Environmental life-cycle impacts of bitumen: Systematic review and new Canadian models
Bibliographic record
Abstract
• Bitumen LCAs globally lack consistency, high-quality data, and sometimes transparency. • Their GWP are likely underestimated: new Canadian models range 826–1098 kgCO 2 eq/t. • Crude oil is a major contributor, fugitive emissions may raise the GWP to 2680 kgCO 2 eq/t. • Transport impacts vary highly (18–290 kgCO 2 eq/t in Canada): models must be tailored. • Robustly recommend LCA-based pavement green practices requires high-quality bitumen LCIs. Bitumen − or asphalt binder − is a major contributor to pavement environmental impacts. Nevertheless, the literature only counts scarce asphalt binder LCAs, with highly variable results. To better understand bitumen environmental impacts, we review LCAs published before 2024. Then, we build bitumen LCA models for different Canadian markets, using TRACI 2.1 and ecoinvent v3.6. The carbon footprint of Canadian asphalt binders ranges within [826–1098] kgCO 2 eq/t (potentially up to 2680 kgCO 2 eq/t when including fugitive emissions). Crude oil extraction is the main contributor to most life cycle environmental impact categories, but likely still underestimated. Transportation impacts can vary highly ([18–291] kgCO 2 eq/t in Canada). Models for these two hotspots must be tailored. Finally, we critically compare the carbon footprints of all published virgin asphalt binders LCAs: previous carbon footprints range within [143–637] kgCO 2 eq/t and are very likely underestimated. Previous pavement LCA results must be questioned, and higher-quality LCIs urgently developed to produce robust regionalized LCA-based recommendations on pavement green practices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".