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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".