Multipollutant impacts to U.S. receptors of regional on-road freight in Ontario, Canada
Bibliographic record
Abstract
On-road freight is a significant source of air pollutant and greenhouse gas emissions. The resulting economic damages can cross borders through processes of atmospheric fate and transport, regardless of whether that freight serves local or regional demand. Understanding patterns of freight demand and atmospheric processes can thus inform inter-jurisdictional efforts to mitigate multipollutant damages. We quantify how different freight trips across 49 census divisions in the Province of Ontario, Canada create an economic burden on downwind US receptors. We apply an integrated modeling approach combining a travel demand model, a mobile emissions simulator, and marginal damages from emissions. Economic damages include the increased risk of premature death from PM2.5 related to primary PM2.5 (represented by damages from inert primary PM2.5), NOX, SO2, and NH3, and the global effects of climate change from greenhouse gases (CO2, CH4, N2O). Over 90% of the $1.4 billion (2010USD) in transboundary air pollutant damages at US receptors result from regional freight demand across Ontario in 2012. A single major freight corridor, the ON-401 expressway, contributes more than half of all damages. Most of these damages impact the states situated to the south and east of the province. Mean estimates of annual damages range from millions to tens of millions (2010USD) across major eastern metropolitan areas including New York, Boston, Philadelphia, and D.C. Most of these damages result from NOX, which constitutes 95% of inorganic PM2.5-related pollutant emissions by mass. Thus, targeting NOx from freight movements along the ON-401 expressway could avoid millions to tens of millions of damages annually in eastern US cities. These results indicate that local green freight policies may be unable to address the environmental burden at cross-border receptors. Cooperation is needed among local, provincial, and federal governments to encourage policies targeting the most harmful emissions along routes servicing regional freight demands.Implications: On-road freight movement in Ontario can yield billions of dollars in annual economic damages to US residents through its effects on air pollution and climate change. We use an integrated modeling approach combining an on-road freight travel demand, mobile emissions, and marginal damages of emissions to quantify and study these economic damages. Regional freight contributes approximately 90% of damages, with one major freight corridor, the ON-401 expressway, contributing 59%. Most damages derive from emissions of NOx and amount to millions to tens of millions of dollars in annual damages across major Eastern US cities. Thus, targeting NOx from freight movements along the ON-401 expressway could avoid millions of damages annually in eastern US cities.
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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 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".