城市环境视角下的幸福感影响机制研究
文献类型：学位论文
| 作者 | 韩诺 |
| 答辩日期 | 2024-06 |
| 文献子类 | 博士 |
| 授予单位 | 中国科学院大学 |
| 授予地点 | 中国科学院心理研究所 |
| 其他责任者 | 朱廷劭 ; 李林妍 |
| 关键词 | 城市环境 幸福感 生理健康 面板数据 器学习 |
| 学位名称 | 理学博士 |
| 学位专业 | 应用心理学 |
| 其他题名 | The Influence Mechanisms of Urban Environments on Human Well-Being |
| 中文摘要 | 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 longterm 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 wellbeing 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 comprises four studies investigating the mechanisms through which urban environments impact well-being. We 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 microlevel. The study collected 3,077 participants in universities of 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 indoor environmental latent variables, including daily chemicals usage, lifestyle, humidity, indoor air processors usage, and environmental perception; and two principal components of physical disease latent variables, 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 established a methodological foundation for examining the impact of urban environments on wellbeing 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 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 model 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 12-year period. Study 4 explored the mechanisms of urban environments on residents' well-being at the macro level, based on panel data from 31 provinces or municipalities nationwide 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 could 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 plays 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. |
| 英文摘要 | 幸福感是学界长期以来关注并深入研究的重要领域，它不仅是个体生活质 量的核心指标，也是衡量社会发展和进步的关键因素。虽然幸福感研究已取得 长足进展，但长期纵向数据的收集困难度、幸福感影响因素的交错复杂性以及 幸福感本身波动的不确定性给深入理解幸福感的长期影响因素带来挑战。我国 快速的城市化发展已被证实给居民身心健康带来严峻挑战。但是目前心理学领 域关注的幸福感长期环境影响因素大多集中于人际环境与社会环境，将物理环 境作为幸福感长期影响因素的研究还相对不足。本博士课题共设计了四个研究， 从多种类型的城市环境因素，多种幸福感维度，以及宏观与微观的不同角度， 结合纵向与横向研究方法，探究城市环境视角下的幸福感影响机制。 研究一在微观层面探究了室内环境对幸福感的影响机制。研究分两次共在 香港与大陆地区大学中收集 3077 名被试，纳入 27 维室内环境因素和 17 维生理 健康因素，探究室内环境、生理健康对幸福感的影响机制。研究首先使用探索 性因子分析，共对室内环境潜变量提取了 5 个主成分，包括日化用品使用、生 活方式、潮湿、室内空气处理器使用和环境感知，对生理健康潜变量提取了病 态建筑综合征与呼吸道及过敏类症状两个主成分；之后以生理健康作为中介变 量基于 Bootstrap 建立中介分析，基于第一轮收集数据的探索性验证，第二轮数 据的重复验证与合并数据验证结果均表明生理健康的中介作用显著，中介效应 占总效应的比例分别是 22.16%、76.64%与 60.18%。 研究二基于微博文本数据，使用机器学习算法建立了大规模幸福感自动感 知模型，从而为后续的宏观角度城市环境影响机制探究提供方法基础。我们基 于线上平台随机向活跃微博用户发送私信进行被试招募，共招募 1,427 名微博用 户，下载微博总数 471,173 条。在使用心理语义词典进行文本特征提取，并进行 不同集成式算法比较与网格搜索调参后，研究二建立了心理幸福感与主观幸福 感两种幸福感预测模型。其中，心理幸福感预测模型的六个维度的校标效度在 0.41-0.54 之间，分半信度达到 0.95-0.97；主观幸福感的三个维度校标效度在 0.50-0.52 之间，分半信度达到 0.94-0.96。所有指标均达到统计学意义上显著水 平。此外，研究二基于 SHAP 值对预测模型建立过程中起到重要贡献的文本特 征进行解释，进一步探索幸福感预测模型的心理学意义。 研究三探究了长时间跨度下幸福感的波动趋势，为后续的宏观角度城市环 境影响机制探究提供理论基础。研究三基于研究二所建立的幸福感预测模型， 计算得到 2010 年 1 月至 2022 年 5 月全国范围内以月为粒度的群体生活满意度与 心理幸福感分数，并使用 Sen's Slope 方法与 Mann-Kendall（MK）方法分别进行趋势分析与趋势检验。结果显示，心理幸福感的趋势斜率为 0.0187，生活满意 度的趋势斜率为0.0079，MK检验均达到统计学意义上显著水平，即心理幸福感 和生活满意度两种幸福感维度在 12 年跨度下显示出了显著上升趋势。 研究四在宏观层面探究了城市环境对于居民幸福感的影响机制，此研究基 于 2010 年至 2021 年共 12 年，覆盖全国 31 个省或直辖市的面板数据进行分析。 其中城市环境与居民生理健康指标下载于国家统计局公开数据，生活满意度与 心理幸福感使用研究二提出的预测模型计算得到。结果显示城市污水处理率、 人均公园绿化面积与城市绿化投资均可以直接或通过作用于居民生理健康间接 对幸福感产生影响，生理健康在城市环境对居民幸福感的影响中介作用显著。 此外我们还发现了城市绿化投资对幸福感的直接影响存在门槛效应。 总结来说，本博士课题结合心理学、环境科学与数据科学多领域知识，为 理解城市环境如何影响居民幸福感提供了深入见解，也为城市规划者和政策制 定者提供了宝贵的理论实证。研究结论强调了在城市发展中综合考虑环境、健 康和福祉的重要性。通过这种多维度研究，本课题希望帮助推动创建更健康、 更幸福的城市生活环境。 |
| 语种 | 中文 |
| 源URL | [http://ir.psych.ac.cn/handle/311026/47980]  |
| 专题 | 心理研究所_社会与工程心理学研究室 |
| 推荐引用方式 GB/T 7714 | 韩诺. 城市环境视角下的幸福感影响机制研究[D]. 中国科学院心理研究所. 中国科学院大学. 2024. |
入库方式： OAI收割
来源：心理研究所
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