Analysis on characteristics and severity of major work safety accidents in China

doi:10.16265/j.cnki.issn1003-3033.2024.02.0946

Abstract

Abstract:

A comprehensive database of MWSA was established to facilitate the data management to reduce the frequency and severity of accidents in China. From 2010 to 2022, a total of 278 records, supervised by the State Council Security Committee, were collected and stored in the MWSA database. The distribution of accident characteristics was explored for the dimensions of accident time, space, industry and type, which were examined in this study. Using a comprehensive quantitative index, a quantile regression model was developed to identify factors that significantly influenced accident severity. The results show that in terms of spatial and temporal distribution, the number of accidents and deaths from June to September is at the peak, and the number of accidents and deaths on sunny days (7:00-18:00) accounts for as much as two-thirds. There are fewer accident records in Beijing, Tianjin, Jiangsu, Zhejiang, Shanghai and Fujian. Explosions and vehicle injuries notably stand out as the primary accident types. At a significance level of 0.05, the accident severity is correlated with various factors, including accident type, date, season, sunlight, weather, average temperature, company staff size and company establishment time. Weather and average temperature emerge as pivotal factors influencing low-severity accidents. Moreover, enterprises with less than 100 employees are more prone to severe accidents.

Key words: major work safety accidents(MWSA), accident severity, accident characteristics, quantile regression, heterogeneity, database

CLC Number:

X928.02事故处理

JIAO Yu, MA Yulei, LI Xian, MA Hongliang, KANG Yutao. Analysis on characteristics and severity of major work safety accidents in China[J]. China Safety Science Journal, 2024, 34(2): 94-102.

Figures/Tables 11

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字段名称	事故ID	事故等级ID	死亡人数	受伤人数	重伤/失踪人数	直接经济损失
数据类型	int	char	int	int	int	decimal
字段大小	4字节	4字节	4字节	4字节	4字节	12字节

变量类别	变量	变量分组	事故频数 (占比/%)	变量类别	变量	变量分组	事故频数 (占比/%)
事故属性	行业	交通运输业=1^*	76(27.3)	环境条件	发生日期	工作日=0^*	196(70.5)
		矿业=2	107(38.5)		发生日期	非工作日=1	82(29.5)
		商贸制造业=3	23(8.3)		季节	春秋冬=0^*	196(70.5)
		建筑业及其他=4	72(25.9)		季节	夏=1	82(29.5)
	类型	车辆伤害=1^*	72(25.9)		日照条件	白天=0^*	187(67.3)
		火灾=2	23(8.3)		日照条件	夜晚=1	91(32.7)
		爆炸=3	80(28.8)		天气	晴=0^*	170(61.2)
		坍塌=4	23(8.3)		天气	恶劣天气=1	108(38.9)
		中毒和窒息=5	28(10.1)		平均温度		15.7(±10.7)
		其他=6	52(18.7)	事故企业	经营状态	正常=0^*	149(46.4)
环境条件	发生地区	西南=1^*	42(15.1)			非正常(违法/规、停产/业)=1	129(53.6)
		西北=2	30(10.8)			非正常(违法/规、停产/业)=1	129(53.6)
		华中=3	55(20.0)		员工规模	(0,100] =1^*	172(61.9)
		华南=4	18(6.5)			(100,999] =2	71(25.5)
		华东=5	63(22.7)			[1 000,+∞) =3	35(12.6)
		华北=6	37(13.3)		成立时长(月)		120.5(±89.0)
		东北=7	33(11.9)

变量	Q (0.25)		Q (0.50)		Q (0.75)		Q (0.90)		OLS
变量	参数估计值	p	参数估计值	p	参数估计值	p	参数估计值	p	参数估计值	p
常量	7.017	<0.001^**	7.764	<0.001^**	10.738	<0.001^**	13.057	<0.001^**	10.412	<0.001^**
事故类型	—	—	—	—	—	—	—	—	—	—
火灾	-1.603	0.168	1.149	0.237	-0.658	0.546	-4.920	0.041^*	-1.211	0.432
爆炸	-5.529	<0.001^**	-2.228	0.070	-1.369	0.297	-0.776	0.866	-1.918	0.066
坍塌	-5.119	<0.001^**	-2.822	0.032^*	-4.268	0.003^**	-5.179	0.056	-3.792	0.015^**
中毒和窒息	-7.279	<0.001^**	-6.648	<0.001^**	-5.224	<0.001^**	-7.874	<0.001^**	-6.638	<0.001^**
其他	-6.380	<0.001^**	-5.595	<0.001^**	-5.584	<0.001^**	-5.514	<0.001^**	-5.790	<0.001^**
发生日期	2.109	0.003^**	2.583	0.002^**	2.473	0.001^**	3.960	0.008^**	2.749	0.001^**
季节	1.719	0.029^*	2.074	0.047^*	2.374	0.019^*	3.963	0.125	2.989	0.006^**
日照条件	1.159	0.053	1.547	0.071	4.396	<0.001^**	4.317	<0.001^**	2.565	0.002^**
天气	1.167	0.020^*	0.792	0.312	0.265	0.719	0.630	0.633	1.168	0.144
平均温度	-0.085	0.001^**	-0.055	0.183	-0.047	0.230	-0.103	0.228	-0.063	0.181
员工规模	—	—	—	—	—	—	—	—	—	—
(100,999]	0.401	0.619	0.612	0.477	-0.458	0.627	-3.063	0.016^**	0.101	0.914
[1000,+∞)	2.027	0.140	1.766	0.138	1.457	0.314	1.953	0.371	1.378	0.278
成立时长	0.007	0.036^*	0.009	0.034^*	0.006	0.230	0.010	0.112	0.007	0.130