Research on the Impact of Digital Trade on Urban Carbon Emissions in China under the "Dual Carbon" Target
Bibliographic record
Abstract
After the "dual carbon" goal was put forward, low-carbon development has gradually become the focus of attention from all walks of life, and digital technology plays an important role in the process of helping carbon emission reduction. Digital trade, as an active field in international economy and trade, is of great significance to the economic growth of all countries and to deal with the problem of carbon emission reduction. This paper analyzes the transmission path of digital trade on urban carbon emission reduction in China from the theory of scale effect, structure effect and technological progress effect, and based on the panel data of 280 cities in China from 2011 to 2019, uses two-way fixed effect model to conduct regression analysis on the carbon emission reduction effect of digital trade. The study finds that China's digital trade development reduces urban carbon emission intensity, and the conclusion is still valid after the robustness test. Heterogeneity analyses show that the carbon emission reduction effect of digital trade varies in different regions in the East, West and Centre, as well as in regions with different degrees of carbon emissions. In terms of the transmission path, digital trade reduces regional carbon emission intensity by promoting scale effects, technological progress and industrial structure upgrading. Therefore, on the road of actively realizing the goal of "dual carbon", China should vigorously promote the development of international trade mode mainly represented by digital trade. At the same time, we fully recognize the heterogeneity of the impact of digital trade on carbon emissions, and adopt differentiated policies according to the different utility levels of digital trade on carbon emissions in different regions. This will narrow the "digital divide" between cities and help achieve China's "dual carbon" goal.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 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".