Abstract
Global warming and rapid urbanization have exacerbated the urban heat island effect. Urban parks contribute to alleviating such an effect and achieving the “carbon emission peak before 2030” and “carbon neutrality before 2060” goals of China. Their popularity is considerably influenced by human thermal comfort. However, limited thermal comfort studies have been conducted in the hot-summer and cold-winter region of China. This study examines human thermal comfort in different landscapes of an urban park in Chengdu and determines the thermal benchmarks. A machine learning (random forest) analysis shows that human thermal sensation is affected by different meteorological factors in different seasons. In addition, the influences of landscape space on human thermal comfort have considerable differences in different seasons. Residents prefer strong solar radiation in winter but fast wind speed in summer. UTCI (universal thermal climate index) is better than PET (physiological equivalent temperature) for outdoor thermal comfort assessment in the study area. This study serves as a valuable baseline and technical reference, contributing to sustainable urban park design.
Original language | English |
---|---|
Article number | 103535 |
Journal | Sustainable Cities and Society |
Volume | 77 |
Early online date | 10 Nov 2021 |
DOIs | |
Publication status | Published - Feb 2022 |
Keywords
- Landscape space
- Machine learning
- Outdoor thermal comfort
- Thermal benchmarks
- Thermal index
- Urban heat island
- Urban park
ASJC Scopus subject areas
- Geography, Planning and Development
- Civil and Structural Engineering
- Renewable Energy, Sustainability and the Environment
- Transportation