How do residents perceive ecosystem service benefits received from urban streets? A case study of Guangzhou, China

• Published in: Journal of cleaner production Vol. 449; p. 141554
• Main Authors: Chen, Chongxian, Lin, Xiaoling, Wu, Yitong, Xie, Jiehang, Liu, Jingyi
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 10.04.2024
• Subjects: Ecosystem services; Guangzhou city; Landsenses ecology; Social perception; Urban streets; Landsenses ecology; Guangzhou city; Ecosystem services; Social perception; Urban streets
• ISSN: 0959-6526; 1879-1786
• DOI: 10.1016/j.jclepro.2024.141554

The degradation of ecosystems poses a significant threat to human well-being, necessitating the urgent protection of ecosystem services (ES). The incorporation of social perceptions into the assessment of ES benefits is paramount in environmental management. However, scant attention has been directed towards investigating the relationship between urban streets and resident's perception of ES benefits. Using the central urban area in Guangzhou as a case study, this research proposes a perceived ES benefits prediction method based on machine learning. The perception of ES benefits in Guangzhou was successfully predicted from street view images and perception data. “Food and material provision”, “aesthetic enjoyment”, “recreation”, and “ecological security” are the most readily perceived ES in urban streets. Spatial analysis further revealed the spatial dependence between the perceived ES benefits and street view characteristics. All perceptions of ES benefits exhibit uneven spatial aggregation patterns in the research area. “Greenness”, “openness” and “scene diversity” are key driving variables spatially related to ES benefits perception. The results of this study contribute valuable insights for practitioners in the field of ecosystem services and offer guidance to urban planners in making informed decisions for the development of sustainable urban ecosystems. •Residents' perceptions of ecosystem service (ES) benefits in Guangzhou were predicted based on machine-learning method.•Integrate spatial autocorrelation and GWR models to clarify the effects of street view features on perceived ES benefits.•“Food and material provision” is the most readily perceived ES benefits derived from urban streets.•The perception of ES benefits in urban streets exhibits an uneven spatial distribution.•“Greenness”, “openness” and “scene diversity” serve as key predictors of residents' perceived ES benefits.