How can urban green space be planned for a ‘happy city’? Evidence from overhead- to eye-level green exposure metrics

Abstract

The benefits of urban green spaces (UGSs) for human health have been extensively documented. Nevertheless, few studies have incorporated multidimensional UGS exposure indicators, and little is known about the effectiveness of different metrics that should be prioritized as nature-based solutions for improving mental well-being. This study aimed to investigate the associations between various UGS exposure metrics and residents’ expressed happiness (EH) as well as to determine the prioritization of metrics in Nanjing, China, a megacity with 9.5 million inhabitants. The study region was divided into 500 m × 500 m grids, and 330,000 geotagged posts from social media (Sina Weibo) were retrieved for sentiment analysis using the Natural Language Processing (NLP). We developed a systematic UGS exposure framework using satellite, land-cover, and street view-derived data, encompassing 17 indicators of composition and configuration at overhead level as well as street green space visibility and perceived quality at eye level. A regression model and Likelihood Ratio Test were used to examine the associations between various UGS indicators and EH and determine the prioritization of indicators. The results indicated that UGS size had the greatest potential for promoting residents’ EH, followed by overall greenness in the surrounding area, aggregated UGS, perceived quality, and visibility of street green spaces. This study also found that overhead-level metrics may be more effective than eye-level metrics in enhancing residents’ EH, although both perspectives showed significant associations with EH. These findings provide valuable insights into health-oriented landscapes and urban planning to promote the development of a “happy city,” particularly in low-green resource settings in low- and middle-income countries.