Quantitative Research on Trade in Value-Added and Emissions Responsibility in Global Value Chain
Bibliographic record
Abstract
The obvious defects of traditional trade statistics methods are shown under the circumstances of international production fragmentation and highly integrated network, and the related problems of greenhouse gas emissions during the value-added process of products are also being focused. How to use qualitative research to measure the residual greenhouse gas in the global industrial chain during the value-added process of products in exporting countries is the key point of this research. This paper summarizes the characteristics of value-added industrial development through sorting out the relevant data of the value-added conditions of global value chain, based on the latest released TiVA database of OECD. Then, the paper selects five time nodes of 12 countries, quantifies the responsibilities of the remains of greenhouse gases in exporting countries, based on value-added part under the trade transaction, and further quantifies the corresponding responsibilities of the emission of greenhouse gases in consumer countries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".