Abstract
Well-being has long been a critical topic of focus and in-depth study for human being, serving not only as a core indicator of individual quality of life but also as a key measure of societal development and progress. Although the study of well-being has a long history and has made significant advancements, the difficulty in collecting long-term longitudinal data, the complexity of influencing factors, and the inherent variability of well-being itself present challenges in understanding the long-term determinants of well-being. The rapid urbanization in China has been proven to pose severe challenges to the physical and mental health of residents. However, the current focus in the field of psychology on the long-term environmental factors affecting well-being is mostly centered on cultural and social environments, with relatively insufficient research on the impact of the physical environment on long-term well-being.Addressing these deficiencies, this thesis designs four studies investigating the mechanisms through which urban environments impact well-being. We have incorporated a variety of factors from urban environments and examined dimensions of well-being from both macroscopic and microscopic perspectives, employing both longitudinal and cross-sectional research methods.
Study 1 investigated the impact of indoor environments on well-being at the micro-level. The study collected 3,077 university students living in Hong Kong and Mainland China across two waves, included 27 dimensions of indoor environmental factors and 17 dimensions of physical health indicators, to explore the impact pathways of indoor environments and physical health on well-being. First, exploratory factor analysis was conducted and extracted five principal components of the latent variable of indoor environment , including daily chemicals usage, lifestyle, humidity, indoor air processors usage, and environmental perception; and two principal components of physical health latent variable, including sick building syndrome and respiratory and allergy symptoms. Subsequently, mediation analysis based on Bootstrap with physical health as the mediation role showed that the mediation effect of physical health is significant, with mediation effects accounting for 22.16%, 76.64%, and 60.18% of the total effect, respectively, based on data of the first and second waves and combined data.
Utilizing ensemble machine learning algorithms to analyze textual data from the Sina Weibo, Study 2 developed large-scale predictive models of well-being and validated them as psychological measurement scales. This study establishes a methodological foundation for examining the impact of urban environments on well-being at a macro level. We recruited participants through random private messages to active Weibo users, collecting 471,173 Weibo posts from 1,427 users. After extracting language features using six psycho-linguistic dictionaries, comparing different ensemble machine learning algorithms and tuning parameters, we established models for both psychological well-being and subjective well-being. The models have demonstrated excellent reliability and validity. The predictive model for psychological well-being showed calibration validity between 0.41-0.54 and split-half reliability of 0.95-0.97 across six dimensions; the subjective well-being model showed calibration validity between 0.50-0.52 and split-half reliability of 0.94-0.96 across three dimensions. All indicators reached statistically significant levels. In addition, SHAP values were used to explain the important language features, further explored the psychological significance of the well-being prediction models.
Study 3 explored the fluctuation trends of well-being over a long period, providing a theoretical basis for subsequent macro-level explorations. Using the predictive models of well-being established in Study 2, Study 3 calculated the monthly scores of residents' life satisfaction and psychological well-being from January 2010 to May 2022 nationwide. Then we used Sen's Slope and Mann-Kendall methods for trend analysis and trend significance testing, respectively. The results showed that the trend slope for psychological well-being is 0.0187 and for life satisfaction is 0.0079, indicating statistically significant upward trends for both well-being indicators over the twelve-year period.
Study 4 investigated the impact of urban environments on residents' well-being at the macro level, utilizing panel data from 36 provinces or municipalities in China spanning from 2010 to 2021. Urban environmental and resident physical health indicators were downloaded from the National Bureau of Statistics public dataset, and life satisfaction and psychological well-being scores were calculated using the predictive model proposed in Study 2. The results showed that urban sewage treatment, green space area, and urban greening investment can directly and indirectly affect well-being by impacting residents' physical health. Additionally, a threshold effect was found in the direct impact of urban greening investment on residences' well-being. The findings based on macro-level data re-validated our hypothesis that physical health play a key mediation role in impact of urban environments on well-being.
In summary, this thesis combines knowledge from psychology, environmental science, and data science to provide in-depth insights into how urban environments affect residents' well-being, offering valuable theoretical and empirical evidence for urban planners and policymakers, emphasizing the importance of considering the environment, health, and well-being in urban development. Through this multidimensional study, we aim to help promote the creation of healthier, happier urban living environments.
| Date of Award | 23 Jul 2024 |
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| Original language | English |
| Awarding Institution |
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| Supervisor | Linyan LI (Supervisor) & Tingshao ZHU (External Supervisor) |