Sustainable Drivetrain Concepts in Earth-moving Machinery: A Systematic Market and Literature Research
Bibliographic record
Abstract
Mitigating climate change and reducing carbon emissions across all industries represents one of the most significant challenges of the 21st century.The earth-moving machinery industry has been largely overlooked in research initiatives aimed at reducing greenhouse gas emissions.The majority of earth-moving machinery is currently powered by fossil diesel, and it is not yet evident which sustainable fuel will emerge as a viable alternative.The objective of this study is to identify sustainable drivetrain concepts that can be employed to operate earth-moving machinery in a variety of applications.A market analysis of five major earth-moving machine manufacturers focusing on the drivetrains of various machine categories is conducted.The results indicate that only one in 20 products offered in Europe is equipped with a sustainable drivetrain, while the current overall market share remains at approximately one percent.For existing vehicles, HVO100 and e-fuels represent promising alternatives.For new machines, in addition to electrification through battery-electric or cable-connected drivetrains, hydrogen combustion engines exhibit high potential, particularly in addressing the partial lack of electrical infrastructure on construction sites.A diversification of drivetrains in the earth-moving machinery sector will 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".