Impact of SO2 Emission on the Gross Domestic Product Growth of China
Bibliographic record
Abstract
Applying a structural panel vector autoregression (VAR) model to panel datasets for 108 cities between 2000 and 2015, we evaluate the effect of SO2 on China’s gross domestic product (GDP) growth by calculating the costs associated with this pollutant and its health effects. The results indicate that SO2 emissions promote GDP growth on a national scale but exhibit high regional heterogeneity in terms of cost. Specifically, although the costs exceed 20% in central China, implying that this environmental pollution contributes more than one-fifth of the GDP, and equal approximately 5% in western China, they have already begun to hinder economic growth in the eastern part of the nation. We also find that the health costs total approximately 2%, 3%, and 1% of the GDP per capita for the eastern, central, and western regions, respectively, revealing that rapid economic growth has been achieved at the expense of health.
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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 | 0.003 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".