China Safety Science Journal ›› 2023, Vol. 33 ›› Issue (11): 59-66.doi: 10.16265/j.cnki.issn1003-3033.2023.11.1087

• Safety social science and safety management • Previous Articles     Next Articles

Network analysis of chemical safety critical causation from perspective of stakeholders

LIU Dan1,2(), ZHU Weichang1,2, LI Moxiao1,2, JIN Qingsong3, SAIDAH Saad3   

  1. 1 China Research Center for Emergency Management, Wuhan University of Technology
    2 School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan Hubei 430070, China
    3 Faculty of Information Science and Technology, National University of Malaysia, UKM Bangi Selangor 43600, Malaysia
  • Received:2023-06-20 Revised:2023-09-13 Online:2023-11-28 Published:2024-05-28

Abstract:

In order to address the issue of the lack of complex coupling relationships among stakeholders in the causes analysis of chemical accidents, firstly, taking 80 typical chemical accidents from 2016 to 2021 as cases, 6 stakeholders and 26 related risk factors were extracted, and the risk matrix was constructed. Then, Netdraw was used to visualize the matrix, and obtain the chemical safety risk network diagram. Secondly, on an overall level, the density matrix and image matrix of the block model in SNA were used to obtain 7 risk factors in the core block. On the individual level, node degree values and centrality were used to determine 9 risk factors. Then, the risk factors selected on the overall and individual levels were intersected to determine the four core risk factors in the network. Finally, the effect of the controlled risk network was tested from two aspects of overall network density and clustering coefficient. The results show that the core risk factors are four, namely illegal operations, chemical enterprises don't have the conditions for safe production, the pre-evaluation of safety evaluation institutions and the acceptance evaluation of the status quo are not in place, and the performance and quality of materials and equipment are insufficient. After controlling the core risk factors, the density of the new risk network is reduced by 35%, and the clustering coefficient is reduced by 21%, which effectively reduces the possibility of chemical accidents caused by a key risk.

Key words: stakeholders, chemical safety, key causative factors, social network analysis (SNA), block model