Integration of Environmental Costs in Ontario’s Pavement Management Systems
Bibliographic record
Abstract
This study aims to quantify the health and environmental damages of emissions released by pavement management activities in Ontario. The construction, maintenance, and rehabilitation of pavement results in greenhouse gases and pollutants which have significant impacts on human health and the environment. Traditional lifecycle costing methods used in pavement management systems do not account for the cost of these impacts. Marginal damages which relate atmospheric releases to economic cost can be applied by decision-makers to understand the damages of activities (such as pavement management) but require careful consideration of underlying factors. Marginal damages from various methods across the literature were adjusted for application in this study. The present work quantified environmental costs for the construction and lifecycle maintenance of five pavement design alternatives based on emissions of carbon dioxide and four air pollutants. Concrete roads were found to have the highest environmental costs (equivalent to 77% of agency costs) whereas asphalt roads rehabilitated with Cold-in-Place recycling had the lowest environmental costs due to the reduction in raw materials used. For the asphalt road alternatives, environmental costs were equivalent to 35% of agency costs. Future work will address limitations in data availability and additional design types. These findings provide insight for further integration of externalities in pavement management systems including of noise, user costs, and use phase emissions.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".