娱乐
生物多样性
地理
林地
联合分析
旅游
环境资源管理
环境规划
中国
社会经济学
生态学
偏爱
社会学
生物
环境科学
考古
经济
微观经济学
作者
Xinlei Hu,M. Francisca Lima,Ross Mclean,Ziwen Sun
标识
DOI:10.1016/j.ufug.2022.127595
摘要
There has been a transformation of value orientation from an anthropocentric to eco-centric view in Chinese urban park design. Biodiversity enhancement has been increasingly seen as a prioritised park design aim by landscape designers. This promotes the rise of a novel park style with wild, less manicured appearance in cities, which shows strong contrasts to the traditional park style with ornamental, manicured characteristics. However, in this urban park transformation process, people’s opinion has been almost ignored. This research investigated the importance of biodiversity compared with other relevant urban park attributes (i.e., Facilities, Woodlands, Maintenance, and Seasonal views) identified from preliminary focus groups. The research further predicted preferences between wild and traditional urban parks. Conjoint analysis was used to address these questions. Five urban park attributes (i.e., Biodiversity, Facilities, Woodlands, Maintenance, and Seasonal views) were included in the conjoint questionnaire survey. The survey (N = 187) was conducted with the public and ecology/landscape professionals in Hangzhou, China. Results showed that for professionals, biodiversity was the most important attribute relative to others; for the public, both facilities and biodiversity were the most important attributes. Preferences for the two park styles varied between the two groups: professionals preferred wild parks, whereas the public preferred traditional parks. Yet, public preferences for wild parks were enhanced by improving maintenance levels and providing recreation facilities. The study concluded the appreciation of biodiversity among both the public and professionals. Differences in professional preferences for wild parks compared to the public should be considered when professionals design wild parks in the future.
科研通智能强力驱动
Strongly Powered by AbleSci AI