Rethinking the semi-periphery: China's impact on global value chains and environment
Bibliographic record
Abstract
The urgency of addressing global warming necessitates rapid carbon emission reductions, a goal increasingly pursued through engagement with global value chains (GVCs). This paper investigates the complex interplay between a country's GVC participation and its impact on the global economic and environmental landscape. Employing a novel assessment framework grounded in a global multi-regional input-output model and counterfactual analysis, we analyze the effects of one country's GVC engagement on global economic and environmental outcomes. Our analytical framework accommodates inter-country differences in price levels and production structures, and it tracks intermediate inputs besides final demands in the global network. Drawing on the world-systems theory, we utilize China—a semi-peripheral nation—as a case study to explore how dynamic GVC participation can exacerbate or alleviate the tension between national and global economic and environmental goals. Our findings demonstrate that China's GVC engagement has been evolving. From 1995 to 2022, China consistently contributed to the reduction in global carbon emissions. Since 2015, however, the impacts of China's GVC participation have diverged considerably, yielding six distinct impact patterns on other countries. The evidence suggests that China is undergoing a transition towards a hybrid semi-peripheral/core status.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".