Details zur Publikation |
| Kategorie | Textpublikation |
| Referenztyp | Zeitschriften |
| DOI | 10.1016/j.scs.2026.107909 |
Lizenz  |
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| Titel (primär) | Optimizing urban green space spatial pattern to reduce land surface temperature: A greedy-based adaptive strategy |
| Autor | Dong, X.; Ye, Y.; Yi, S.; Cui, L.; Yang, R.; Zhou, T.; Haase, D.; Lausch, A.
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| Quelle | Sustainable Cities and Society |
| Erscheinungsjahr | 2026 |
| Department | CLE |
| Sprache | englisch |
| Topic | T5 Future Landscapes |
| Supplements | Supplement 1 |
| Keywords | urban green space; land surface temperature; urban heat island; spatial pattern; adaptive decision-making |
| Abstract | Urban
green space (UGS) planning often draws on average relationships between
spatial configuration and land surface temperature (LST), whereas phased
interventions require choices for specific locations and landscape
states. This study compares three predefined greening rules with a
greedy-based adaptive strategy (AS) in Philadelphia. A CNN-XGBoost model
provides a common scenario-evaluation engine; the adaptive decision
procedure selects among centralized, irregular, and decentralized rules
for each planning unit and round. The original 420 m group-based split
yielded a test R² of 0.909. An additional validation excluding
overlapping predictor windows yielded a mean R² of 0.886, providing a
more conservative assessment of within-city prediction. Under the stated
scenario assumptions, AS achieved the greatest cumulative predicted
cooling and the highest average cooling efficiency, 0.159°C per
percentage-point increase in UGS cover. This efficiency exceeded those
of the irregular, decentralized, random-switching, and centralized
strategies by 12.0%, 14.4%, 25.2%, and 57.4%, respectively. In the
reference allocation, 72% of planning units changed their selected rule
at least once over four rounds. Repetition and sensitivity analyses
supported the comparative advantage of AS across the tested settings.
The findings support reassessing a defined set of greening alternatives
as local conditions and implementation stages change, providing a
practical framework for adaptive scenario comparison during phased urban
greening. |
| Dong, X., Ye, Y., Yi, S., Cui, L., Yang, R., Zhou, T., Haase, D., Lausch, A. (2026): Optimizing urban green space spatial pattern to reduce land surface temperature: A greedy-based adaptive strategy Sust. Cities Soc. 10.1016/j.scs.2026.107909 |
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