Hybrid Vehicles as a Transition for Full E-Mobility Achievement in Positive Energy Districts: A Comparative Assessment of Real-Driving Emissions
Bibliographic record
Abstract
Air pollution is a major concern, particularly in developing countries. Road transport and mobile sources are considered the root causes of air pollutants. With the implementation of zero-carbon and zero-energy concepts at the district scale, cities can make great strides towards sustainable development. Urban planning schemes are moving from mere building solutions to the larger positive energy district (PED) scale. Alongside other technology systems in PEDs, increased uptake of electro-mobility solutions can play an important role in CO2 mitigation at the district level. This paper aims to quantify the exhaust emissions of six conventional and two fully hybrid vehicles using a portable emission measurement system (PEMS) in real driving conditions. The fuel consumption and exhaust pollutants of the conventional and hybrid vehicles were compared in four different urban and highway driving routes during autumn 2019 in Iran. The results showed that hybrid vehicles presented lower fuel consumption and produced relatively lower exhaust emissions. The conventional group’s fuel consumption (CO2 emissions) was 11%, 41% higher than that of the hybrids. In addition, the hybrid vehicles showed much better fuel economy in urban routes, which is beneficial for PEDs. Micro-trip analysis showed that although conventional vehicles emitted more CO2 at lower speeds, the hybrids showed a lower amount of CO2. Moreover, in conventional vehicles, NOx emissions showed an increasing trend with vehicle speed, while no decisive trend was found for NOx emissions versus vehicle speed in hybrid vehicles.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".