Table 1.

Summary of key literature on SUHI mitigation.

Author(s) (Year)	Methods	Key Findings	Gaps Addressed in the Present Study
Zhao et al. (2014)6	Observational data analysis	Local climate strongly influences SUHI, with background effects contributing up to 50%.	Justifies multi-factor drivers (e.g., climate, land use) in our geographic detector model.
Manoli et al. (2019)5	Climate-ecosystem modelling	Humidity limits evaporative cooling in humid climates, bounding SUHI intensity.	Humidity limits evaporative cooling in humid climates, bounding SUHI intensity.
Richards et al. (2020)18	Vegetation type cooling analysis	Multi-layered vegetation outperforms grasslands in the tropics.	Emphasises structural complexity in our cooling-source identification.
Liu et al. (2024)7	Circuit theory for cold islands	Networks reduce temperatures by 1–3 °C; validated in 50 + cities.	Extends to plain cities like Nanchang; validates transferability in Singapore.
Li et al. (2024)20	Multi-level ecological networks	Connected greenspaces amplify cooling within 200–500 m.	Builds on for Nanchang’s fragmented spaces; adds MSPA for pattern analysis.
Yuan et al. (2025)4	Satellite-based global analysis	SUHI intensifies faster in low-income countries (0.021 °C/year increase) due to impervious expansion.	Provides global context for Nanchang’s rapid urbanisation; the framework offers network-based solutions for such regions.
Ching et al. (2025)15	Sensor-based park cool island study	Tropical parks cool by 2.2 °C during the day; intensity varies with morphology.	Supports validation in Singapore; informs our corridor simulation.
Evangelisti et al. (2025)11	In-situ green roof performance	Green roofs reduce energy use in Mediterranean climates.	Complements NbS discussion; suggests vertical integration for dense areas.
Fiorentin et al. (2026)12	Green roof sustainability assessment	Environmental benefits include SUHI mitigation and biodiversity.	Enhances NbS role; addresses gaps in compact urban strategies.