Changing Technology or Behavior? The Impacts of a Behavioral Disruption
Bibliographic record
Abstract
Transportation is a key factor in the fight against climate change. Consumer behavior changes in transportation are underrepresented in energy policies, even if they could be essential to achieve the fixed GHG emission reduction targets. To help quantify the role of behaviors in energy transition and their implications on the dynamics of an energy system, this study is conducted using the North American TIMES Energy Model, adapted to Quebec (Canada). A behavioral disruption scenario (an increase in carpooling) is introduced in the model’s transportation sector and is compared to a massive electrification scenario. Our results highlight the fact that a behavioral disruption can lead to the same GHG emission reductions (65%) by 2050 as an electrification policy, while alleviating different efforts (such as additional electrical capacity and additional costs) associated with massive electrification. Moreover, the results are sensitive to behavior-related parameters, such as social discount rates and car lifetimes.
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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 | 0.001 |
| 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".