Differential impacts of the US–China trade war and the outbreak of COVID-19 on Chinese air quality
Bibliographic record
Abstract
Purpose Over the last couple of years, the Chinese manufacturing sector was affected by the onset of the US–China trade war and the outbreak of coronavirus disease 2019 (COVID-19). In such a scenario air quality in China has encountered a shock, and the impacts of these two incidents are unknown. In this study, the authors analyze the convergence of air quality in China in the presence of multiple structural breaks and how the impacts of these two events are different from each other. Design/methodology/approach In order to assess the nature of shocks in the presence of multiple structural breaks, unit root tests with multiple structural breaks are employed. Findings The results reveal that air quality in China is showing the sign of convergence, and it is consistent across 18 provinces which are worst hit by the outbreak of COVID-19. In the presence of transitory shocks, the impact of COVID-19 outbreak is found to be higher, whereas the impact of the US–China trade war is found to be more persistent. Lastly, the outbreak of COVID-19 has been found to have more impact on pollutants with higher severity of health hazard. Originality/value To the best of the authors’ knowledge, this is the first study that contributes to the empirical literature in terms of investigating the convergence of overall air pollution and individual air pollutants taking COVID-19 and the trade war into account.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".