Automated Extraction and Analysis of Greenhouse Gas Emissions in Canadian Construction Project Procurement
Bibliographic record
Abstract
The construction industry accounts for 37% of global emissions in 2022, mainly from the production and transportation of materials, making it a critical contributor to greenhouse gas (GHG) emissions.Green Supply Chain Management (GSCM) offers principles to reduce emissions and improve sustainability across the supply chain.Yet, the industry faces barriers in adopting these principles due to the lack of accessible, user-friendly, and standardized tools that can effectively integrate and visualize sustainability indicators for procurement.Existing tools have limitations such as narrowed functionalities, lack of comprehensive data, and limited accessibility.This paper aims to facilitate GSCM by introducing the Green Supplier Selection Tool (GSST), a web-based application incorporated with AI automatic data extraction and analysis.The GSST allows users to identify suppliers based on certification (ISO 14001, BCorp, and LEED), calculate and compare Embodied Carbon Factor (ECF) across different materials and transportation modes, and automatically extract and summarize key emission data from Environmental Product Declaration (EPD) files.Moreover, the tool includes an interactive map that allows users to compare suppliers based on transportation modes, material quantity, and suppliers' emission levels while comparing emission thresholds.Initial results demonstrate the application's potential in assisting stakeholders in a deeper understanding of the sustainability implications of procurement choices through interactive, dynamic and integrated visualizations.The tool aims to raise awareness among stakeholders on procurement-related environmental impact.Further, it contributes to the reduction of the carbon footprint in the construction industry and supports global climate goals.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".