Coal Resource Security Assessment in China: A Study Using Entropy-Weight-Based TOPSIS and BP Neural Network

Abstract

Energy security has become a worldwide issue in recent years. Coal resources security (CRS), an important part of energy security, has been an emerging concern in many countries, due to the diminishing fossil energy reserve and unbalanced energy structure. However, there is no universally agreed method of constructing indicator system for CRS assessment. Subjectivity in the process of evaluation also affects the results of assessment. Moreover, CRS is a complex system that should be evaluated scientifically under diverse methods. Therefore, we constructed an indicator system and evaluation model of CRS and used a case study of China and 31 provinces in its mainland to evaluate CRS at both national and provincial levels. The indicator system included two subsystems—long-term CRS and short-term CRS. We also chose a few elements and factors that are consistent with China’s reality. Different research methods were used: the entropy-weight-based TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method is applied to evaluate the degree of CRS, which avoids the subjectivity of weight determination and reflects the relative merit of each indicator; the BP (Back-Propagation) Neural Network method is used to analyze the sensitivity of CRS to each index. The results show that the national level of CRS dropped in the early years but slowly picked up with the help of government intervention. Investment in coal industry development resulted in the immediate effect of improving CRS. The positive impact of maintaining environmental sustainability is stable over either the short, medium, or long term. The degrees of CRS vary significantly across provinces, even between those with similar coal stock levels. Extra attention should be paid to the transportation of coal resources among provinces and intervention to balance supply and demand within the regions.