The empirical research of the correlation between economic growth and air quality in Tianjin, China
Bibliographic record
Abstract
This paper identified the correlation between the economic growth and the air quality in Tianjin, China. To specify, the data on GDP per capita from 2003 to 2015 was used to represent the economic growth. Furthermore, to indicate the air quality, five indexes were selected: the concentration of SO2, NO2, and PM10, ambient air quality bad rate, and the volume of industrial. For converting the five correlated indexes into a set of uncorrelated variables, two main components have been extracted from the five indexes through the principal component method in IBM SPSS Statistics. Next, by Unit Root Test, the rate of increasing the GDP per capita and the air quality have a cointegration relationship and Granger Causality Test in Eviews 9.0. Then, when the speed of GDP per capita is increasing 1 unit, the air condition will be worse 0.0725 units from Johansen Cointegration Test. Finally, from VAR model, the air quality was contributed 10% for the fluctuation of the rate of changing GDP per capita in Tianjin, and the rate of changing GDP per capita was contributed 50% for arising the change of air quality. To suggest, the government of Tianjin should make a balance between controlling the speed of increasing the economy and protecting the air quality.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".