Dynamic Assessment on Sustainable Development Capacity of Mining Cities by BP Neural Network

In this paper, we focused on the dynamic evaluation of sustainable development capacity for mining cities in China. The indicator system of sustainable development capacity for mining cities was constructed from the aspects of resources, environment, economy and social development. BP neural network method was adopted to dynamically predict the capacity for sustainable development system of Panjin City. The dynamic results indicated that of factors influencing the sustainability in the future, economic level, mineral resources production and degree of coordination between industry and environment are very strong. Insufficient educational investment will dissatisfy the requirements of industrial transformation and socio-economic development. Furthermore, the promotion of optimization and upgrading of industrial structure in mining cities should base on the characteristics of resources and regional economy, meanwhile, mining cities should develop clean energy and improve investment environment. This paper laid an experience for the application of BP neural network model in socio-economic research field.