TROPOMI NO<sub>2</sub> Shows a Fast Recovery of China’s Economy in the First Quarter of 2023
Bibliographic record
Abstract
On May 5, 2023, the World Health Organization declared that the three-year Coronavirus Disease 2019 pandemic no longer constitutes a public health emergency of international concern. As a major player in international trade, whether China’s economy can quickly recover in the postpandemic era attracts global attention, while we lack direct indicators to track economic dynamics in real-time. Here, we analyze the daily changes in ambient nitrogen dioxide (NO 2 ), a short-lived pollutant released from fuel combustion, to monitor the pace of the economic recovery in China. The satellite-observed tropospheric NO 2 columns from 2005 to 2023 are interpreted with chemical transport model simulations to exclude metrological influences and disentangle the variations caused by anthropogenic sources. Satellites revealed a rapid recovery of NO 2 columns after the Chinese New Year in 2023, the fastest rate ever observed since 2005, especially over the densely populated areas where transport and industrial emission sources are concentrated. These agreed with the fast recovery of China’s industrial production, and the provinces with larger industrial production observed a faster recovery in NO 2 columns than the other provinces. Our study suggests that China’s economy recovered fast in early 2023 and satellite daily NO 2 data provide possibilities to track social-economic dynamics in real-time.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".