Can transportation network companies improve the sustainability of urban transportation?
Bibliographic record
Abstract
• The effects of TNCs promotion on urban mobility and GHG emissions were dynamically investigated. • TNCs promotion in areas with low car ownership and strong car travel demand may increase GHG emissions. • Raising prices and limiting fleet size significantly mitigated the adverse effects. • Adopting BEVs can only reduce GHG emissions during the transition to electrification. • Heterogeneity based on travel distance and demographic characteristics was examined. This study investigates the effects of transportation network companies (TNCs) on user travel behavior and greenhouse gas (GHG) emissions in Beijing. The results show that TNCs in areas with low car ownership and strong car travel demand may increase GHG emissions. Raising prices and limiting vehicle supply can significantly mitigate the adverse effects by greening modal shift patterns and reducing TNC service use. TNCs, in the initial phase under laissez-faire policy, increased GHG emissions by 3.58 kg of CO 2 -eq per user per month, while in the mature development stage under strict regulatory policies, this value dropped to 1.06 kg of CO 2 -eq. In addition, significant heterogeneity was found among users with different demographic characteristics. It is worth noting that under the current modal shift patterns, adopting battery electric vehicles can improve the sustainability of TNCs, but it can only reduce GHG emissions in the transitory stage.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".