Greenness, Perceived Pollution Hazards and Subjective Wellbeing: Evidence from China

Abstract

Urbanisation from the developing world has been phenomenal and renewed the interest of studying the connection between urban greenness and subjective wellbeing. This paper responds to this greenness-wellbeing connection by shifting its focus towards systematically exploring the influences of urban greenness, perceived pollution hazards, and their interaction terms on subjective wellbeing. Using a combination of green view data and individual survey data in Beijing, we find that perceived pollution hazards about the disposal of waste, polluted water, and air pollution have significant interaction effects with eye-sensored greenness exposures on subjective wellbeing. Findings of this study suggest that policies geared towards mitigating particular domains of pollution hazards and improving green landscape should work together for shaping people's quality of life.

Not all types of nature have an equal effect on urban residents’ well-being: A structural equation model approach

“…Exposure to and activities practiced in, or in relation to, nature may contribute to urban residents' well-being in several ways. It may reduce harm related to urban nuisances -violence, perceived noise, and pollution hazards (see Markevych et al, 2017;Wu et al, 2020;Kondo et al, 2018), and so improve the perceived quality of urban residences. It can contribute to restoring emotional and cognitive capacities affected by an urban lifestyle.…”

“…Moreover, following past research (see Kondo et al, 2018;Wu et al, 2020), one may hypothesize that undomesticated nature, if implying woodlands in particular, is more liable both to buffer or reduce urban nuisances-air or noise pollution, violence, and crime-and to increase the perceived aesthetics of the site. Those two aspects that may contribute to the observed increase in citizens' perceived quality of their urban home environment.…”

Landscape Design and Green Space Perception Enhancement Based on Panoramic Imaging and Segnet

“…Deep learning (DL) refers to learning the intrinsic laws and levels of representation of sample data so that machines can have the same analytical learning ability as humans and can recognise data such as text, images and sound [8][9][10]. At present, deep learning has gained wider application in the field of artificial intelligence technology [11][12][13][14][15]. With the development of deep learning techniques in recent years, a series of open source convolutional neural network (CNN) models for semantic segmentation of images have emerged.…”