The Empirical Research on the Estimation of the Construction CO<sub>2</sub> Emission Based on EPA Non-Road Modeling
Bibliographic record
Abstract
The green and sustainable construction has become a popular topic, especially with the respect of the green house gas emissions. Meanwhile, the estimation of the CO 2 emission during the construction is an important process among the CO 2 emission management. The paper carries out the empirical research on the construction CO 2 emission based on the EPA non-road modeling and a real case. Firstly, the research methodology is developed based on the EPA non-road modeling. Then, based on a real case, the total CO 2 emission of the project and the CO 2 emission of each machine are calculated. Finally, other scenarios are considered and compared, and the optimal solution is found. The proposed methodology provides an accurate, consistent & realistic quantification method of CO 2 emissions at micro level during construction, which will be of benefit to the green and sustainable construction.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".