Spatio-Temporal evolution and scenario-based optimization of urban ecosystem services supply and Demand: A block-scale study in Xiamen, China
Bibliographic record
Abstract
• Block-scale analysis offers a novel perspective for assessing urban ecosystem services (ES). • Application of the GMOP-PLUS model simulates future land-use patterns and ES dynamics. • Significant spatial imbalances in ecosystem service ES supply and demand are identified in Xiamen. • Actionable strategies are proposed to optimize ES provision and improve green space configuration through urban planning. The imbalance between the supply and demand of urban ecosystem services significantly impacts land resource utilization and residents’ quality of life. This study innovatively examines the spatio-temporal evolution of these services at the block scale in Xiamen, China, from 2012 to 2022, addressing a gap in current research that often focuses on larger scales like watersheds. Using multi-source data, six ecosystem services, including water conservation, carbon sequestration, and habitat quality, were assessed, revealing notable deficiencies. The study also employs the GMOP-PLUS model to simulate land use and ecosystem service changes under three scenarios—Natural development (ND), Economic development (ED), and Ecological low-carbon development (EL)—projected to 2027. Results highlight a significant spatial imbalance with a “North Supply, South Demand” pattern, particularly in southern urban areas. While all scenarios show a decline in green space and ecosystem services, the economic growth scenario improves economic benefits, and the ecological conservation scenario enhances low-carbon and ecological services. This research provides novel insights and optimization strategies for urban land use planning, aiming to enhance ecosystem services and support sustainable urban development.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".