The Chinese Government Auditing and Green Finance: The Mediating Role of Fiscal Execution Efficiency
Bibliographic record
Abstract
Amidst the dual constraints of insufficient resource supply and an imperfect institutional environment hindering the development of green finance in China, the Chinese government has actively advanced reforms to its government audit system to enhance fiscal execution efficiency. This study utilizes panel data covering 30 provinces in China from 2010 to 2021 and employs Hausman regression to assess the impact of Chinese government auditing on green finance. Furthermore, it empirically examines the mediating effects of fiscal revenue and expenditure execution efficiency on the relationship between the two. The empirical results indicate that Chinese government auditing has a significant impact on enhancing the development level of green finance. However, fiscal expenditure execution efficiency exhibits a significant full mediating effect. Correspondingly, the role of fiscal revenue execution efficiency is limited, exhibiting only a partial mediating effect. These findings highlight the institutional advantages of Chinese government auditing in promoting the development of green finance by improving fiscal execution efficiency. This study integrates government auditing, green finance, and fiscal execution efficiency into a unified analytical framework, enriching the theoretical system of green finance driving mechanisms. The research results also provide policy support for local governments in China to enhance their auditing models and strengthen fiscal execution capabilities, thereby improving the overall effectiveness of green finance development.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".