Research on the Influence of Enterprise’s Digital Transformation on Carbon Emission Intensity——A Moderated Mediation Model
Bibliographic record
Abstract
Digitalization and low-carbon development are parallel in the digital economy. In what ways does the development of low-carbon economy can be contributed by the digital transformation of enterprises? This paper analyzes the impact of digital transformation on carbon emissions from a micro perspective. Based on unbalanced panel data from 278 listed companies between 2010 and 2018, a double fixed-effect model is used to test the overall effects of digitalization on carbon emission intensity, the intermediary role of green technology innovation capacity and the regulatory effect of absorptive capacity. Results show that digital transformation of enterprises contributes to the reduction of carbon emission intensity, green technology innovation capability partially mediates the impact of digital transformation on carbon emission intensity, and absorptive capacity moderates the impact of digital transformation on green technology innovation capacity. Based above, this paper puts forward corresponding countermeasures from the aspects of digitalization, greening and low carbon.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".