Liu Jian, Zhang Zhuqing, Feng Rui
School of Resource and Safety Engineering, University of Science and Technology Beijing, Beijing, China.
Key Laboratory of High-Efficient Mining and Safety of Metal Mines of the Ministry of Education, University of Science and Technology Beijing, Beijing, China.
PLoS One. 2025 Jun 20;20(6):e0325393. doi: 10.1371/journal.pone.0325393. eCollection 2025.
Accidents are often attributed to frontline operator errors, overshadowing higher-level organizational and regulatory factors. This study integrates Systems-Theoretic Accident Model and Processes (STAMP) with fuzzy-set Qualitative Comparative Analysis (fsQCA) and Necessary Condition Analysis (NCA) - a configurational approach - to examine 80 major accident investigation reports from five high-risk Chinese industries (chemical, construction, transportation, coal mining, firefighting) spanning 2010-2022. Four systemic control elements (control activities errors, feedback errors, controller failures, controlled process errors) were assessed against three severity indicators (fatalities, injuries, direct economic losses). Results reveal distinct yet overlapping causal pathways. In chemical accidents, feedback errors are crucial for high fatalities. Construction and coal mining often link early controller/control activity failures to severe outcomes. Transportation highlights control activity errors for injuries, while firefighting points to the combination of control activity errors and controller failures. NCA corroborates key factors like feedback errors and controller failures as necessary conditions (effect sizes d > 0.1, p < 0.05). While supplementary statistical analysis confirmed these factors' general importance, it faced data limitations (small N, collinearity); the fsQCA/NCA approach provided more robust insights into combinatorial pathways and necessity. Bottleneck analyses further indicate that even modest increments in key errors can trigger disproportionately large losses. These findings underscore the need for multi-level interventions-strengthening feedback loops, organizational oversight, and control processes-to mitigate accident severity in complex socio-technical systems, demonstrating the utility of configurational methods for understanding systemic failures.
事故往往归咎于一线操作人员的失误，从而使更高层面的组织和监管因素相形见绌。本研究将系统理论事故模型与过程（STAMP）与模糊集定性比较分析（fsQCA）和必要条件分析（NCA）（一种组态方法）相结合，以审视2010年至2022年期间来自中国五个高风险行业（化工、建筑、运输、煤矿、消防）的80份重大事故调查报告。针对三个严重程度指标（死亡人数、受伤人数、直接经济损失）评估了四个系统控制要素（控制活动失误、反馈失误、控制器故障、受控过程失误）。结果揭示了不同但相互重叠的因果路径。在化工事故中，反馈失误对高死亡率至关重要。建筑和煤矿行业常常将早期的控制器/控制活动故障与严重后果联系起来。运输行业突出了控制活动失误与受伤情况的关联，而消防行业则指出了控制活动失误和控制器故障的组合。NCA证实了反馈失误和控制器故障等关键因素是必要条件（效应量d>0.1，p<0.05）。虽然补充统计分析证实了这些因素的普遍重要性，但它面临数据限制（样本量小、共线性）；fsQCA/NCA方法为组合路径和必要性提供了更有力的见解。瓶颈分析进一步表明，即使关键失误有适度增加也可能引发不成比例的巨大损失。这些发现强调了需要进行多层次干预——加强反馈回路、组织监督和控制过程——以减轻复杂社会技术系统中的事故严重程度，证明了组态方法在理解系统故障方面的效用。
