China’s Economic Growth: Threats and Challenges to Chinese Economy and Energy Security
Bibliographic record
Abstract
Despite global pandemic Chinese economic growth rate was 2.3 percent in 2020. GDP surpassed US $ 15 trillion and growth rate raised to 6.5 percent in fourth quarter of 2020 and US $ 17 trillion GDP was recorded in first quarter of 2021. People Republic China’s (PRC) gigantic military budget and revolution in military affairs (RMA) creates senese of hegemonic ambitions in its neighbours. Contrarily, United States (US) sights PRC has ambitions to expand its political influence, gain access to economic markets, change international order by replacing US. This potential asymmetrical and imbalanced relationship locks America in typical Thucydides trap. Washington reached conclusion that economic growth and military might are intertwined. However, it is dependent on China’s energy supplies. PRC’s rise can be slowed down by stopping or interrupting the flow of energy supplies. Range of threats are posed to PRC oil imports i.e. US aerial strike on PRC oil//gas pipelines, use of proxies specially ast Turkestan Islamic Movement (ETIM) to disrupt oil supplies, terrorist attacks on oil containers on land and naval blockade in Persian Gulf. The inference drawn is energy security dependent on Strait of Malacca is Achilles Heel of China. This paper aims at probing Washington’s capacity to disrupt or stop energy supplies to PRC in Malacca strait, Persian Gulf, land routes in Pakistan. It discusses various strategies including direct naval blockade, use of proxies and direct military strikes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".