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

Arifuzzaman (Arif) Sheikh (), Steven J. Simske and Edwin K. P. Chong ()
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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

Sustainability, 2024, vol. 16, issue 23, 1-39

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.

Keywords: artificial intelligence; circular economy; digital marketplace; resource allocation; demand-supply matching; dynamic pricing (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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