Forecasting law enforcement frequency of internet+ coal mine safety supervision
-
Published:2023-07-10
Issue:
Volume:
Page:
-
ISSN:1750-6220
-
Container-title:International Journal of Energy Sector Management
-
language:en
-
Short-container-title:IJESM
Author:
Long Yuzhen,Yang Chunli,Li Xiangchun,Lu Weidong,Zhang Qi,Gao Jiaxing
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.
Subject
Strategy and Management,General Energy
Reference83 articles.
1. Introduction to multivariate regression analysis;Hippokratia,2010
2. Artificial intelligence, machine learning and process automation: existing knowledge frontier and way forward for mining sector;Artificial Intelligence Review,2020
3. Prediction of air transportation passenger volume based on multivariate regression model;Aeronautical Computing Technique,2019
4. Research on safety assessment of LPG filling-station based on social network analysis;China Safety Science Journal,2015