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Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model

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  • Feng Dong

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

  • Yifei Hua

    (School of Management, China University of Mining and Technology, Xuzhou 221116, China)

Abstract

While enriching people’s lives, the rapid development of online shopping has posed a severe challenge to the environment. Questionnaires focusing on the intention to recycle packaging waste are designed. These questionnaires contain first-level variables such as recycling behavior attitude, recycling behavior cognition, situational factors, historical recycling behavior, and recycling behavior intention. With the collected questionnaire data, a regression analysis is first conducted on the selection of variables and the effect of variable prediction. After ensuring the validity of the variables, 15 second-level variables are extracted into eight principal components using principal component analysis. These components serve as input to a Bayesian regularized neural network. Subsequently, a three-layer (8-15-1) neural network model is constructed; the trained neural network model achieves a high degree of fit between the predicted and measured values of the test set, thus further proving the rationality of the selected variables and the neural network model. Finally, this study uses the connection weights matrix of the neural network model and the Garson formula to analyze in depth the specific impact of each second-level variable on the intention to recycle packaging waste. Note that given the particularity of packaging waste recycling behavior, the impact on social norms, recycling behavior knowledge, values, and publicity on behavioral intentions in second-level variables is different from that obtained in similar previous studies.

Suggested Citation

  • Feng Dong & Yifei Hua, 2018. "Are Chinese Residents Willing to Recycle Express Packaging Waste? Evidence from a Bayesian Regularized Neural Network Model," Sustainability, MDPI, vol. 10(11), pages 1-24, November.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:11:p:4152-:d:182176
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    References listed on IDEAS

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    Cited by:

    1. Yi He & Qianqian Xu & Da Zhao, 2020. "Impacts of the BOPS Option on Sustainable Retailing," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
    2. Li Ling & Ran Anping & Xu Di, 2023. "Proposal of a hybrid decision-making framework for the prioritization of express packaging recycling patterns," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(3), pages 2610-2647, March.
    3. Qinqin Wu & Yuanqi Li & Faiza Siddiqui & Du Jie, 2024. "Consumer intention to express packaging recycling in China's digital economy: an expansion of the theory of planned behavior," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 26(12), pages 31627-31655, December.
    4. Pengfei Li & Yutao Ru & Jianhong Wu, 2023. "Influential Factors Affecting Recycling Behavior toward Cardboard Boxes in the Logistics Sector: An Empirical Analysis from China," Sustainability, MDPI, vol. 15(18), pages 1-20, September.
    5. Lu Xiao & Rongrong Fan & Chaojie Wang & Jun Wang, 2020. "Policy Analyses on Promoting the Recycling of Express Packages," Sustainability, MDPI, vol. 12(22), pages 1-12, November.
    6. Huilin Wang & Aweewan Mangmeechai, 2021. "Understanding the Gap between Environmental Intention and Pro-Environmental Behavior towards the Waste Sorting and Management Policy of China," IJERPH, MDPI, vol. 18(2), pages 1-16, January.
    7. Bowen Qin & Ge Song, 2022. "Internal Motivations, External Contexts, and Sustainable Consumption Behavior in China—Based on the TPB-ABC Integration Model," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    8. Huilin Wang & Jiaxuan Li & Aweewan Mangmeechai & Jiafu Su, 2021. "Linking Perceived Policy Effectiveness and Proenvironmental Behavior: The Influence of Attitude, Implementation Intention, and Knowledge," IJERPH, MDPI, vol. 18(6), pages 1-17, March.

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