Air Quality and Health Impacts of Freight Modal Shifts: Review and Assessment
Bibliographic record
Abstract
Freight movement is a significant and growing contributor to transportation emissions globally. Modal shifts in freight, that is, moving freight from a higher emission mode to one associated with lower emissions, are discussed as a strategy to reduce emissions of criteria pollutants and greenhouse gases (GHGs). However, there is limited knowledge of the magnitude of potential benefits and their impacts on human health. The overall goal of this study is to identify and characterize the potential of modal shifts in freight transport for mitigating air pollutant emissions, air pollutant concentrations, population exposure to air pollutants, and health impacts. The analysis was conducted in the Canadian context, with a focus on land-based freight such as trucks, trains, and pipelines, as well as marine shipping for inland and coastal waters. A structured review of the existing literature database, and a critical assessment of the findings was conducted, using a weight-of-evidence approach. The assessment took into consideration potential local and regional variables for Canada. The results indicated that there is limited evidence that road-to-rail, road-to-marine, and rail-to-marine modal shifts could reduce pollutant and GHG emissions. There was insufficient evidence on modal shifts involving the pipeline mode, and on the air quality, population exposure, and health impacts related to any modal shift. Several research gaps remain, which must be addressed establish the emissions, air quality, and health impacts of freight modal shifts.
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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 | 0.007 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".