China's Provincial Digital Economy and Carbon Emissions: A Spatio-Temporal Analysis During 2013–2019
Bibliographic record
Abstract
Today, with the rapid development of the digital economy, China is also working with the world to deal with the huge challenges brought about by the deteriorating climate environment. In the context of the "dual carbon" development goal, the digital economy is in a new era of integration with environmental protection and other fields. Studying how the development of the digital economy has an impact on carbon emissions is the key to solving the current coordinated development of the economy and the environment. This paper firstly takes 30 provinces in China (except Tibet, Hong Kong, Taiwan, and Macau due to data unavailability) as the research object, and takes 2013-2019 as the research time to construct an index system for measuring the development level of digital economy, and calculates the level of digital economy development at different time points in each region based on the entropy weight TOPSIS method. It is found that the average annual development level of the digital economy is the eastern coastal area, the central area, the northeast area and the western area from high to low. Then, considering that China's carbon emissions and digital economy development have certain spatial patterns, and at the same time doing a spatial autocorrelation test based on Moran's I, geographically and temporally weighted regression (GTWR) model with the dependent variable being carbon emission intensity and the independent variables being digital economy development level, population, urbanization rate, secondary production ratio, industrial solid waste utilization rate and per capita GDP was established. Finally, we obtained that the temporal and spatial evolution of the influence has weakened from the promotion in the west to the surrounding areas to the inhibition in the eastern coastal and northeastern regions; the overall promotion decreased and the inhibition increased with time.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".