Document Type

Article

Publication Date

Summer 7-29-2026

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.

Comments

Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval: This study did not involve direct interaction with human participants or the collection of personally identifiable private information. The analysis was based on publicly accessible product-review content and published secondary sources. Declaration of generative AI and AI-assisted technologies in the manuscript preparation process: During the preparation of this work, the authors used AI-assisted tools for language improvement and editorial support only. After using these tools, the authors reviewed and edited the content as needed and take full responsibility for the content of the manuscript.

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