TROPOMI NO<sub>2</sub> Shows a Fast Recovery of China’s Economy in the First Quarter of 2023
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it