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Evaluating Artificial Intelligence Models for Resource Allocation in Circular Economy Digital Marketplace

Author

Listed:
  • Arifuzzaman (Arif) Sheikh

    (Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Steven J. Simske

    (Department of Systems Engineering, Colorado State University, Fort Collins, CO 80523, USA)

  • Edwin K. P. Chong

    (Department of Electrical and Computer Engineering, Colorado State University, Fort Collins, CO 80523, USA)

Abstract

This study assesses the application of artificial intelligence (AI) algorithms for optimizing resource allocation, demand-supply matching, and dynamic pricing within circular economy (CE) digital marketplaces. Five AI models—autoregressive integrated moving average (ARIMA), long short-term memory (LSTM), random forest (RF), gradient boosting regressor (GBR), and neural networks (NNs)—were evaluated based on their effectiveness in predicting waste generation, economic growth, and energy prices. The GBR model outperformed the others, achieving a mean absolute error (MAE) of 23.39 and an R 2 of 0.7586 in demand forecasting, demonstrating strong potential for resource flow management. In contrast, the NNs encountered limitations in supply prediction, with an MAE of 121.86 and an R 2 of 0.0151, indicating challenges in adapting to market volatility. Reinforcement learning methods, specifically Q-learning and deep Q-learning (DQL), were applied for price stabilization, resulting in reduced price fluctuations and improved market stability. These findings contribute a conceptual framework for AI-driven CE marketplaces, showcasing the role of AI in enhancing resource efficiency and supporting sustainable urban development. While synthetic data enabled controlled experimentation, this study acknowledges its limitations in capturing full real-world variability, marking a direction for future research to validate findings with real-world data. Moreover, ethical considerations, such as algorithmic fairness and transparency, are critical for responsible AI integration in circular economy contexts.

Suggested Citation

  • Arifuzzaman (Arif) Sheikh & Steven J. Simske & Edwin K. P. Chong, 2024. "Evaluating Artificial Intelligence Models for Resource Allocation in Circular Economy Digital Marketplace," Sustainability, MDPI, vol. 16(23), pages 1-39, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10601-:d:1535951
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    References listed on IDEAS

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    1. Sean J. Taylor & Benjamin Letham, 2018. "Forecasting at Scale," The American Statistician, Taylor & Francis Journals, vol. 72(1), pages 37-45, January.
    2. Veale, Michael & Binns, Reuben, 2017. "Fairer machine learning in the real world: Mitigating discrimination without collecting sensitive data," SocArXiv ustxg, Center for Open Science.
    3. Helen Onyeaka & Phemelo Tamasiga & Uju Mary Nwauzoma & Taghi Miri & Uche Chioma Juliet & Ogueri Nwaiwu & Adenike A. Akinsemolu, 2023. "Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review," Sustainability, MDPI, vol. 15(13), pages 1-20, July.
    4. Muhammad Salman Pathan & Edana Richardson & Edgar Galvan & Peter Mooney, 2023. "The Role of Artificial Intelligence within Circular Economy Activities—A View from Ireland," Sustainability, MDPI, vol. 15(12), pages 1-18, June.
    5. Julien Walzberg & Jean‐Marc Frayret & Annika L. Eberle & Alberta Carpenter & Garvin Heath, 2023. "Agent‐based modeling and simulation for the circular economy: Lessons learned and path forward," Journal of Industrial Ecology, Yale University, vol. 27(5), pages 1227-1238, October.
    6. Elizabeta Stamevska & Aleksandra Stankovska & Vasko Stamevski, 2020. "Principles Of The Circular Economy," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 17(1), pages 99-107.
    7. Franck Galtier & François Bousquet & Martine Antona & Pierre Bommel, 2012. "Markets as communication systems," Journal of Evolutionary Economics, Springer, vol. 22(1), pages 161-201, January.
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