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Assessing the risk of green building materials certification using the back-propagation neural network

Author

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  • Changlu Zhang

    (Beijing Information Science and Technology University)

  • Jian Zhang

    (Beijing Information Science and Technology University)

  • Peng Jiang

    (Shandong University)

Abstract

The development and implementation of green product certification have created new requirements for risk assessment of the certification process. Conventional methods of scoring and subjective evaluation are at odds with the requirements of timeliness, objectivity, intelligence and authority of green certification risk assessment in the era of “big data.” In this study, an improved prototype decision structure was developed based on published literature. Then the Delphi method was used to reach consensus among experts, identify the key risk points of green building materials certification (GBMC), and build a formal decision structure consisting of three aspects and 11 risk points. A conventional back-propagation (BP) neural network model and a Levenberg–Marquardt (L–M) improved BP model, were used to assess the risk of GBMC. The applicability and characteristics of the two models were compared using empirical data. Both models accurately described the risk of GBMC, but the improved LMBP model performed better. The results showed that the new LMBP model performs better than the conventional BP model in terms of mean square error, gradient to a solution, and training efficiency. The enhancement effects were improved by 99.1%, 91.9%, and 33.3%, respectively. This paper believes that when assessing the risk of GBMC, considerations should include not only the risk in the certification process, but also the internal risk of certification agencies and certification businesses. Furthermore, the proposed LMBP model is suitable for use in assessing the risk assessment of GBMC, especially in the environment of “big data”. In order to effectively control the risk of GBMC, the risk of certification institutions, including historical risk, experience risk and ability risk, as well as the risk related to the application enterprise, including its credit risk, satisfaction risk and green index risk, should be taken into account seriously.

Suggested Citation

  • Changlu Zhang & Jian Zhang & Peng Jiang, 2022. "Assessing the risk of green building materials certification using the back-propagation neural network," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6925-6952, May.
    • Handle: RePEc:spr:endesu:v:24:y:2022:i:5:d:10.1007_s10668-021-01734-0
    DOI: 10.1007/s10668-021-01734-0
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