Impact of Landscape Elements on Public Satisfaction in Beijing’s Urban Green Spaces Using Social Media and Expectation Confirmation Theory

Ruiying Yang, Wenxin Kang, Yiwei Lu, Jiaqi Liu, Boya Wang and Zhicheng Liu ()
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Ruiying Yang: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Wenxin Kang: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Yiwei Lu: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Jiaqi Liu: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Boya Wang: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China
Zhicheng Liu: School of Landscape Architecture, Beijing Forestry University, Beijing 100083, China

Sustainability, 2025, vol. 17, issue 22, 1-25

Abstract: A core challenge in urban green space (UGS) management lies in precisely identifying public demand heterogeneity toward landscape elements. Grounded in Expectation Confirmation Theory (ECT), this study aims to systematically identify the key landscape elements shaping public satisfaction and elucidate their driving mechanisms to inform UGS planning. Using 107 UGS in central Beijing as case studies, this study first retrieved 712,969 social media data (SMD) from multiple online platforms. A landscape element lexicon derived from these data was then integrated with the Bidirectional Encoder Representations from Transformers (BERT) model to assess public attention and satisfaction toward the natural, cultural, and artificial attributes of UGS, achieving an accuracy of 84.4%. Finally, spatial variations and the effects of different landscape elements on public satisfaction were analyzed using GIS-based visualization, K-means clustering, and multiple linear regression. Key findings reveal the following: (1) satisfaction follows a “core-periphery” gradient, peaking in heritage-rich City Wall Parks (>0.63) and plunging in green belts due to imbalanced element configurations (~0.04); (2) naturally dominant green spaces contribute most to satisfaction, while a nonlinear relationship exists between element dominance and satisfaction: strong features enhance perception, balanced patterns mask issues; (3) regression analysis confirms natural elements (vegetation β = 0.280, water β = 0.173) as core satisfaction drivers, whereas artificial facilities (e.g., service infrastructure β = 0.112, p > 0.05) exhibit a high frequency but low satisfaction paradox. These insights culminate in a practical implementation framework for policymakers: first, establish a data-driven monitoring system to flag high-frequency, low-satisfaction facilities; second, prioritize budgeting for enhancing natural elements and contextualizing cultural elements; and finally, implement site-specific optimization based on primary UGS functions to counteract green space homogenization in high-density cities.

Keywords: landscape perception; satisfaction drivers; deep learning; moderating effect; urban regeneration (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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