The Multiple Impacts of the COVID-19 Pandemic on China’s Oil Security and the Rising Green Opportunities
Bibliographic record
Abstract
The COVID-19 pandemic has seriously challenged the global oil market, and coronavirus-induced oil prices crash, oil demand decline and global economic recession affect China’s oil supply as well. China has high oil vulnerability due to its rising oil import dependency which aggravates Beijing’s concerns about oil security, despite at a time of the pandemic-induced oil oversupply. This study uses the SWOT analytical model to identify the strengths, weaknesses, opportunities and threats in China’s oil sector, and the changes in opportunities and threats caused by the COVID-19 pandemic. The pandemic has brought multiple impacts to China’s oil security. Results from the analysis show that the existing opportunities such as oil investments in the Belt and Road Initiative (BRI) and domestic upstream opening-up have been weakened; new threats that the uncertainty over global oil demand-supply and decrease in global upstream investments have emerged; opportunities that an increase in domestic strategic petroleum reserve (SPR) and low-carbon development are rising amid the pandemic. Notably, the COVID-19 pandemic has demonstrated the vulnerability of the global oil market to systemic risks and accelerated the transition to renewable energy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".