China's Trade‐Off Between Economic Benefits and Sulfur Dioxide Emissions in Changing Global Trade
Bibliographic record
Abstract
Abstract China has been suffering from air quality degradation since its ascension into the World Trade Organization in 2001. The unequal exchange that occurs with international trade—that is, developed countries obtaining larger shares of trade‐related value added relative to the shares of trade‐related air pollution incurred locally—may obstruct the greening of global supply chains. In this study, we conduct a multi‐regional input‐output analysis to examine the change in the distribution of economic benefits and sulfur dioxide emissions underlying China's international trade from 2002 to 2015. The results show that both net trade‐related economic benefits and SO 2 emissions in China rapidly increased from 2002 to 2007 and then decelerated after 2007 due to changes in China's green development strategy. In the past 13 years, China has suffered from economic‐environmental inequality due to trade with most developed countries, for example, the United States, the European Union, East Asia, and Canada. East Asia, particularly Japan and South Korea, became both an economic and environmental winner while trading with China in 2015. China has also outsourced emissions to less developed regions, such as Sub‐Saharan Africa. We propose policy implications to further reduce the economic‐environmental inequality underlying China's international trade.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".