The divergent effects of spatial structure of urban agglomerations on carbon emission reduction capacity
Bibliographic record
Abstract
The adjustment of the spatial structure of urban agglomerations is an effective tool for improving carbon emission reduction. Given the complexity of sustainable environment and carbon emission reduction, urban low-carbon transformation faces challenges such as population agglomeration, factor mobility, and urban expansion. This study constructs a carbon emission reduction capacity indicator system and adopts Zipf's law to measure the centrality index of the spatial structure of urban agglomerations. The influence of spatial structure on carbon emission reduction capacity is verified using five Chinese national urban agglomerations in the Yellow River Basin. The results show that urban agglomerations are dominated by a polycentric spatial structure, with strong carbon emission reduction capacity in the middle and lower reaches and weak capacity in the upper reaches. Carbon emission reduction capacity presents an inverted U-shaped relationship with spatial structure, and functional specialization presents nonlinear mediating effects. Carbon emission reduction effects can be maximized through rational functional specialization of the spatial structure. The moderation of the increase in factor mobility shows a weakening effect. Thus, a moderately polycentric spatial structure of urban agglomerations should be optimized to promote production factor mobility through sustainable governance. Reasonable urban functional positioning and the coordinated management of carbon emission reduction among cities should be developed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".