Using BP Neural Networks to Prioritize Risk Management Approaches for China’s Unconventional Shale Gas Industry
Bibliographic record
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".