Chinese urbanization promoted terrestrial ecosystem health by implementing high‐quality development and ecological management
Bibliographic record
Abstract
Abstract High‐quality urbanization and a healthy ecosystem are both the material basis for sustainable social development. However, the tie between terrestrial ecosystem health (TEH) and urbanization is still unclear. Therefore, we assessed the spatial and temporal dynamics of urbanization and TEH at 368 cities in China from 2000 to 2020, then explored their spatial interaction and driving mechanisms by spatial autocorrelation analysis and structural equation modeling. The results showed: (1) China's comprehensive urbanization index (UI) improved from 0.08 in 2000 to 0.10 in 2020, contributing by some national urban agglomerations such as Beijing‐Tianjin‐Hebei, Yangtze River Delta, and Pearl River Delta. (2) China's terrestrial ecosystem health index (EHI) also increased from 0.67 to 0.68. Ecosystem vigor improved significantly, while ecosystem organization and resilience both decreased. (3) EHI and UI appeared to be locally spatially dependent, and path dependence was presented at municipal scales. (4) At the national scale, urbanization positively relates to EHI, which was enhanced by social, economic, and topography factors. The dominant drivers on EHI varied among regions, and urbanization improved EHI in all regions except for the southwest. Our study demonstrated that urbanization would promote TEH by implementing high‐quality development and ecological management simultaneously, providing theoretical support for urban sustainable development and ecological management.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".