Quantifying Environmental Costs for Sustainable Pavement Management
Bibliographic record
Abstract
We quantify the effect of Ontario's provincial transportation infrastructure decisions on multipollutant exposures, impacts, and life-cycle costs. A variety of evidence shows that roadway construction and maintenance affect human health and climate change via emissions of air pollutants and greenhouse gases. With recent policy shifts, provincial transportation decision-makers are focused on the role of roadway design and maintenance on these environmental exposures; however, they lack appropriate tools to incorporate them into decisions. Here, we present a decision-making tool that quantifies the health, environmental, and economic impacts of construction, maintenance, and rehabilitation of roads and highways. We examine various pavement design and management approaches, including standard practices, and innovations to processes and materials. We estimate multipollutant emissions, including (CO2, NOx, SO2, PM2.5, CO). We review literature that connects these exposures to health and economic impacts directly through marginal damage estimates. Current literature estimates for the cost of emitting a single metric tonne of fine particulate matter range between $600 and $50,000 (2010 CAD) depending on the impacts considered and the cost measures used. Preliminary findings for environmental costs of emissions from new-construction of a double-lane, one-kilometre road range between $300 to $50,00 for asphalt roads and $3000 to $400,00 (2010 CAD) for concrete roads. In this research, we expand on these findings and quantify contributions of uncertainty from exposures, exposure-response, and economic impacts. These findings allow infrastructure managers to account for health-related impacts of environmental exposures, including air pollutants and greenhouse gases, and thus to design more sustainable solutions.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".