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.
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.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".