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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".