Smart urban mobility for mitigating carbon emissions, reducing health impacts and avoiding environmental damage costs
Bibliographic record
Abstract
Significant global investments are being made into smart urban mobility technologies but there is limited evidence of the potential co-benefits for reducing carbon emissions, environmental pollutants and human health impacts at scale and over the long-term. We use conservative estimates of vehicle electrification and grid decarbonisation to focus specifically on information and communication technology (ICT) interventions. In doing so, we develop a smart mobility framework focusing on more efficient road networks and driving behaviour enabled by rapid ICT deployment. Our scenarios suggest that a combination of ambitious policy measures aimed at smoothing traffic speeds as well as improving driver behaviour in urban areas could reduce carbon emissions for cars ∼29% saving ∼7 MtCO 2 and for vans ∼33% saving ∼3 MtCO 2 by 2050. Potential reductions in nitrogen oxides and particulate matter 2.5 for cars are ∼22% and vans ∼10% and ∼16% respectively. We use human toxicological classification of air pollution (HCA) to assess the potential damage on human health and our scenarios suggest an upper range of ∼23% and ∼30% reductions in HCA by 2050 for cars and vans respectively. Using conservative cost values, we estimate damage costs could be avoided from car emissions range from ∼£42–£130 million and vans ∼£89–£163 million per year. However, our baseline projections indicate that emissions and damage costs avoided from passenger cars could be partially offset by growing demand for urban van delivery and freight services that are currently outpacing improved fuel and emissions performance of the vehicle stock. This may reflect broader lifestyle and consumer trends towards on-line shopping, food and delivery services, which warrants further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".