SSELF: A Specific SEmiautomated Lifecycle Footprinting framework to go beyond generic data in LCA
Bibliographic record
Abstract
Advancements in life cycle assessment (LCA) and environmentally extended input-output analysis enable quick generic estimations of the environmental footprint of almost any product and service. However, going beyond a generic estimate to an assessment based on actual, specific supply chain data remains costly and impracticable and demands significant sharing of proprietary data between supply chain actors and the LCA practitioner. Achieving widespread specificity in LCA requires fundamentally changing the way inventory and emission data are collected, stored, and exchanged. This research develops the SSELF (Specific SEmiautomated Lifecycle Footprinting) framework to go beyond generic data in LCA in a way that can scale up, while safeguarding sensitive data. A key feature of the framework is decentralizing inventory collection and footprint calculations. Thus, production functions remain private and upstream impacts are calculated using an iterative approach with a database of unique product identifiers and the footprints reported by other users, capturing changes in the footprints of suppliers. Although this substantially reduces the effort of footprint assessments, implementing the framework in practice presents new challenges, which are identified and discussed in this paper along with recommendations on how they can be addressed and their implications. This work provides important insight into how to get to a point where every product and service has its unique footprint. Broad access to footprints with more specificity is necessary to help consumers reduce their consumption-based impacts and make companies take accountability for, and reduce, their indirect impacts. Supplementary Information: The online version of this article (doi:10.1111/jiec.70056) contains supplementary material, which is available to authorized users.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".