Decoding Key Drivers of Information-Sensitive Waste Electrical and Electronic Equipment Recycling in China Using a DEMATEL-BP Neural Network Approach
DOI:
https://doi.org/10.15377/2410-3624.2025.12.7Keywords:
DEMATEL model, BP neural network, Recycling behavior, Stakeholder analysis, Information-sensitive WEEEAbstract
At the intersection of environmental sustainability and personal data security, the recycling of information-sensitive waste electrical and electronic equipment (WEEE), such as smartphones and laptops, presents the dual challenges of low formal recycling rates and data security risks. Existing research often overlooks the specific dynamics of information-sensitive WEEE and the complex, interdependent factors influencing residents’ recycling decisions from a holistic stakeholder perspective. The novelty of this study lies in integrating stakeholder theory with a hybrid analytical approach combining the Decision-making Trial and Evaluation Laboratory (DEMATEL) model and a back-propagation (BP) neural network model. This method first clarifies causal relationships among key factors and then examines their non-linear impacts on recycling intentions. The findings reveal three primary insights: (i) the cost of information cleaning has the greatest impact on the size of the recycling market. (ii) Economic benefits have the greatest impact on competition within informal recycling channels, residents’ awareness of information protection and preference for recycling channels. (iii) Incentive publicity has the greatest impact on residents’ environmental awareness. In conclusion, this study contributes a stakeholder-focused framework and demonstrates the efficacy of combining DEMATEL and BP neural network to decipher complex behavioral influences. The results underscore the necessity for integrated policies that simultaneously address economic incentives, cost barriers, and awareness campaigns to effectively promote the recycling of information-sensitive WEEE
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