ArticleScientific reports2026
Interpretable AI-enabled decision support for drinking-straw substitution using per-use greenhouse-gas indicators and user-review evidence.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Plastic drinking straws are a visible single-use plastic product, yet selecting suitable substitutes remains challenging because literature-derived climate evidence and reported user experience are rarely evaluated together. This study develops and demonstrates an interpretable AI-enabled decision-support workflow that integrates literature-derived per-use greenhouse gas (GHG) indicators with review-derived user evidence extracted from online customer reviews using natural language processing (NLP). Drinking-straw alternatives were used as an information-rich case study. The integrated assessment combined a GHG-derived score, a user-experience feature score, and rating-based consumer approval within a transparent multi-criteria decision analysis (MCDA) under four predefined decision-priority scenarios. Among the five shortlisted materials and within the evaluated dataset, the selected per-use GHG assumptions, review-derived user evidence, normalization procedure, and scenario-specific weights resulted in Silicone achieving the highest integrated MCDA score across all four scenarios, whereas Paper ranked lowest. Silicone combined a low per-use GHG indicator with the highest user-experience feature score and high consumer approval. Paper had the highest per-use GHG indicator and a moderate user-experience feature score, while lexical analysis identified recurring functionality-related expressions in its reviews. A shallow decision tree identified a 0.081 kg CO₂e/use threshold separating Paper from the lower-per-use-GHG reusable alternatives within the evaluated decision matrix. The study is not a new process-based life-cycle assessment or comprehensive sustainability assessment. Instead, it demonstrates decision support limited to per-use GHG indicators and review-derived user evidence; broader sustainability dimensions were outside the scope. Future studies may adapt and evaluate the workflow for other product categories using product-appropriate environmental criteria and relevant user-derived evidence.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.