Introducing behavioral change in transportation into energy/economy/environment models
Bibliographic record
Abstract
Transportation is vital to economic and social development, but at the same time generates undesired consequences on local, regional, and global scales. One of the largest challenges is the mitigation of energy-related carbon dioxide emissions, to which this sector already contributes one-quarter globally and one-third in the United States. Technology measures are the prerequisite for drastically mitigating energy use and all emission species, but they are not sufficient. The resulting need for complementing technology measures with behavioral change policies contrasts sharply with the analyses carried out by virtually all energy / economy / environment (E3) models, given their focus on pure technology-based solutions. This paper addresses the challenges for E3 models to simulate behavioral changes in transportation. A survey of 13 major models concludes that especially hybrid energy models would already be capable of simulating some behavioral change policies, most notably the imposition of the full marginal societal costs of transportation. Another survey of major macroscopic transportation models finds that key specifications required for simulating behavioral change have already been implemented and tested, albeit not necessarily on a global scale. When integrating these key features into E3 models, a wide range of technology and behavioral change policies could be analyzed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".