Carbon Footprint Assessment Model for Sustainable Construction Sites
Bibliographic record
Abstract
The construction industry plays a significant role in global carbon emissions, yet the focus of decarbonization efforts often centers on the operational phase of buildings and facilities.The construction phase, a critical contributor to overall emissions, has received comparatively less attention.This paper presents the development of a comprehensive model model for measuring and evaluating the carbon footprint of construction projects, addressing this gap.The proposed model leverages conversion factors from reputable standards, including the Greenhouse Gas (GHG) Protocol, to ensure accuracy and reliability.It evaluates both direct emissions, such as those produced by construction processes involving heavy machinery, and indirect emissions, such as the energy consumption of temporary site facilities and utility use.This dual-layer approach provides a holistic view of emissions during the construction stage.By integrating data from construction activities, the model identifies and quantifies the carbon emissions associated with various processes and operations.Construction managers can utilize the outputs of the model to pinpoint high-emission activities and develop targeted mitigation strategies.This allows for informed decision-making aimed at reducing the carbon footprint of construction projects.The implementation of the model not only promotes sustainable construction practices but also supports compliance with international standards and climate action goals.Through practical applications and case studies, this paper demonstrates the utility of the model in real-world scenarios, emphasizing its adaptability to different project types and scales.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".