China's artificial intelligence efficiency and its influencing factors: Based on DEA-Malmquist and Tobit regression model
Bibliographic record
Abstract
The proliferation of artificial intelligence (AI) has emerged as a critical metric for assessing a country's technological advancement, but also for regional economic coordination and high-quality development in China. Based on panel data collected from 31 provinces between 2006 and 2021, this study employs the DEA-Malmquist index model and panel Tobit model to examine the scale, distributional attributes, and influencing factors of AI resource allocation. Results indicate that China's AI resource allocation efficiency has generally increased, with technical efficiency generating a “pull effect” that propels total factor productivity growth rates higher than those attributable to technological progress. Furthermore, AI efficiency in non-coastal regions outstrips that in coastal areas, with total factor productivity growth arising from a substantial increase in technological progress rates. Regional economic development, labor demand, openness to foreign participation, and human capital level exert pivotal roles in enhancing AI resource allocation efficiency. Based on these findings, we suggest a set of strategies aimed at enhancing China's AI resource allocation efficiency, including amplifying government guidance, increasing R&D investments, upgrading economic development levels, fostering the development and strengthening of tangible economy, and attracting and nurturing high-quality scientific research talent.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".