Linking Relationship of Ecosystem Service Supply and Demand Into Sustainable Development Goals ( <scp>SDGs</scp> )
Bibliographic record
Abstract
ABSTRACT Ecosystem services (ES) play a crucial role in sustainable development. We systematically assessed the spatial and temporal dynamics of ecosystem service supply (ES‐s) and demand (ES‐d) in China from 1990 to 2020 by using a supply–demand matrix integrated with land use/cover data. The findings indicate that in 2020, grasslands accounted for 28.02% of the land area. Meanwhile, forest land expanded to 23.99% of the total area, primarily composed of close ‐ canopy forests. ES‐s remains relatively stable and high in the eastern coastal regions, whereas the western and northeastern regions face persistent supply shortages. Rapid economic growth and urbanization have driven a substantial increase in ES‐d in the eastern and southern regions, exacerbating supply–demand imbalances. Furthermore, the study highlights the critical role of different land use types—such as forests, cultivated land, and construction land—in supporting multiple Sustainable Development Goals (SDGs). Based on these findings, we propose targeted policy recommendations to mitigate regional disparities, including the implementation of ecological compensation mechanisms, the development of green infrastructure, and enhanced cross‐regional cooperation. These measures aim to restore the ES‐s and ES‐d balance and advance SDGs progress.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".