Using BP Neural Networks to Prioritize Risk Management

Today, my latest article was published in the open access journal Sustainability. The article — Using BP Neural Networks to Prioritize Risk Management Approaches for China’s Unconventional Shale Gas Industry — was co-authored by Cong (Cindy) Dong (a Ph.D. student at China University of Petroleum School of Business Administration, currently visiting me at the University of Alberta), Xiucheng Dong (China University of Petroleum, School of Business Administration), Joel Gehman (University of Alberta School of Business), and Lianne M. Lefsrud (University of Alberta, Department of Chemical and Materials Engineering).

China has become the top energy consumer in the world. At the same time, China is facing intense international and domestic pressure to reduce the greenhouse gas and other emissions resulting from its primarily coal-based energy system. Given these twin pressures of increasing energy demand while controlling emissions, the development of China’s shale gas industry has emerged as a strategic national priority.The shale gas resource distribution in China is illustrated in Figure 1. Seven provinces—Sichuan, Xinjiang, Chongqing, Guizhou, Hunan, Hubei and Shanxi—account for 68.9% of the nation’s total reserves.

Figure 1. Shale gas resource potential in China’s provinces (trillions of m3).

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