Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data
Bibliographic record
Abstract
The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China. A timely assessment of COVID-19’s impact on provincial CO emission reductions is crucial for accurately understanding the degree of reduction and its implications for mitigation measures. Here, we used gross domestic product (GDP) and an inventory (CEADs) to estimate the reductions in the first quarter (Q1) of 2020. We find a reduction of -257.7 Mt CO (-11.0%) over 2019 Q1. Secondary industry contributed 72.5% of the total reduction, due largely to lower coal consumption and cement reduction. At the provincial level, Hubei contributed the most to reductions. Transportation reduction also made a significant contribution. One policy implication is advocating working from home and holding teleconferences to reduce traffic emissions. We provide provincial reductions as spatial constraints for modeling studies and further support for both the carbon cycling scientists and policy makers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".