Forecasting law enforcement frequency of internet+ coal mine safety supervision
International Journal of Energy Sector Management
ISSN: 1750-6220
Article publication date: 10 July 2023
Abstract
Purpose
Coal is the basic energy and essential resource in China, which is crucial to the economic lifeline and energy security of the country. Coal mining has been ever exposed to potential safety risks owing to the complex geologic environment. Effective safety supervision is a vital guarantee for safe production in coal mines. This paper aims to explore the impacts of the internet+ coal mine safety supervision (CMSS) mode that is being emerged in China.
Design/methodology/approach
In this study, the key factors influencing CMSS are identified by social network analysis. They are used to develop a multiple linear regression model of law enforcement frequency for conventional CMSS mode, which is then modified by an analytical hierarchy process to predict the law enforcement frequency of internet+ CMSS mode.
Findings
The regression model demonstrated high accuracy and reliability in predicting law enforcement frequency. Comparative analysis revealed that the law enforcement frequency in the internet+ mode was approximately 40% lower than the conventional mode. This reduction suggests a potential improvement in cost-efficiency, and the difference is expected to become even more significant with an increase in law enforcement frequency.
Originality/value
To the best of the authors’ knowledge, this is one of the few available pieces of research which explore the cost-efficiency of CMSS by forecasting law enforcement frequency. The study results provide a theoretical basis for promoting the internet+ CMSS mode to realize the healthy and sustainable development of the coal mining industry.
Keywords
Acknowledgements
The authors wish to recognize for supporting the improvement of this manuscript to Grant ID 2018YFC0808301 of the National Key Research and Development Program of China, Grant ID 19YJCZH087 of the Youth Foundation of Social Science and Humanity of the Ministry of Education of China, Grant ID KFJJ22-15M of Opening Project of the State Key Laboratory of Explosion Science and Technology (Beijing Institute of Technology), Grant ID 52274245 of the National Natural Science Foundation of China and Grant ID 23CA001-04 of the Innovation Engineering Project of Beijing Academy of Science and Technology.
Competing interests: The authors have no relevant financial or non-financial interests to disclose.
Citation
Long, Y., Yang, C., Li, X., Lu, W., Zhang, Q. and Gao, J. (2023), "Forecasting law enforcement frequency of internet+ coal mine safety supervision", International Journal of Energy Sector Management, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/IJESM-03-2023-0015
Publisher
:Emerald Publishing Limited
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