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Interpretable AI-enabled decision support for drinking-straw substitution using per-use greenhouse-gas indicators and
Marwa S Hassan1, Shymaa Khamis2, Ahmed Barakat3
1Systems and Information Department, Engineering Research Institute, and New and Renewable Energy, National Research Centre, Giza, Egypt. ms.hassan-elsayed@nrc.sci.eg.
Scientific Reports
|July 29, 2026
Summary
Selecting sustainable drinking straws is hard. This study uses AI to combine climate data and user reviews, finding silicone straws best balance environmental impact and user satisfaction.
Area of Science:
- Environmental Science
- Materials Science
- Artificial Intelligence
Background:
- Single-use plastic straws present environmental challenges.
- Evaluating alternatives requires integrating climate data with user experience, which is rarely done.
Purpose of the Study:
- To develop and demonstrate an AI-enabled decision-support workflow.
- To integrate per-use greenhouse gas (GHG) indicators with user experience data from online reviews.
- To assess drinking straw alternatives using a multi-criteria decision analysis (MCDA).
Main Methods:
- Utilized natural language processing (NLP) to extract user evidence from customer reviews.
- Integrated literature-derived per-use GHG indicators with NLP-derived user experience scores.
- Applied MCDA under four decision-priority scenarios to rank straw materials.
- Developed a decision tree to identify environmental thresholds.
Main Results:
- Silicone straws achieved the highest integrated MCDA score across all scenarios.
- Paper straws ranked lowest due to high per-use GHG indicators.
- Silicone demonstrated low GHG impact, high user experience, and strong consumer approval.
- A decision threshold of 0.081 kg CO₂e/use separated paper from reusable alternatives.
Conclusions:
- The AI-enabled workflow effectively integrates environmental and user data for product assessment.
- Silicone emerges as a favorable alternative to plastic straws based on the evaluated criteria.
- The study provides a transparent decision-support framework for similar product evaluations.
