Towards Sustainable Earth-Moving Machinery: A Study on Emission-Free Drivetrain Applications
Bibliographic record
Abstract
Mitigating climate change by reducing carbon emissions represents one of the most significant challenges across all industries.The earth-moving machinery industry has thus far received negligible attention from research endeavours in this particular field.Most earth-moving machinery is currently still powered by fossil diesel, and it remains to be seen which sustainable drivetrain concept will emerge as a viable alternative.The objective of this study is hence to give an overview of emission-free drivetrain concepts for earth-moving machinery in different application scenarios and infrastructural framework conditions.A market analysis of five major earthmoving machine manufacturers focusing on current drivetrain concepts is conducted.The results indicate that currently only one in 20 products offered in Europe is equipped with a sustainable drivetrain, while the overall market share of emission-free vehicles remains at approximately one percent.Results include that for existing vehicles, HVO100 and e-fuels can already be used and represent promising alternatives.For new machinery, in addition to electrification through batteryelectric or cable-connected drivetrains, hydrogen combustion engines exhibit high potential, particularly in addressing the lack of electrical infrastructure on construction sites and the high-performance requirements of the machines.In sum, a diversification of drivetrain concepts in the earth-moving machinery sector will most likely occur, presenting challenges for both manufacturers and contractors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".