Methodology for material flow analysis at the organizational scale
Bibliographic record
Abstract
Organizations play a crucial role in facilitating the transition to a circular economy. Implementing circular practices often begins with a material flow analysis (MFA) to identify issues and opportunities. However, existing MFA methodologies focus mainly on territorial applications and lack effectiveness at the organizational level. Conducting MFA at this level presents distinct challenges, requiring insights into dynamic material circulation and effective handling of heterogeneous data. Addressing this gap, this study introduces an innovative methodology tailored to organizational contexts. The developed methodology consists of six steps for conducting MFA while proposing an archetype-based approach capable of analyzing extensive and disparate datasets, coupled with bootstrapping and Monte Carlo simulation techniques, that considerably reduces the complexity of MFA execution. Furthermore, this methodology enables a comprehensive understanding of material flows within the system and provides a straightforward method for estimating uncertainty in mass estimations by incorporating confidence level calculations. A case study from a governmental organization is used to illustrate the proposed methodology. • Clarifying the steps to perform Material Flow Analysis at the organizational level proposed. • The archetype approach enables to analyze a large amount of heterogeneous data. • This approach reduces processing time while maintaining a suitable level of accuracy. • Bootstrapping and Monte Carlo Simulation (MCS) are utilized to enhance this approach. • A straightforward technique for measuring uncertainty based on MCS.
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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 | 0.001 | 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 | 0.000 | 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) | 0.004 | 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".