Motorized Two-Wheeled Vehicle Emissions in India: Behavioral and Institutional Issues
Bibliographic record
Abstract
Motor vehicle activity is growing rapidly in Indian cities, as in other Asian cities, with serious impacts, including deteriorating urban air quality. Motorized two-wheeled (M2W) vehicles, which provide affordable mobility to millions, form the bulk of the motor vehicle fleet and contribute significantly to transport emissions. Vehicle and fuel technologies are important and have been vastly improved since the 1990s. However, on the basis of an in-depth survey of vehicle users and an analysis of emerging trends in consumer preferences, policies, and industry plans, this paper demonstrates various important ways in which user preferences and choices relating to vehicle purchase, operation, and maintenance, interacting with institutional and technological factors, contribute to emissions and affect policy implementation, particularly with reference to M2W vehicles in India. The paper highlights the importance of considering the interaction of these factors, and how users and other actors are affected by and respond to policies, in more effectively addressing emissions from M2W vehicles and other vehicles, especially given in-use realities and constraints.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".