EcoTrace: A Modular Framework for Product Sustainability Transparency Using AI-Assisted Evaluation and QR-Based Traceability
Aenugu Shiva Jyothi
Assistant Professor
Department of Computer Science and Engineering
Geethanjali College Of Engineering And Technology
Hyderabad, India
shivajyothi9492@gmail.com
Karthikeya Goud Nagelli
Department of Computer Science and Engineering
Geethanjali College Of Engineering And Technology
Hyderabad, India
22r11a05m6@gcet.edu.in
Manohar Reddy Vaila
Department of Computer Science and Engineering
Geethanjali College Of Engineering And Technology
Hyderabad, India
22r11a05n5@gcet.edu.in
Charan Sai Macha
Department of Computer Science and Engineering
Geethanjali College Of Engineering And Technology
Hyderabad, India
22r11a05l9@gcet.edu.in
Abstract— Ensuring transparency in product sustainability has become a critical challenge due to increasing environmental concerns and the growing demand for responsible consumption. Existing sustainability communication mechanisms largely rely on static eco-labels, certifications, and manufacturer-declared claims, which often lack detailed traceability and are difficult for consumers to verify [1]. Although supply chain traceability systems have been widely explored to improve accountability and product lifecycle visibility, many of these systems are designed primarily for internal monitoring rather than consumer-facing transparency platforms [2].
This research proposes EcoTrace, a modular and scalable framework aimed at improving product sustainability transparency through structured traceability and AI-assisted evaluation. The framework introduces a layered architecture that separates product definition, batch lifecycle tracking, sustainability assessment, verification, and public transparency. A decision-based evaluation mechanism determines when sustainability reassessment is required, thereby reducing redundant analysis and improving system efficiency.
AI-assisted sustainability scoring generates interpretable indicators that help communicate environmental impact information in a structured and understandable manner. The framework also supports optional third-party verification mechanisms to enhance credibility and trust. To enable real-time consumer access, physical products are linked to digital transparency records through QR-based access mechanisms, allowing users to retrieve sustainability information for specific product batches.
The proposed framework is validated through a prototype-based qualitative evaluation focusing on architectural feasibility, modularity, and transparency effectiveness. The results indicate that EcoTrace provides a practical and extensible foundation for developing digital systems that promote sustainability transparency and informed consumer decision-making.
Keywords— sustainability transparency, product traceability, AI assisted evaluation, QR based access, sustainable consumption
