Assessing Risk in Chinese Shale Gas Investments Abroad: Modelling and Policy Recommendations
Bibliographic record
Abstract
As the shale gas revolution expands globally, the future potential and economic profits of overseas shale gas reserves have attracted the interest of Chinese investors. Overseas shale gas development is becoming an investment hotspot for Chinese oil companies. However, this multibillion-dollar venture is surrounded by a complex and uncertain environment. Therefore, this paper carries out an integrated and publicly available model for assessing risk in overseas shale gas investments. The purpose of this model is to address the index weight calculation and risk ranking and provide investor with risk information. In view of this, the comprehensive weights are obtained based on an analytic hierarchy process (AHP) and entropy weight methods; and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is performed to rank target countries. First, the paper identities five categories of risks with full consideration of the economic risk, political risk, geological risk, technological risk, and internal managements risk. Based on the risk identification, the assessment index system is established and valued. Secondly, China is taken as an example nation to use this model to prove the effectiveness of the proposed model and help the investor make wise decisions. According to the results, low-risk countries, such as Canada, Argentina, United States, and Algeria can be considered to be future key targets of shale gas investment abroad, while investors should be more cautious of high-risk countries such as South Africa and Brazil. Finally, policy recommendations are proposed to optimize the overseas shale gas investments from both the government and investor perspectives.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".