China And America’s Trade War on Clean Energy Revolution: Taking Electric Vehicle Industry as An Example
Bibliographic record
Abstract
This paper is based on several backgrounds. The first one is the increasingly severe global climate crisis and the imperative global green energy revolution. The second one is China’s ambitious Belt and Road Initiative which has been put into efforts since 2013 in order to “create more opportunities for common development on the principle of extensive consultation and joint contribution and for the commonwealth of the world” (proposed by Xi Jinping, on a meeting with Emmanuel Macron and Angela Merkel on July 15, 2021) and America’s countermeasures against China’s economic movement. The research focuses on the electric vehicle industry area that clearly shows the tendency of global green energy revolution, the innovations and the complex geopolitical situation and conflicts of countries on this rising industry. To get into the topic, 2 specific examples: The cobalt (a vital resource in the EV industry) mining industry in the Democratic Republic of Congo (DRC), and Tesla’s global strategy and its factory in Shanghai. This research will also talk about the orientation of the new technologies in the electric vehicle industry and how it is going to reshape the industry and their geopolitical impact on the world. This research mainly uses the method of literature research. The paper focuses on collecting and analyzing the facts and data of the industry, different perspectives and viewpoints from different stakeholders (mainly the US and China) and the developing situation of the geopolitical environment in the nowadays world.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".