Digital Inclusive Finance, Human Capital and Inclusive Green Development—Evidence from China
Bibliographic record
Abstract
To analyze the impact of digital inclusive finance and human capital on inclusive green economic development in China, we build a comprehensive indicator system to measure the level of inclusive green development and use the super-efficiency SBM method to measure the inclusive green total factor productivity (IGTFP) in Chinese cities, then the system GMM model is used to empirically test the direct and interactive influences. Inclusive green development in China has maintained a growing trend in recent years, reaching a peak in 2017. The development of digital inclusive finance in terms of breadth, depth and degree of digitization is conducive to promoting inclusive green development. Although human capital does not directly affect inclusive green development, it plays a significantly positive moderating role in the process of digital inclusive finance promoting inclusive green development. In this paper, the impact of digital inclusive financial and human capital and their interactions on inclusive green development is analyzed within a unified framework, which has important practical significance for the orderly promotion of the development of digital inclusive finance, improving residents’ education level and promoting inclusive green development.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".