Bibliographic record
Abstract
Population, resources and environment have always attracted much attention from the society. Nowadays, pollution and population aging are urgent problems to be solved in China, and many scholars have found a strong correlation between pollution and aging. This paper constructs a KAYA model with aging variables, making an empirical analysis of the relationship between population aging and air pollution based on the panel data of 82 cities in China from 2011 to 2016. We found that population aging has a significant and positive impact on air pollution. 1% change of the population aging will lead to a 0.203% change in AQI. The deepening of China’s aging level will lead to ineffective improvement of air quality and even lead to more serious air pollution. Then we make the further analysis of the impact mechanism of population aging on air quality, the results show that population aging could have a positive impact on air pollution by improving labor productivity, promoting technological innovation, increasing fossil energy consumption and the household consumption, and changing the structure of household consumption. At last, in order to improve the air pollution under the background of population aging, we put forward the policy recommendations according to the conclusion of this paper.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".