Investigating drivers impacting vegetation carbon sequestration capacity on the terrestrial environment in 127 Chinese cities
Bibliographic record
Abstract
Vegetation cover significantly improves the terrestrial environment by increasing carbon sequestration capacity. It is projected that a major threat to China's terrestrial environment will be happened by 2030 due to the increment in carbon emissions. Identifying reliable techniques to assess carbon absorption by green coverage is necessary to build a resilient environment. This research examines the performance of two weighted regression models to explain the capacity of vegetation carbon sequestration (VCS), spatial distribution, and degree of influence of vegetation coverage for reducing carbon emission. The results demonstrate changes in the VCS capacity from slow to fast, with an average yearly growth rate of 0.043% (2005–2010) to 1.963% (2010–2015) and more obvious growth in local cities. Variables such as the night-time light index, average relative humidity, and length of sunlight substantially impacted VCS capacity, although their effect varied yearly. Finally, the comparative results show that This study can play an influential role in finding specific locations facing issues with carbon emissions and can support local governments through the association of effective measures to mitigate it.
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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 | 0.003 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".