon footprints related to the associated activity than those who thought the action was moderate or low impact. The overall relationship between pro-environmental action and carbon literacy was weak. It showed that for high impact actions, there is a slight negative correlation between carbon literacy and personal carbon footprint whereas for moderate and low impact actions, there is a positive correlation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".