Using BP Neural Networks to Prioritize Risk Management Approaches for China's Unconventional Shale Gas Industry

18 Pages Posted: 7 Jun 2017 Last revised: 19 Sep 2017

See all articles by Cong Dong

Cong Dong

China University of Petroleum, Beijng

Xiucheng Dong

China University of Petroleum, Beijng

Joel Gehman

University of Alberta - Department of Strategic Management and Organization

Lianne Lefsrud

University of Alberta - Faculty of Engineering

Date Written: June 7, 2017

Abstract

This article is motivated by a conundrum: How can shale gas development be encouraged and managed without complete knowledge of the associated risks? To answer this question, we used back propagation (BP) neural networks and expert scoring to quantify the relative risks of shale gas development across 12 provinces in China. The results show that the model performs well with high predictive accuracy. Shale gas development risks in the provinces of Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu are relatively high (0.4~0.6), while risks in the provinces of Xinjiang, Guizhou, Yunnan, Anhui, Hunan, Inner Mongolia, and Shanxi are even higher (0.6~1). We make several recommendations based on our findings. First, the Chinese government should promote shale gas development in Sichuan, Chongqing, Shaanxi, Hubei, and Jiangsu Provinces, while considering environmental, health, and safety risks by using demonstration zones to test new technologies and tailor China’s regulatory structures to each province. Second, China’s extremely complex geological conditions and resource depths prevent direct application of North American technologies and techniques. We recommend using a risk analysis prioritization method, such as BP neural networks, so that policymakers can quantify the relative risks posed by shale gas development to optimize the allocation of resources, technology and infrastructure development to minimize resource, economic, technical, and environmental risks. Third, other shale gas industry developments emphasize the challenges of including the many parties with different, often conflicting expectations. Government and enterprises must collaboratively collect and share information, develop risk assessments, and consider risk management alternatives to support science-based decision-making with the diverse parties.

Keywords: shale gas, risk assessment, BP neutral networks, environmental impacts

JEL Classification: D81, Q4, N5, O13, M14, A14

Suggested Citation

Dong, Cong and Dong, Xiucheng and Gehman, Joel and Lefsrud, Lianne, Using BP Neural Networks to Prioritize Risk Management Approaches for China's Unconventional Shale Gas Industry (June 7, 2017). Sustainability, Forthcoming; University of Alberta School of Business Research Paper No. 2982421. Available at SSRN: https://ssrn.com/abstract=2982421

Cong Dong

China University of Petroleum, Beijng ( email )

18 Fuxue Road
Beijing, 102249
China

Xiucheng Dong

China University of Petroleum, Beijng ( email )

18 Fuxue Road
Beijing, 102249
China

Joel Gehman (Contact Author)

University of Alberta - Department of Strategic Management and Organization ( email )

Edmonton, Alberta T6G 2R6
Canada
(780) 248-5855 (Phone)

HOME PAGE: http://www.joelgehman.com

Lianne Lefsrud

University of Alberta - Faculty of Engineering ( email )

Edmonton, Alberta T6G 2R3
Canada

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