Embodied emissions in rail infrastructure: a critical literature review
Bibliographic record
Abstract
Abstract This paper investigates the state of knowledge in quantifying the embodied greenhouse gas (GHG) emissions in rail infrastructure and develops a sketch model for estimating the GHG impact of rail infrastructure based on the literature. A literature review identified 22 publications, containing 57 case studies, at least touching on the embodied GHG for different types of rail infrastructure. The cases studies include high speed rail, intercity rail, light rail, commuter rail, heavy rail, freight, and metro rail. The paper examines the GHG impact per kilometre of rail infrastructure reported across the case studies and compares the boundaries, functional units, methods, and data used. Most studies employed process-based LCA for an attributional analysis. The embodied emissions associated with the case studies range from 0.5 to 12 700 tCO2 km−1; much of the variation is dependent on the proportion of the rail line at-grade, elevated, or in a tunnel. However, large ranges in GHG per kilometre remain after controlling for elevated and tunneled distance. Comparing the embodied emissions across the rail types was challenging, due to the large variations in system boundaries, study goals, and inventory methods adopted in the publications. This review highlights the need for standardization across the reporting of embodied GHG for rail infrastructure to better facilitate hot spot detection, engineering design and GHG policy decision making. The statistical model finds that overall ∼941(±168) tCO2e are embodied per kilometre of rail at-grade, and tunneling has 27 (±5) times more embodied GHG per kilometre than at-grade construction. The statistical model is based on the findings of published literature and does not explicitly consider function, geometry, specifications, emphasis on whole lifecycle, legislative constraints, socio-economic factors, or the physical and environmental conditions of the construction site.
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 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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".