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Evaluation criteria for AI-assisted product carbon footprinting systems: the cases of mapping and supply chain modeling
- Truvace record
- TRV-2026-2490raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Benchmark or lab evaluation
- Sources
- 1 sourcePeer-reviewed
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- a5d312b769f8…check it
Abstract Large language models (LLMs) are increasingly used to automate components of product carbon footprinting (PCF). However, the carbon footprinting literature lacks criteria for evaluating the quality, documentation, uncertainty indication, and reviewability of AI-assisted systems for specific PCF use cases. We developed a set of evaluation criteria for AI-assisted carbon footprinting (AI-CF) through standards review, structured stakeholder elicitation with corporate practitioners and verification speciali…
The problem
In a test by the developer's own staff, an AI carbon-footprint modeler had a 33% median error against 269 product declarations, and its materials mapper matched experts on only 59.7% of vague inputs.
What this doesn’t fix
The evaluation covers only two proprietary systems from the authors' own company, with labels from small annotator pools and no formal inter-annotator agreement; component-level checks could only be tested against product totals.
Sources
1 on file- Journal of Industrial Ecologydoi:10.1007/s44498-026-00196-9
Cite this record
v1Truvace Impact Record TRV-2026-2490, v1: “Evaluation criteria for AI-assisted product carbon footprinting systems: the cases of mapping and supply chain modeling.” Truvace, 2026-11-12. /record/TRV-2026-2490 (accessed on the day you copy it). sha256 a5d312b769f85298…
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1 versionEvery change made to this record since it was first published, and why. Nothing has changed since it went up.
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