Impacts of Urban Shrinkage on Haze Pollution-Evidence from China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study focuses on 55 shrinking cities selected by the urban shrinkage index using data about the urban population of 250 prefecture-level Chinese cities from 2012 to 2017. It analyzes the theoretical impacts of urban shrinkage on haze pollution and the spatial distribution and autocorrelation of urban shrinkage. The spatial error model (SEM) and the fully modified least squares (FMOLSs) regression are used to empirically examine the impacts of urban shrinkage on haze pollution at national and regional levels. The results indicate that shrinking cities showed spatial agglomeration and that northeast China had the largest number of shrinking cities. Nationwide, urban shrinkage reduced haze pollution. An increase in the proportion of secondary industries, economic development, and built-up areas intensified haze pollution, while an increase in the green area in parks alleviated such pollution. Regionally, except for west China, the impacts of urban shrinkage on haze pollution were significantly negative. Urban shrinkage in central China had the greatest impacts on haze, followed by northeast China and east China. Haze pollution was intensified by the increase in the proportion of secondary industries in east, central and west China, alleviated by economic development in east and west China, slowed down by the increase in green area in parks in northeast, east and west China, and aggravated by the rise in built-up areas in northeast, central, and west China. Targeted suggestions are proposed herein to reduce haze pollution, adapt to urban shrinkage and build quality small cities based on local conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 it