The Impact of the Digital Economy on Carbon Emissions: Evidence From Machine Learning, Graph Neural Networks, and the EKC Hypothesis
Bibliographic record
Abstract
This study utilizes panel data from 30 Chinese provinces spanning 2007 to 2023 and integrates machine learning and graph neural network (GNN) approaches to examine the spatial dynamics of carbon emissions. It aims to systematically evaluate the impact pathways of the digital economy on carbon intensity and to uncover its spatial diffusion patterns and regional heterogeneity. The empirical findings are threefold. First, the digital economy significantly reduces carbon intensity, consistent with the Environmental Kuznets Curve (EKC) hypothesis, and this effect exhibits clear heterogeneity across economic development levels and regions. Second, due to the existence of spatial spillover effects, GNN models outperform traditional machine learning methods in carbon emission prediction tasks. Third, carbon intensity displays strong temporal inertia and negative spatial spillovers across regions. Notably, spatial diffusion capacity and sensitivity to the digital economy vary substantially: central and western regions exhibit stronger spillover effects, northeastern provinces show more pronounced internal feedback mechanisms, while eastern coastal areas demonstrate relatively weaker effects. Overall, this study expands the analytical perspective on the digital economy's role in carbon mitigation and provides theoretical and empirical support for the design of differentiated emission reduction policies and coordinated regional governance.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".