This study investigates the correlation between previous coal mine safety policies and accidents in China. Data on coal mine accidents and government regulatory information from 2008 to 2021 are collected. The characteristics of coal mine accidents are analyzed, and safety policy indexes are identified. An ordinary least squares (OLS) regression model is established to quantitatively analyze the correlation between accidents and safety policy. The study finds that safety policies have some impact on accident occurrence in coal mines. Although there has been a decrease in accidents and deaths over time, higher mortality rates are observed during periods of increased production intensity and on weekends. Gas accidents are the most common, followed by roof and flood accidents. The study concludes that national safety policies with wider coverage and a stronger system are effective in preventing accidents, but caution should be exercised to avoid reduced vigilance with decreasing death rates.

In the process of green and smart mine construction under the context of carbon neutrality, China\'s coal safety situation has been continuously improved in recent years. In order to recognize the development of coal production in China and prepare for future monitoring and prevention of safety incidents, this study mainly elaborated on the basic situation of coal resources and national mining accidents over the past five years  (2017-2021), from four dimensions (accident level, type, region, and time), and then proposed the preventive measures based on accident statistical laws. The results show that the storage of coal resources has obvious geographic characteristics, mainly concentrated in the Midwest, with coal resources in Shanxi and Shaanxi accounting for about 49.4%. The proportion of coal consumption has dropped from 70.2% to 56% between 2011 and 2021, but still accounts for more than half of the all. Meanwhile, the accident-prone areas are positively correlated with the amount of coal production. Among different levels of coal mine accidents, general accidents had the highest number of accidents and deaths, with 692 accidents and 783 deaths, accounting for 87.6% and 54.64% respectively. The frequency of roof, gas, and transportation accidents is relatively high, and the number of single fatalities caused by gas accidents is the largest, about 4.18. In terms of geographical distribution of accidents, the safety situation in Shanxi Province is the most severe. From the time distribution of coal mine accidents, the accidents mainly occurred in July and August, and rarely occurred in February and December. Finally, the \"4 + 4\" safety management model is proposed, combining the statistical results with coal production in China. Based on the existing health and safety management systems, the managements are divided into four sub-categories, and more specific measures are suggested.

The risk of coal mine accidents rises significantly with mining depth, making it urgent for accident prevention to be supported by both scientific analysis and advanced technologies. Hence, a comprehensive grasp of the research progress and differences in hotspots of coal mine accidents in China serves as a guide to find the shortcomings of studies in the field, promote the effectiveness of coal mine disaster management, and enhance the prevention and control ability of coal mine accidents. This paper analyzes Chinese and foreign literature based on data mining algorithms (LSI + Apriori), and the findings indicate that: (1) 99% of the available achievements are published in Chinese or English-language journals, with the research history conforming to the stage of Chinese coal industry development, which is characterized by \"statistical description, risk evaluation, mechanism research, and intelligent reasoning\". (2) Chinese authors are the primary contributors that lead and contribute to the continued development of coal mine accident research in China globally. Over 81% of the authors and over 60% of the new authors annually are from China. (3) The emphasis of the Chinese and English studies is different. Specifically, the Chinese studies focus on the analysis of accident patterns and causes at the macroscale, while the English studies concentrate on the occupational injuries of miners at the small-scale and the mechanism of typical coal mine disasters (gas and coal spontaneous combustion). (4) The research process in Chinese is generally later than that in English due to the joint influence of the target audience, industrial policy, and scientific research evaluation system. After 2018, the Chinese studies focus significantly on AI technology in deep mining regarding accident rules, regional variation analysis, risk monitoring and early warning, as well as knowledge intelligence services, while the hotspots of English studies remain unchanged. Furthermore, both Chinese and English studies around 2019 focus on \"public opinion\", with Chinese ones focusing on serving the government to guide the correct direction of public opinion while English studies focus on critical research of news authenticity and China\'s safety strategy.

The risk factors affecting workers\' unsafe acts were comprehensively identified by Human Factors Analysis and Classification System (HFACS) and grounded theory based on interview data and accident reports from deep coal mines. Firstly, we collected accident case and field interview data from deep coal mines issued by authoritative institutions. Then, the data were coded according to grounded theory to obtain relevant concepts and types. The HFACS model was used to classify the concepts and categories. Finally, the relationship between core and secondary categories was sorted out by applying a story plot. The results show that risk factors of unsafe acts of deep coal mine workers include environmental factors, organizational influence, unsafe supervision and unsafe state of miners, and the main manifestations of unsafe acts are errors and violations. Among them, the unsafe state of miners is the intermediate variable, and other factors indirectly affect risky actions of coal miners through unsafe sates. Resource management, organizational processes and failure to correct problems are the top three risk factors that occur more frequently in unsafe acts. The three most common types of unsafe act are unreasonable labor organization, failure to enforce rules, and inadequate technical specifications. By combining grounded theory and the HFACS framework to analyze data, risk factors for deep coal miners can be quickly identified, and more precise and comprehensive conceptual models of risk factors in unsafe acts of deep coal miners can be obtained.

It has been revealed in numerous investigation reports that human and organizational factors (HOFs) are the fundamental causes of coal mine accidents. However, with various kinds of accident-causing factors in coal mines, the lack of systematic analysis of causality within specific HOFs could lead to defective accident precautions. Therefore, this study centered on the data-driven concept and selected 883 coal mine accident reports from 2011 to 2020 as the original data to discover the influencing paths of specific HOFs. First, 55 manifestations with the characteristics of the coal mine accidents were extracted by text segmentation. Second, according to their own attributes, all manifestations were mapped into the Human Factors Analysis and Classification System (HFACS), forming a modified HFACS-CM framework in China\'s coal-mining industry with 5 categories, 19 subcategories and 42 unsafe factors. Finally, the Apriori association algorithm was applied to discover the causal association rules among external influences, organizational influences, unsafe supervision, preconditions for unsafe acts and direct unsafe acts layer by layer, exposing four clear accident-causing \"trajectories\" in HAFCS-CM. This study contributes to the establishment of a systematic causation model for analyzing the causes of coal mine accidents and helps form corresponding risk prevention measures directly and objectively.

Mining has been historically considered as a naturally high-risk industry worldwide. Deaths caused by coal mine accidents are more than the sum of all other accidents in China. Statistics of 320 coal mine accidents in Shandong province show that all accidents contain indicators of \"unsafe conditions of the rules and regulations\" with a frequency of 1590, accounting for 74.3% of the total frequency of 2140. \"Unsafe behaviors of the operator\" is another important contributory factor, which mainly includes \"operator error\" and \"venturing into dangerous places.\" A systems analysis approach was applied by using structural equation modeling (SEM) to examine the interactions between the contributory factors of coal mine accidents. The analysis of results leads to three conclusions. (i) \"Unsafe conditions of the rules and regulations,\" affect the \"unsafe behaviors of the operator,\" \"unsafe conditions of the equipment,\" and \"unsafe conditions of the environment.\" (ii) The three influencing factors of coal mine accidents (with the frequency of effect relation in descending order) are \"lack of safety education and training,\" \"rules and regulations of safety production responsibility,\" and \"rules and regulations of supervision and inspection.\" (iii) The three influenced factors (with the frequency in descending order) of coal mine accidents are \"venturing into dangerous places,\" \"poor workplace environment,\" and \"operator error.\"