Impact of the tradeoff between urbanization and ecological construction on the supply and demand balance of carbon sequestration services in China
Bibliographic record
Abstract
Clarifying the spatiotemporal distribution characteristics of the supply-demand balance of ecosystem carbon sequestration services and the impact of human activities and natural factors on it is of great value for exploring nature-based carbon neutrality solutions. This study quantified and mapped the supply and demand of carbon sequestration services in China in 2001, 2010, and 2019, clarified the spatiotemporal distribution pattern of the supply-demand balance of carbon sequestration services in terrestrial ecosystems, identified the tradeoff relationship between urbanization and ecological construction, and analyzed its impact on the supply-demand balance of carbon sequestration services. Results indicate that China’s demand and supply for ecological carbon sequestration services increased gradually from 2001 to 2019. Spatial mismatches are becoming more pronounced as the supply-demand balance for carbon sequestration services is weakening across all regions. Urbanization and ecological construction have a mostly synergistic relationship, but the strength of synergy progressively declines. The balance of carbon sequestration services supply and demand is affected by the tradeoffs between ecological construction and urbanization. The research results provide s a theoretical reference for formulating differentiated emission reduction and sink enhancement policies and promoting the coordinated development of human-environment relations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".