Impact of land cover spatial patterns on urban CO₂ emissions: Evidence from China
Bibliographic record
Abstract
Dramatic changes in land cover impact spatial patterns of carbon sources and sinks in cities, which can potentially exacerbate urban CO₂ emissions, further threatening ecosystems, human health and economic development. While previous studies have explored the relationship between spatial patterns of land cover and carbon emissions, most of them focused on a single type of land cover or neglected the spatial heterogeneity. This study introduced an innovative approach to effectively leverage land cover spatial patterns for reducing CO₂ emissions. Based on the data across 304 Chinese cities in 2005, 2010, 2015 and 2020, the relationships between different land covers (cropland, forest, grassland and impervious) and CO₂ emissions were explored by multiscale geographically weighted regression (MGWR) model. Our analysis revealed that the growth rate of total CO₂ emissions decreased from 45% to 5% between 2005 and 2020, with the Beijing-Tianjin-Hebei and Yangtze River Delta regions being high-emission areas. The area of cropland had a double-edged effect on CO₂ emissions. Expanding the area of forest or fostering uniform distribution of land cover types contributed to CO₂ emission mitigation. The spatial hierarchy and complexity of grassland needed to increase. Impervious ground surface required the control of its expansion rate.These findings offer new insights into urban carbon reduction through comprehensive land use planning, providing an actionable strategy to optimize the spatial arrangement of land cover types.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".