Abstract
Coal-based energy has provided strong support and made outstanding contributions in the process of China’s economic development. Coal mining in China has gradually developed into intelligent, refined and green mining. However, due to the lack of effective science popularization and propaganda in coal mining for a long time, people’s understanding of coal mining often stays in the stereotype of dirty, messy and very dangerous. Based on this fact, this paper firstly discusses the difficulties and pain points of the popularization of science in coal mining based on the questionnaire survey. And then a VR-AR system for intelligent coal mining was developed. Finally, popular science teaching activities based on VR-AR system were carried out during the “Open Day” activity and “Entering Campus” activity. It is found that the long-term negative reports of coal mining and the complexity of coal mining system make the science popularization and propaganda in coal mining less effective. The proportion of primary and secondary school students with bad impression reached 85.0% and 90.3%, respectively, and 63.1% for college students. With our VR-AR system in coal-based energy education, the impression of the coal industry has increased significantly, the proportion of bad impression decreased to 23.4%. This helps to form the nationwide coal mining science popularization and justifies China’s coal mining.
Funder
National Natural Science Foundation of China
Natural Science Foundation of Beijing
China University of Mining and Technology (Beijing) Teaching Reform Project
Research Fund of Key Laboratory of Deep Coal Resource Mining (CUMT), Ministry of Education
Fundamental Research Funds for the Central Universities
Subject
Management, Monitoring, Policy and Law,Renewable Energy, Sustainability and the Environment,Geography, Planning and Development,Building and Construction
Reference48 articles.
1. Modelling adaptation of the coal industry to sustainability conditions;Suwala;Energy,2008
2. Coal consumption, CO2 emission and economic growth in china: Empirical evidence and policy responses;Bloch;Energy Econ.,2012
3. Forecasting long-run coal price in china: A shifting trend time-series approach;Dong;Rev. Dev. Econ.,2010
4. Wang, D., Xue, X., and Wang, Y. (2021). Overcapacity risk of china’s coal power industry: A comprehensive assessment and driving factors. Sustainability, 13.
5. (2022, January 15). Statistical Review of World Energy 2022, 71th ed. Available online: bp.com/statisticalreview.
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献