Applying the Project Management Cost Estimating Standard to Carbon Footprinting
Bibliographic record
Abstract
Anthropogenic emissions have a significant effect on Earth’s atmosphere and contribute to changes in the global climate. These emissions and their impacts need to be tracked in order to understand their potential consequences and to be able to determine how these impacts can be eliminated or reduced by changes in methods, behaviors, and tools. A carbon footprint is the amount of carbon dioxide and other greenhouse gas emissions generated by an entity over a specific time period or lifecycle. Developing a consistent and clear approach to determining the sources and quantities of these emissions is important due to the emerging demand to account for carbon impacts. Unfortunately there are very few approaches that can accurately estimate and track carbon to determine the climate change impacts of organizations, businesses, and activities. In this paper we propose an approach to carbon footprinting in which the amount of one or more types of carbon gas emissions can be estimated. We propose that by adapting cost estimation standards to carbon footprinting practices, a standard approach can be developed, thus providing a clearer and more focused approach to carbon footprinting. In this study, we have adapted the cost estimation standard of the Project Management Body of Knowledge (PMBOK). This adaptation results in a new methodology for carbon footprint quantification that provides more clarification and robustness to carbon footprinting processes. By breaking down the whole process into three key steps, i.e., inputs, tools and techniques, and outputs, and by introducing relevant steps to take, the methodology can function as a guideline for carbon footprinting studies.
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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 | 0.032 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".