An Information Framework for Facilitating Cost Saving of Environmental Impacts in the Coal Mining Industry in South Africa

Research on Early Warning for Gas Risks at a Working Face Based on Association Rule Mining

In the process of gas prediction and early warning, outliers in the data series are often discarded. There is also a likelihood of missing key information in the analysis process. To this end, this paper proposes an early warning model of coal face gas multifactor coupling relationship analysis. The model contains the k-means algorithm based on initial cluster center optimization and an Apriori algorithm based on weight optimization. Optimizing the initial cluster center of all data is achieved using the cluster center of the preorder data subset, so as to optimize the k-means algorithm. The optimized algorithm is used to filter out the outliers in the collected data set to obtain the data set of outliers. Then, the Apriori algorithm is optimized so that it can identify more important information that appears less frequently in the events. It is also used to mine and analyze the association rules of abnormal values and obtain interesting association rule events among the gas outliers in different dimensions. Finally, four warning levels of gas risk are set according to different confidence intervals, the truth and reliable warning results are obtained. By mining association rules between abnormal data in different dimensions, the validity and effectiveness of the gas early warning model proposed in this paper are verified. Realizing the classification of early warning of gas risks has important practical significance for improving the safety of coal mines.

Enhancing a Decision-Making Framework to Address Environmental Impacts of the South African Coalmining Industry

The South African coalmining industry has a rich and long history and contributes significantly to the economic wellbeing of the country. Despite its importance in developing the economy, the industry is causing severe environmental challenges. For example, Emalahleni, a city situated in the Mpumalanga Province in South Africa, has been exposed for over a century to the continuous mining of coal. Challenges experienced include the sterilisation of land due to underground fires, water pollution, surface collapse, and acidification of topsoil. Previous work by the researchers formulated a conceptual framework aimed at addressing some of these challenges. In an extension of this work, the authors comprehensively enhance the preliminary framework on the strength of a set of qualitative propositions coupled with a parallel, exploratory survey. Interviews among various stakeholders were conducted, aimed at enhancing the components of the framework, followed by a focus group to validate the associations among the components of the framework. Aspects reinforced by the survey findings include the role of environmental management accounting, tools like material-flow cost accounting and life-cycle costing, and regulatory and accountability aspects. New aspects elicited from the interviews and the focus group include stakeholder education and training with respect to the value of environmental management accounting for the coalmining industry; adherence to risk management linked to environmental challenges; advanced technologies, for example, financial modelling; and an improved understanding of waste management aspects around acid mine drainage, volatile organic components, CO2 emissions, and post-mine closure. The novelty of the work lies in the approach taken to address coalmining challenges. Previous authors concentrated mostly on scientific and engineering aspects, while this research looks at it from an accounting perspective using environmental management accounting tools to address these challenges.

IT2 Fuzzy-Based Multidimensional Evaluation of Coal Energy for Sustainable Economic Development

The aim of this study is to determine the impact of coal energy on the economic development of countries. For this purpose, similar studies in the literature have been examined and nine different criteria have been determined for three dimensions affecting the use of coal energy. In order to determine the most important factors, an analysis is performed with IT2 fuzzy DANP method. The most important contribution of this study to the literature is that a proposal can be made for the use of coal energy by considering both positive and negative opinions related to this energy. On the other hand, IT2 fuzzy DANP method has been taken into consideration in this study for the first-time regarding coal energy and it is believed that methodological originality has been achieved. It is identified that social factors have the most importance in the use of coal energy. In this context, environmental pollution, health problems and demographic factors resulting from the use of coal energy should be taken into consideration in the use of this energy. Thus, problems arising from the use of coal energy far outweigh the economic benefits of using this energy. Therefore, factors that may prevent air pollution, such as carbon capture technology, should be considered in the use of coal energy. In addition, the use of high-quality coal will contribute to the reduction of the problems caused with this energy. In addition, investing in renewable energy sources that do not have negative impacts on the environment is also important for the sustainability of future energy policies.