Environmental Risk and Policy Choices in an Energy Intensive Region of China—An Empirical Study in Shanxi Province

Abstract

Given that there has been ecological degradation for a long time in Shanxi Province, China. This research addresses this situation by analyzing the energy structure and environmental risk in the past and in the future. Consequently, this study uses two indicator systems, ecosystem interaction system and environmental risk representation, to illustrate the environmental risk in an energy intensive region of China. And we build a BP-SVM model applying a back-propagation (BP) neural network and support vector machine (SVM) arithmetic to predict the future ecology-economy-society system interactions in Shanxi Province. At last, we classify the risk rank by using environmental risk representation indicators. The main conclusions from this research are as follows: Firstly, two indicator systems have advantages in describing the ecological-economy-society interaction especially the human society's impact on the ecosystem over a single indicator system. Secondly, by the BP-SVM model, there is a relatively high ecological risk rank in next few years in Shanxi Province, although it fluctuates occasionally. Finally, this study not only offers recommendations for the government to develop policies to transform from a coal-energy based system to a clean, safe, and efficient modern energy system, but also points out the implications for government administrations in energy intensive areas of developing countries to guide the economic transformation. INDEX TERMS Energy intensive region, environmental risk, ecosystem interaction prediction, risk recognition, policy implications.

Extracting Frequent Sequential Patterns of Forest Landscape Dynamics in Fenhe River Basin, Northern China, from Landsat Time Series to Evaluate Landscape Stability

“…Additionally, the middle zone of the Fenhe River Basin is covered by loess soil (with uneven thickness, undulation, and crisscross ravines), which is a typical landform of the Loess Plateau, and has experienced serious soil and water loss. The Fenhe River Basin is one of the largest coal production zones of China [51]. Mining-related activities have damaged the ecological environment, such as causing forest degradation that is difficult to recovery [52].…”

Based on artificial intelligence and digital space technology, research on environmental landscape design of Jinghe art and culture centre

“…*1085626372@qq.com; 15091339567@163.com; 2805004751@qq.com This study aims to conduct in-depth research and discussion on the environmental landscape design of Jinghe Art and Culture Center with the help of artificial intelligence and digital space technology [10][11][12][13][14] . Through the use of advanced technology, we can realize the scientific analysis and prediction of the site topography, climate conditions, spatial layout and other factors, providing an objective and reliable basis for the design [15][16][17][18][19][20] .…”