Analysis on Efficiency and Its Influence Factors of Financial Support for Listed Companies of Guangdong New Energy Industry
Bibliographic record
Abstract
The development of Guangdong new energy industry plays an important role in promoting the current process of economic restructuring in Guangdong province. As the core of modern economy, the financial system also has an important impact on the development of the new energy industry. This paper mainly employs DEA method to measure the efficiency of financial support of the new energy industry in Guangdong Province, and takes it as a core to establish an influence-factor model of financial support efficiency. Through empirical analysis, we can find the stability of Guangdong new energy industry insufficient; on the condition of indirect financing power shortage, the direct financing makes the flow of capital available, thus showing a more positive impact on the efficiency of financial support. According to the results, we should broaden the direct financing way of the new energy industry in Guangdong Province, reduce bank credit financing costs, promote diversified development of financial services, and improve the ability of the new energy industry against market risks.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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 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".