The Impact of Environmental Regulations, Corruption and Economic Freedom on Economic Growth: Empirical Evidence from China
Bibliographic record
Abstract
The purpose of this study is to investigate the impact of environmental regulations, corruption and economic freedom on economic growth in China. Different indices were used as measurements of the variables; Environmental Policy Stringency Index, Control of Corruption Index and Economic Freedom of the World Index. The study uses quantitative methods to empirically determine which factors play a role in China’s progressive economic growth rates. Unit root test, Johansen cointegration and the Autoregressive Distributed Lag (ARDL) modelling were applied to examine the short and long run correlations. Results indicated that there is in fact a correlation between environmental regulations, corruption, economic freedom and economic growth. Long run coefficients demonstrated that environmental regulations had a negative impact on economic growth, while corruption and economic freedom displayed positive results. However, short run coefficients showed that environmental regulation is insignificant in the short run, corruption maintains a positive impact and economic freedom negatively effects economic growth in the short run.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.001 | 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 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".