Review of factors affecting earthworks greenhouse gas emissions and fuel use
Bibliographic record
Abstract
Research on greenhouse gas (GHG) emissions in the building sector has concentrated on the use phase of buildings, and more recently embodied emissions from construction materials. Much less research has focused on reducing GHG emissions from onsite fuel use during construction, in part because predicting and measuring fuel use is complex and lacks guidance to help modelers focus on key factors that drive variability in results. This paper addresses that challenge by examining the state-of-the-art in onsite fuel use accounting with a focus on earthworks, one of the largest drivers of onsite fuel consumption. It provides a comprehensive summary of the existing literature, describes and quantifies the ways in which factors influence fuel use, and fills several gaps identified during the review process by drawing from research in related fields. The result is a new taxonomy for categorizing fifteen factors which influence fuel use, including equipment factors (e.g., engine specifications, attachment selection), operational factors (e.g., operator skill, fleet configuration), and site factors (e.g., soil type, excavation depth). Drawing on earthwork productivity and productivity/fuel use in related fields (e.g., freight hauling, military equipment) to augment the construction emissions literature, most notably to investigate the influence of weather on earthwork GHG emissions. Across case studies, soil type, attachment selection, engine specifications, weather and hauling conditions arise as the most influential factors. Finally, this work presents recommendations for structured fuel use data collection to improve the consistency of future data collection and reporting, along with subsequent fuel use modelling and optimization efforts.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".