A Digital Recycling and Affordable Resale Marketplace Using Artificial Intelligence

Abstract

Rapid urban expansion and changing consumer habits have driven a significant rise in global solid waste production. A considerable portion comprises items with residual economic value that are prematurely discarded due to limited awareness, insufficient guidance, and lack of effective reuse avenues. Conventional waste management systems largely focus on collection and disposal, neglecting reuse, recycling, and value recovery. Recent studies demonstrate that digital platforms can mitigate these challenges by enhancing accessibility, fostering stakeholder involvement, and streamlining recycling processes. This paper builds upon prior research on digital recycling platforms, reuse-centered systems, and incentive-driven models, exploring how Artificial Intelligence can optimize decision-making and system efficiency. Through analysis of prior studies and user behavior patterns, the research identifies critical gaps and presents a conceptual framework for an integrated digital recycling and affordable resale ecosystem that advances sustainable waste management and circular economy objectives.

Introduction

Urban waste patterns reveal significant inefficiencies: usable furniture, electronics, and clothing are prematurely discarded, reflecting a linear “dispose-first” approach in current waste management systems. Traditional policies focused on landfilling and incineration, while fragmented recycling initiatives fail to retain material value or integrate with everyday consumption. Circular economy principles—emphasizing reuse, repair, resale, and eventual recycling—remain underutilized, with consumers lacking guidance, trust, and economic incentives.

Digital tools, online resale platforms, and AI applications have emerged to address these gaps, but existing efforts are largely fragmented: educational platforms inform without motivating, resale marketplaces extend product life but lack transparency, and AI optimizes operations without engaging users. This disconnection prevents a cohesive system that preserves material value while promoting sustainable behaviors.

The study proposes an AI-driven marketplace framework that unifies digital recycling guidance, affordable resale, and intelligent decision support. Using circular economy principles and socio-technical perspectives, the framework integrates user engagement, trust-building, data-driven pricing, and intelligent sorting to create a seamless, value-retaining ecosystem. The research methodology involves qualitative, exploratory analysis of digital recycling tools, resale systems, incentive mechanisms, and AI applications to identify gaps and guide the design of this integrated system.

Overall, the framework aims to transform fragmented digital waste management into a cohesive, user-centered, and economically viable circular system that encourages sustainable consumption and extends product lifecycles.

Conclusion

This study proposes an AI-driven marketplace framework that unifies digital recycling guidance, affordable resale mechanisms, and intelligent decision support into a cohesive circular ecosystem. By addressing the limitations of existing systems—which often operate in isolation—the model demonstrates how AI can strengthen user participation, improve economic value retention, and support evidence-based sustainability planning. The framework offers a forward-looking blueprint for digital circularity, advancing both theoretical understanding and practical innovation in sustainable waste management. With appropriate policy support and empirical validation, it holds the potential to transform everyday disposal decisions into meaningful contributions toward a more circular and resource-efficient future.

References

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Copyright

Copyright © 2025 Areeba Beg, Binita Biswas, Ankita Singh, Pooja Gupta, Dr. Ranjeet Kumar Rai. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.