The impact of the EU carbon border adjustment mechanism on China based on the climate club
Bibliographic record
Abstract
This study analyzes the EU Carbon Border Adjustment Mechanism (CBAM)’s implications for China’s trade, GDP, and carbon emissions under evolving global climate governance frameworks. Combining climate club theory with recent policies from the U.S., U.K., Japan, and Canada, it proposes a potential multi-climate club coexistence model. Using the GTAP-E model, the research quantifies CBAM’s effects under two scenarios: a single EU-led climate club and a multi-club system. Key findings reveal that while China’s short-term export reductions to the EU remain marginal (<1% across industries), long-term trade diversion intensifies, with 30% of cement exports projected to shift to non-EU markets by 2034. Multi-club cooperation exacerbates GDP growth challenges for China compared to a single-club scenario but amplifies carbon reduction incentives, particularly if CBAM expands to cover indirect emissions. High-energy sectors (e.g., cement, chemicals) emerge as most vulnerable. The study underscores CBAM’s dual role as both a trade barrier and a catalyst for industrial decarbonization. Recommendations emphasize strengthening China’s carbon pricing mechanisms, proactive engagement in multilateral climate negotiations, and targeted support for energy-intensive industries. These insights highlight the urgency of adaptive strategies to reconcile economic resilience with climate obligations amid fragmented global governance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".