The Interplay and Feedback Mechanism between Environmental Pollution and Economic Growth in China
Bibliographic record
Abstract
China’s rapid economic growth has given rise to serious clashes with environment protection needs. In order to address the dilemma between environment protection and economic growth, a thorough investigation into the relationship between the two is fundamental to the formulation and implementation of environment policies. This paper uses econometric analyses of time-series data on industrial pollution and GDP per capita between 1980 and 2008, examines the long-term dynamic equilibrium and interplay between environment quality and per capita income in China, and obtains the following points. First, there is cointegration between economic growth and environment pollution in China. Second, the increase of per capita GDP has evident lag effects on environment quality. Third, the simulated structure of Generalized Impulse Response Function shows that the responses of environment quality indicators to the impulse of per capita growth rate are mostly fluctuant. Fourth, the results of variance decomposition show that the contribution of pollution indicators to the variance decomposition of GDP per capita is relatively small.
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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 | 0.002 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".