Transitioning to Green Urban Logistics: The Role of Passenger Preferences and The Government Policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Urban logistics plays a significant role in daily life across the globe. People rely on transportation for various activities such as commuting to work, shopping, and leisure, which collectively contribute to a substantial amount of emissions. Public transportation is a key solution to address this issue; however, alternative options, such as ride-sourcing services offer passengers a more convenient and faster experience. This study examines how the decision between buses and ride-sourcing services (e.g., Uber) influences urban transit departments’ strategies for adopting green alternatives. To analyze this scenario, an evolutionary game theory approach is employed. The findings emphasize the significance of government support in facilitating this transition. By providing financial assistance to both passengers and the transit department, governments can encourage environmentally friendly activities. Furthermore, it has been observed that cost is a substantial barrier to passenger participation in this transportation mode, surpassing environmental awareness. To effectively encourage public transit usage, governments should implement strategies to reduce passenger costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 it