China’s Energy Security: A Review of Current Landscape, Policy Framework and Future Pathways
Bibliographic record
Abstract
Surrounded by the tense and unstable regional situations, China’s pursuit of energy security has become increasingly central to accomplishing its long-term economic and geopolitical policies that aim to achieve consistent and sustainable development. Ever since the Cold War, the international oil price has been highly dependent on ongoing regional disputes, such as the Gulf and Russia-Ukraine wars, soaring global awareness of energy security and further heightening China’s perception of its own energy vulnerability. Therefore, this paper investigates the current landscapes of China and its plans for securing energy supply through qualitative analysis of policies, data, and international cooperation projects. Domestically, China has adopted strategies including technological innovation, industrial upgrading, and energy system diversification to reduce structural vulnerabilities. Externally, to reduce overreliance on specific suppliers, it secured long-term contracts globally and expanded infrastructures along Belt and Road routes. Further, by employing case studies like the China-Russia Energy Partnership, this study explores the benefits and limitations of existing approaches and seeks to develop possible advice to strengthen China’s energy resilience. Ultimately, this article argues that a mixed approach that integrates and balances domestic innovation with strategic international engagement is essential for China’s long-term energy security.
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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 | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| 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".