A systems approach to extraordinarily major coal mine accidents in China from 1997 to 2011: an application of the HFACS approach

Abstract

This study aimed to provide a greater understanding of the systemic factors involved in coal mine accidents and to examine the relationships between the contributing factors across all levels of the system. Ninety-four extraordinarily major coal mine accidents that occurred in China from 1997 to 2011 were analyzed using the human factors analysis and classification system (HFACS). The empirical results showed that the frequencies of unsafe behaviors, inadequate regulation and failure to correct hidden dangers were the highest among five levels, 14 categories and 48 indicators, respectively. The odds ratio technique was applied to quantitatively examine the relationships between contributing factors. Various statistically significant associations were discovered and should receive greater attention in future attempts to develop accident measures. In addition, several strategies concerning the main contributing factors and routes to failure are proposed to prevent accidents from reoccurring in an organization.

Keywords:

• accident analysis
• systems approach
• human factors analysis and classification system
• extraordinarily major coal mine accidents

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Yingyu Zhang http://orcid.org/0000-0002-6507-8867

Notes

1 According to the China State Council (CSC) [14], accidents are classified into four types related to deaths, injuries and losses: extraordinarily major accidents, major accidents, large accidents and general accidents. Extraordinarily major accidents are the highest level of accident and are defined as those that have death tolls of more than or equal to 30, cause more than 100 seriously injured persons or cause direct economic losses of more than CNY 100 million (EUR 100 = CNY 786.3; USD 100 = CNY 661.4).

2 EUR 100 = CNY 786.3, USD 100 = CNY 661.4.

Additional information

Funding

This work was supported by National Social Science Foundation of China [Project ID: 16BGL176].