Transport emissions and climate change: Which actions are the hardest?
Bibliographic record
Abstract
• This study investigated the general difficulty of climate change behaviors. • Living vehicle-free was the hardest climate change behavior for Canadians. • Among transport-based actions, using a plug-in hybrid car is the simplest. • There is a high correlation between transport-based behaviors with CC-SoC. Climate change is a global challenge, making this a crucial time for altering human behaviors to mitigate its effects. This study investigates the difficulty or ease of different climate change-related behaviors, particularly those associated with transportation. To this end, the Rasch model is employed. This paper also intends to examine the link between those behaviors and a robust measure to evaluate individuals’ environmental behaviors and attitudes, called the Climate Change Stage of Change (CC-SoC). In this regard, a machine learning method ranks various climate change-related behaviors according to their influence on CC-SoC. The findings indicate that transport-based actions are generally among the most challenging to change, with living without a vehicle being the most difficult. Avoiding long-haul flights, using an electric vehicle, and riding an electric-assist bicycle were within the top five determinants of CC-SoC, indicating the strong influence of transport-related behaviors on climate change. The findings of this study are critical for informing transport policy, since they help identify which behavioral shifts are most impactful yet most resistant to change, allowing for more targeted and effective interventions.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".