Impact of urban planning on ecosystem services supply-demand balance: Stormwater retention and recreation in Chiba, Japan
Bibliographic record
Abstract
Sustainable management of ecosystem services is a critical global challenge, as reflected in the Kunming-Montreal Global Biodiversity Framework. However, few studies have analyzed zoning’s impact on ecosystem services supply and demand via land-use areas and composition. Therefore, this study investigates the impact of land-use zoning on ecosystem services supply-demand balance through landscape patterns in Chiba Prefecture, Japan. Using data from various sources, including the Japan Land-use and Land Cover Map, and employing multiple comparison analyses, we examined how different zoning types, including urbanization promotion areas (UPA) and urbanization control areas, affect landscape patterns and ecosystem service supply and demand. The findings revealed that urbanization in UPA and unzoned urban planning areas with use districts (WUD) led to significantly reduced greenspace and increased fragmentation, resulting in decreased stormwater retention supply and heightened demand. Specifically, UPA exhibited the highest fragmentation and lowest greenspace area among the zones, while the demand for stormwater retention outpaced supply in both UPA and WUD. In contrast, recreational services maintained a balanced supply-demand ratio across all zones. This study provides valuable insights for urban planners seeking sustainable practices aligned with global biodiversity targets and emphasizes the importance of policy frameworks to prevent greenspace fragmentation in areas of rapid urbanization. • Urban planning affects ecosystem services supply-demand balance in Chiba. • Zoning impacts stormwater retention supply and demand through greenspace changes. • Recreation services maintain balanced supply-demand across urban and zoned areas. • Findings guide sustainable urban planning aligned with biodiversity goals.
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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 | 0.000 | 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 | 0.001 | 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".